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Action perception and motor imagery: Mental practice of action
Accepted Manuscript Title: Action Perception and Motor Imagery: Mental Practice of Action Authors: Helen E. Savaki, Vassilis Raos PII: DOI: Reference:
S0301-0082(18)30190-4 https://doi.org/10.1016/j.pneurobio.2019.01.007 PRONEU 1603
To appear in:
Progress in Neurobiology
Received date: Revised date: Accepted date:
1 November 2018 21 January 2019 28 January 2019
Please cite this article as: Savaki HE, Raos V, Action Perception and Motor Imagery: Mental Practice of Action, Progress in Neurobiology (2019), https://doi.org/10.1016/j.pneurobio.2019.01.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Title: Action Perception and Motor Imagery: Mental Practice of Action
Authors: Helen E. Savaki and Vassilis Raos Department of Basic Sciences, School of Medicine, University of Crete P.O. Box 2208, 71003, Iraklion, Crete, Greece.
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Computational Neuroscience Group, Institute of Applied and Computational Mathematics,
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Foundation for Research and Technology-Hellas, Plastira N 100 str, 71003, Iraklion, Crete,
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Greece.
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Corresponding author: Helen Savaki
71003, Iraklion, Crete, Greece.
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Department of Basic Sciences, School of Medicine, University of Crete P.O. Box 2208,
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tel.: +302810394513, fax: +302810394530, e-mail: [email protected]
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Total number of words in the manuscript, excluding references and figure legends:
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16212
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Hihglights
Mental simulation of actions underlies motor cognition. Conceptual representations are grounded in sensory-motor codes. Perception and mentalization are instantiated by sensory-motor neural activity.
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Abstract
Motor cognition is related to the planning and generation of actions as well as to the recognition and imagination of motor acts. Recently, there is evidence that the motor system participates not only in overt actions but also in mental processes supporting covert actions. Within this framework, we have investigated the cortical areas engaged in execution, observation, and
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imagination of the same action, by the use of the high resolution quantitative 14C-deoxyglucose method in monkeys and by fMRI in humans, throughout the entire primate brain. Our data
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demonstrated that observing or imagining an action excites virtually the same sensory-motor cortical network which supports execution of that same action. In general agreement with the results of five relevant meta-analyses that we discuss extensively, our results imply mental practice, i.e. internal rehearsal of the action including movements and their sensory effects. We
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suggest that we actively perceive and imagine actions by selecting and running off-line restored
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sensory-motor memories, by mentally simulating the actions. We provide empirical evidence
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that mental simulation of actions underlies motor cognition, and conceptual representations are grounded in sensory-motor codes. Motor cognition may, therefore, be embodied and modal.
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Finally, we consider questions regarding agency attribution and the possible causal or
Abbreviations
anterior intraparietal blood-oxygen-level dependent caudomedial area of the auditory belt cingulate motor area extrastriate body area fusiform body area fundus of superior temporal area inferior frontal gyrus inferior parietal lobule local field potentials lingual gyrus middle frontal gyrus primary motor cortex middle part of the superior temporal cortex forelimb region in primary motor cortex middle occipital gyrus medial superior temporal area middle temporal area
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AIP BOLD CM CMA EBA FBA FST IFG IPL LFPs LG MFG MI mid-STS MI-forelimb MOG MST MT
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epiphenomenal role the involved sensory-motor network could play in motor cognition.
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posterior part of the middle temporal gyrus ventral premotor cortex pre-supplementary motor area posterior part of superior temporal gyrus parietal ventral area retroinsula primary somatosensory cortex forelimb region in primary somatosensory cortex secondary somatosensory area supplementary motor area Superior parietal lobule superior temporal polysensory area superior temporal sulcus temporo-parietal junction temporo-parieto-occipital association area ventral intraparietal area ventral somatosensory area
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pMTG PMv preSMA pSTG PV Ri SI SI-forelimb SII SMA SPL STP STS TPj TPO VIP VS
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Keywords: action observation, agency attribution, embodied cognition, mental
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simulation, motor imagery.
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Motor cognition can be studied in the framework of action representation, which is the basis of covert actions, such as action perception and motor imagery. We use the term action representation to refer to an internal cognitive construct that symbolizes an action in the real world. Action perception is often unconscious, automatic, and stimulus-driven. On the other
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hand motor imagery, that is the process by which an individual experiences acting within a mental world, resembles perceptual experience but occurs in the absence of current sensory
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stimulation. It also occurs in the absence of overt motor action, but is conscious and under voluntary control.
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One of the most influential contemporary theories of motor cognition refers to embodied
cognition theory (Rosch et al. 1991), with motor cognitive abilities depending on and achieved by the mechanism of mental simulation (Gordon 1986; Heal 1986), i.e. by mental re-enactment of action execution. The consensus among the embodied cognition theorists is that cognitive processes are deeply grounded in our bodily interactions with the environment (Barsalou 2008a; Bergen and Wheeler 2010; Borghi and Cimatti 2010), and that simulation plays a significant role in embodied cognition (Garbarini and Adenzato 2004; Hostetter and Alibali 3
2008; Bergen and Wheeler 2010). Low-level simulation processes are supposed to be unconscious, automatic and stimulus-driven, whereas high-level simulation processes are typically conscious, under voluntary control, stimulus-independent (Barlassina and Gordon 2017) and guided by imagination (Goldman 2012b). Accordingly, action perception reflects a low-level simulation process, whereas motor imagery reflects a high-level simulation process (Barlassina and Gordon 2017). Interestingly, the embodied cognition theory assumes that both phenomena, i.e. the non-conscious automatic action observation and the conscious effortful
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motor imagery share common representations (Barsalou 2008a; Barlassina and Gordon 2017).
Taking into account the fragmented results of relevant meta-analyses and our
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experimental findings, namely that overt and covert actions share common neural circuits, we
provide empirical support to the embodied cognition theory. We argue that covert actions are embodied because they re-activate the neural circuitry supporting their overt counterparts. We maintain that an overt action is mentally simulated during the practice of its covert equivalent.
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However, the question remains whether simulation is necessary and sufficient for meaning
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construction or simply an epiphenomenon. And if not, what function does it serve, and what
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other processes are involved in making meaning (Bergen 2015)? Hopefully, the use of neurophysiology to support or contradict neuro-psychological and neuro-philosophical notions
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of cognition, such as the grounding of concepts in sensory-motor brain systems, will contribute
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to the advancement of theories of knowledge.
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1. Studying overt and covert actions
1.1 Non-human primates: execution and observation of the same action In the field of investigation of the cognitive sciences, it is known that not only humans but also
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non-human primates attribute mental states to other subjects and have an intentional understanding of their social world (Premack and Woodruff 1978; Flombaum and Santos
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2005). In fact, non-human primates can represent the mental states of conspecifics, and often act according to a manipulative strategy (Byrne and Whiten 1988). In a series of studies during the last fifteen years, we demonstrated that the same occipito-temporo-parieto-frontal cerebral cortical circuits are involved in monkeys executing a reaching-to-grasp action and in monkeys observing another subject who generates the same action (Raos et al. 2004, 2007; Evangeliou et al. 2009; Kilintari et al. 2011, 2014; Raos and Savaki 2016, 2017).
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More specifically, we studied the effects of execution and observation of the same reaching-to-grasp action in the entire cerebral cortex of non-human primates, using a powerful quantitative high resolution imaging technique, the
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C-deoxyglucose (14C-DG) method
(Sokoloff et al. 1977; Savaki et al. 1980). The advantages of this method over fMRI imaging are: (i) the 14C-DG method assesses directly brain activity whereas fMRI estimates changes in brain activity indirectly via alterations of blood oxygenation level, (ii) the
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C-DG is a
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quantitative method measuring local cerebral glucose consumption in mol/100g/min whereas fMRI is semi-quantitative estimating relative differences, (iii) the resolution is much higher with the
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C-DG, using brain sections of 20 microns, and (iv) the
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C-DG allows for
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cytoarchitectonic identification of the activated areas in histological sections adjacent to the
autoradiographic ones. To exclude the potential effects of unspecific arousal, attention, gaze fixation, eye movements due to scanning the object, visual stimulation by the object and by the biological motion of the reaching forelimb, in addition to the fixation-control monkeys, we
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used motion-control monkeys. These monkeys observed an aimless arm movement, similar to
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the reaching component of the experimental paradigm reach-to-grasp. Eye-movements were
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recorded with infrared oculometer, and muscle activity was recorded in the biceps and wrist extensor (Raos et al. 2004, 2007). We reconstructed two-dimensional quantitative functional
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maps, which we normalized geometrically based on the determined cytoarchitectonic borders of cortical areas. This allowed for the generation of averaged maps per condition and for
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quantitative comparison of activations in homologous brain areas. At the level of the central sulcus we found that specifically the forelimb region of the primary motor (MI) and primary somatosensory (SI) cortical areas were significantly activated,
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not only for grasping execution, but also for grasping observation (Raos et al. 2004). Our quantitative
high-resolution
neuroimaging
study,
combined
with
cytoarchitectonic
identification of the activated cortical areas, resolved a conflict in the literature related to the
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involvement of MI in action observation (Grafton et al. 1996; Rizzolatti et al. 1996; Decety et al. 1997; Hari et al. 1998; Iacoboni et al. 1999). This conflict was largely due to the lower
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sensitivity of the techniques employed earlier. Indeed, our high-resolution neuroimaging finding that MI is somatotopically activated during action observation was later confirmed by neurophysiological recordings from single neurons in the MI of monkeys (Tkach et al. 2007; Dushanova and Donoghue 2010; Vigneswaran et al. 2013). Therefore, the specific activation of the forelimb region in MI cortex (MI-forelimb) during observation of a hand-action in our study provided strong evidence for the involvement of MI in action perception, similar to that
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observed during generation of the same action. Our finding that MI is engaged in action perception is compatible with previous reports demonstrating that MI plays a crucial role in the processing of cognitive information related to motor function, such as mental rotation (Georgopoulos et al. 1989) and context recall (Carpenter et al. 1999). Differences in degree and lateralization of MI-forelimb activations between execution and observation are discussed in a following section: attribution of action to the correct agent. Moreover, activation of the forelimb region in the SI cortex (SI-forelimb), during observation of the same hand-action
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performed by another subject, was similar to the re-afferent feedback accompanying its overt
counterpart. Therefore, we thought that there could be a correspondence between action’s
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percept and its stored mental construct, which was acquired during its execution. We thought that movements along with their somatosensory effects may be stored as motor and
somatosensory representations in the primary motor and primary somatosensory cortical areas, respectively, and both of them may be recalled during observation of the action. We suggested
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that, given the absence of any recorded arm muscle activity, the somatotopic activation of MI
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and SI during action observation may involve mental re-enactment of the action by the observer
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(Raos et al. 2004). In the absence of any actual movement and consequently in the absence of any actual re-afferent input, SI-forelimb activation could involve mental rehearsal of previous
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knowledge about the somatosensory effect of the action. In a following study (Raos et al. 2007) we demonstrated conclusively and for the first
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time the extensive overlap of the lateral and medial frontal and cingulate somatosensory-motor cortical circuits supporting action execution and action observation (Fig. 1, common activations colored orange). Virtually the same cortical circuitry was recruited for both
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generation and perception of the same action, including, in addition to MI- and SI-forelimb regions, the premotor areas F2-forelimb and F5, the supplementary somatosensory cortical area SSA, anterior cingulate areas 24d, 24c, 24ab, the medial cortical areas 8m and 9m, and specific
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regions within areas F3/SMA (supplementary motor area) and F6/preSMA as well as regions within cingulate motor areas CMA-dorsal and CMA-ventral (Matelli et al. 1991; He et al. 1995;
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Geyer et al. 2000; Morecraft et al. 2004). Remarkably, the forelimb regions of the primary somatosensory and motor cortical areas were more activated in execution than in observation, and premotor area F7 was activated only for action observation. The latter findings are discussed in detail in following sections. Of course, the overlapping activations for action execution and action observation do not necessarily indicate involvement of the same cell populations in the two conditions. However, the extensive overlap of specific somatosensorymotor areas activated somatotopically for both action execution and action observation further 6
supported our hypothesis that internal re-enactment of action in the observer’s brain/mind may underlie the perception of others’ acts. We suggested that the somatotopic motor and premotor activations elicited by action observation reflect rehearsal of the physical act, whereas the somatotopic primary and supplementary somatosensory activations reflect the recall of learned sensory–motor associations concerning the anticipated somatosensory feedback. In other words when we observe another subject reaching-to-grasp an object, we recruit not only our action-memory related to the intended reaching of the arm and shaping of the hand for efficient
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grasping of the object, but also our perception-memory related to the anticipated somatosensory
(proprioceptive/tactile) consequences of the intended reaching/grasping movement. As if we
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perceive the act while we execute it, and we mentally rehearse the act while we observe it.
Later on we demonstrated, also for the first time, that the overlap of the two cortical networks underlying execution and observation of the same action was also very extensive at the level of the parietal/intraparietal cortex (Evangeliou et al. 2009). It included the superior
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parietal areas PE-lateral and PE-caudal which receive somatosensory afferents (Jones et al.
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1978) and process information about movement kinematics (Kalaska et al. 1990; Ashe and
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Georgopoulos 1994), the inferior parietal area PF which receives somatosensory input and projects to premotor arm-related areas (Petrides and Pandya 1984), the intraparietal areas PEip,
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anterior intraparietal (AIP), and ventral intraparietal (5VIP and 7VIP) (Colby et al. 1993; Matelli et al. 1998), the medial parietal areas PGm/7m (Cavada and Goldman-Rakic 1989) and
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retrosplenial 29/30 (Morris et al. 1999), as well as the parieto-occipital area V6 which contains direction-selective (Galletti et al. 1996) and real-motion (Galletti and Fattori 2003) cells. Consequently, far from being restricted to the medial and lateral somatosensory-motor frontal
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cortical areas, the action observation/execution corresponding system also involves extensive regions of the superior and inferior parietal, lateral, medial and intraparietal cortex associated with sensorimotor and visuomotor processes, operating at the interface of perception, action
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and cognition (Fig. 1). These findings further supported our hypothesis about mental reenactment of the observed action by the onlooker.
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At the level of the lateral sulcus, we examined all the high order somatosensory
association areas (Raos and Savaki 2016). We found that both execution and observation of the same action activated the secondary somatosensory area (SII) and its adjacent somatorecipient parietal ventral area (PV), both characterized by somatotopic organization (Krubitzer et al. 1995). Execution as well as observation activated also the ventral somatosensory area (VS) and the retroinsula (Ri) both of which display a rather systematic representation of the body (Robinson and Burton 1980; Cusick et al. 1989), and the 7
caudomedial area (CM) of the auditory belt which is characterized by convergence of auditory and somatosensory inputs (Fu et al. 2003). These findings complement a previous suggestion that prediction/anticipation during observation may turn motor commands into expected sensory consequences (Kilner et al. 2004; Kilner et al. 2007). Activation of the somatorecipient insular areas not only for action execution but also for action observation, demonstrated for the first time in our study (Fig. 1), further supported our hypothesis that the perception of actions performed by an external agent presupposes knowledge about the action-effect relationships,
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and that understanding others' actions consists of running off-line previously stored sensory-
motor programs. In fact, the recognition process involves correspondence between the
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observed action and past similar experience, implicating perception, memory and decisionmaking. Interestingly, it was reported that neurons in higher order somatosensory areas
combine past and present sensory information for decision-making (Romo et al. 2002; de Lafuente and Romo 2005; Romo and de Lafuente 2013). Remarkably, the gradual increase in
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activity from the primary to the association somatosensory areas that we found during action
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observation, matches the reported gradual build-up of subjective sensory experience across the
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cortical regions of somatosensory hierarchy (de Lafuente and Romo 2006). Therefore we suggested that the activation of lower and higher order somatosensory areas for action
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observation indicates that the somesthetic consequences of movements, which are generated in a bottom-up manner during action execution, are also triggered in a top-down manner during
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action observation to represent the predicted sensory consequences of the perceived movement. Having demonstrated that the motor cognitive process associated with action-observation involves a strong somatosensory component, characterized by the somatotopic activation of
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the primary somatosensory SI cortex (Raos et al. 2004), the supplementary somatosensory cortex SSA (Raos et al. 2007), the secondary somatosensory cortex SII and several somatorecipient parietal (Evangeliou et al. 2009) and insular (Raos and Savaki 2016) cortical
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areas, we examined whether this process also involves a visual component. To this end we studied the effects of action execution and observation throughout the striate and extrastriate
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cortex of the occipital operculum, lunate, and inferior occipital sulci (Kilintari et al. 2011). We found that the extrastriate cortical visual areas V3A and V3d-occipitotemporal (component of the ventral visual stream) which are associated with central vision, as well as area V3doccipitoparietal (part of the dorsal visual stream) associated with peripheral vision (Baizer et al. 1991) were activated for both motor conditions (Fig. 1). Interestingly, area V3 is known to process visual information related to the requirements of the motor system to control the action. Its activation in our study could not be attributed to differences in oculomotor behaviour 8
(Galletti and Battaglini 1989) because the monkeys were fixating throughout the task. Given that area V3 is known to represent the 3D-object to be reached and manipulated as well as its spatial location (Galletti and Battaglini 1989; Tsao et al. 2003 Konen and Kastner 2008; Arcaro and Kastner 2015), we suggested that its activation indicates that both action generation and action observation utilize information about visuospatial-related and object-related features of the object to be reached and grasped. Of note is that this area in monkeys was suggested to correspond to the human extrastriate body area (EBA) which is associated with functions
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similar to those ascribed to monkey V3, such as limb movements to visual targets, mental simulation of goal-directed movements (Astavief et al. 2004) and processing of visual features
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of action representations (de Lange et al. 2005; Milton et al. 2007; Bakker et al. 2008). In fact,
visuospatial information required for reaching and 3D object-related information useful for grasping may be relayed from the activated area V3, via its parietal projections (Andersen et al. 1990; Felleman and Van Essen 1991; Passarelli et al. 2011) which were found to be activated
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for both motor conditions in one of our earlier studies (Evangeliou et al. 2009), to the
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premotor/motor cortical areas (Petrides and Pandya 1984; Matelli et al. 1998; Petrides and
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Pandya 1999; Gamberini et al. 2009) which were also activated for both execution and observation in one of our previous studies (Raos et al. 2007). Accordingly, we proposed that a
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single visuo-motor neural system represents both physical and mental practice. It is recruited in a bottom-up manner when sensory driven, and in a top-down manner when mentally driven,
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presumably generated by a forward model (Wolpert and Ghahramani 2000). This sensorymotor system extends beyond the parietofrontal somatosensory-motor which we described earlier, to include visual in addition to the somatosensory information concerning the action. It
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reflects the physical as well as the mental visuomotor representation of the act, providing further support to a process-driven (simulation like) rather than a theory-driven mechanism underlying action perception.
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At the level of the temporal cortex, all the main components of the motion complex (MT-
dorsal and ventral, MST and FST) were activated for action observation, but not for action
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execution when compared to the motion-control (Kilintari et al. 2014). In contrast, both execution and observation of the same action were found to engage the superior temporal polysensory area (STP), including the temporo-parieto-occipital association area TPO-rostral and the PGa, as well as area TS2 (Fig. 1). Further support to our suggestion, that activation of certain temporal cortical areas for action observation reflects covert rehearsal of the observed action by the onlooker, is provided by previous reports (Goebel et al. 1998; Saygin et al. 2010). Interestingly the STP, which is implicated in visual and somatosensory processing and its role 9
in expectation has been emphasized (Mistlin and Perrett 1990), was much more activated for observation than for execution in our study (Kilintari et al. 2014). This finding agrees well with neurophysiological data demonstrating that neurons in area STSa/STP respond to the perception of movements made by body parts of other subjects rather than by the monkey’s own hand (Perrett et al. 1985b; Perrett et al. 1989; Hietanen and Perrett 1993; Jellema et al. 2000; Keysers and Perrett 2004). Also, this finding indicates greater involvement of a high order polysensory area in action observation than in execution, in contrast to the smaller
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involvement of lower order cortical areas such as the primary sensory-motor cortex discussed above. The latter differences indicate stronger involvement of association compared to primary
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cortical regions in observation compared to execution.
Finally, we analyzed the lateral prefrontal cortex consisting of a dorsolateral component [areas 8, 9, 10, 46d, and 9/46-dorsal (9/46d) (Petrides and Pandya 1999)] and a ventrolateral constituent [areas 9/46-ventral (9/46v), 45, and dorsal 47/12 (Petrides and Pandya 2002)] as
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well as the orbitofrontal cortex including areas 10, 11, 47/12 and 13 (Barbas 2007; Petrides
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2007). We found that execution and observation activated in common most of the lateral
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prefrontal and orbitofrontal cortical areas examined: 8-convexity and 8-bank, 9-lateral, 46dorsal, 46-ventral, 46-principal both upper and lower banks, 47/12-dorsal, 47/12-ventral, 45-
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convexity, 45-bank and area 10 (Raos and Savaki 2017). Interestingly, most of these areas have been involved in on-line monitoring and manipulating visual information related to actions
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(Petrides 1994). Moreover, activation of the supramodal frontopolar area 10 for both execution and observation is compatible with its involvement in coordinating attention between externally presented and internally represented information (Burgess et al. 2009 ; Medalla and
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Barbas 2014). All the above results indicate that the prefrontal cortex integrates information in the service of both action generation and action perception. Markedly, area 9/46v was activated exclusively for execution whereas 9/46d was activated only for observation (Fig. 1, execution
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and observation effect in red and green respectively). The latter finding could provide an explanation to the reason why activation of the motor system during observation of an action
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does not result in overt movements. This explanation has as follows: It is known that the dorsal part of the dorsolateral prefrontal cortex projects to the dorsal premotor cortex (Matelli et al. 1986; Barbas and Pandya 1987; Pandya and Yeterian 1996) and that its F7-projection zone occupies the broadest territory (Wang et al. 2002). As mentioned above, the dorsal premotor area F7 was also exclusively activated for action observation but not for action execution (Raos et al. 2007). Interestingly, the rostral part of the premotor cortex, including area F7, is associated with the selection of particular movements between alternatives based on pre10
learned conditional rules (Petrides 1987; 1997). It is also known that area F7 has an inhibitory effect (Moll and Kuypers 1977; Sawaguchi et al. 1996) and reaches the spinal cord both directly and indirectly (Keizer and Kuypers 1989). In our study on the effects of action execution and observation in the spinal cord, we demonstrated that observation decreases activity bilaterally in the spinal section representing the forelimb (the cervical enlargement) whereas execution activates the same area ipsilaterally to the moving arm (Stamos et al. 2010). We then suggested that the depression of activity in the spinal segment of forelimb representation for action
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observation could explain the suppression of its particular overt equivalent, despite the activation of the observer’s primary motor cortex. In other words, our results taken together
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indicate that, area 9/46d which is activated exclusively for action observation (Raos and Savaki
2017) may excite area F7 which is also exclusively activated for action observation (Raos et al. 2007) and the latter may inhibit the spinal cord which is exclusively depressed for action observation (Stamos et al. 2010), explaining why execution of the specific observed movement
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is prevented although the motor system in the observer’s brain is activated (Savaki 2010).
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In summary, with our experiments in non-human primates we established that the brain
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system engaged in action execution by an acting monkey is re-employed by a monkey merely observing the same action. This system includes frontal premotor and motor, parieto-temporal
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somatosensory, and temporo-occipital visual cortical regions as well as prefrontal and occipitoparieto-temporal association areas (Fig. 1). Apparently, an action such as reaching-to-grasp an
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object with one hand does not simply involve perception of the object and generation of the correct movement. It also involves memory related to the object’s features and affordances, to the planned arm movement for successful reaching, to the intended shaping of the hand for
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efficient grasping, to the visual guidance of reaching/grasping, and to the anticipated somatosensory consequences of the intended action. All our results taken together indicate that both generation and perception of an action activate similar motor, somatosensory, visual and
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association networks, which presumably recruit shared contextual representations. Perception of an action performed by another subject may trigger the effects of our previous knowledge
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about the act and its predicted consequences. Therefore, action perception may correspond to simulation of its overt counterpart. We may decode the actions of others by activating our own action system. We may understand observed actions by performing them mentally. We may learn how to act and how to recognize others’ actions by acquiring movement-effect associations. And this may be the neuronal basis of observational learning. Activation of the neural circuitry supporting action execution, during observation of the same action performed by another subject, could facilitate its activation during following performances. In other 11
words, simulation of the action during its observation could facilitate its subsequent execution, suggesting that motor skills can be learned by observation. Indeed, subjects merely watching an experimenter performing a task can learn to execute it as efficiently as after practicing the task themselves (Heyes and Foster 2002). Based on our results, we developed a model exhibiting learning from observation, with generalization capacities and interesting predictions regarding the mechanisms employed for observation in the cortex (Hourdakis et al. 2011).
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1.2 Human studies on overt and covert actions
In this section we first report the results of five meta-analyses of human neuroimaging data
concerning the cortical areas engaged in action generation, action perception and motor
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imagery, investigated mostly in isolation from each other, and we discuss their strengths and limitations. Then we present the results of two human fMRI studies, which are the only ones to our knoweledge investigating the whole brain in all three conditions of execution,
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observation and imagination of the very same hand action in the very same subjects, therefore
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overcoming most of the limitations of the meta-analyses.
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1.2.1 Meta-analyses
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In a descriptive meta-analysis of neuroimaging data concerning the cortical areas engaged in action generation, action perception and 1st person motor imagery, Grezes and
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Decety state that there is evidence of “functional equivalence” among these three motor conditions, indicating shared motor representations (Grezes and Decety 2001). The focus of their meta-analysis was to evaluate the extent of overlap of the activations elicited by overt and covert actions in several different studies. In Table I, the authors list 22 studies used for their
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analysis. These include 8 execution, 6 imagination and 8 observation studies, using a variety of tasks of different goal-directed hand movements. Specifically, only 6 of them used the same
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task to study either execution and observation (3 studies) or observation and imagination (3). Of course pooling of data from different studies which use a variety of experimental designs,
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motor conditions, tasks, subjects and instructions is problematic, because different types of tasks and goals may involve different cortical areas. Nevertheless, this meta-analysis demonstrated that there is a good overlap between action execution, observation and imagination in the supplementary motor area, the dorsal premotor cortex, the supramarginal gyrus, the superior parietal lobe and the superior occipital gyrus, i.e. in areas extending well beyond the two ones containing mirror neurons. Interestingly, this meta-analysis revealed that motor imagery recruits the primary motor cortex, while both imagery and observation recruit 12
the pre-SMA and the dorsolateral frontal gyrus. Overall, the authors conclude that the overlapping activations for action execution, observation and imagination, across studies, support a functional equivalence between overt and covert actions. However, this equivalence is not strict if we take into consideration some scattered non-overlapping foci associated with the three motor conditions across the analyzed studies (Grezes and Decety 2001). In a meta-analysis of 139 fMRI/PET experiments, using the method of activation likelihood estimation, Caspers et al identified brain areas consistently activated for action
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observation and imitation (Caspers et al. 2010). They revealed a largely bilateral network, including areas in the prefrontal, premotor, parietal, temporal and occipital cortex, activated
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for both action observation and imitation. Additional sub-analyses for different effectors (body part involved in the movement) revealed a comparable brain network to that of the general analysis across all experiments. More specifically, brain regions consistently activated across the 104 experiments using tasks of action observation (including different effectors and
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instructions) were symmetrically distributed in both hemispheres within frontal areas BA
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44/45, lateral dorsal premotor cortex (BA 6), supplementary motor area (SMA), rostral inferior
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parietal lobule (area PFt), primary somatosensory cortex (BA 1/2), superior parietal (area 7A), intraparietal cortex, posterior middle temporal gyrus at the transition to visual area V5, and
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fusiform face area/fusiform body area (Caspers et al. 2010). Brain regions activated across the 35 action imitation experiments included bilateral BA 44, BA 6, BA2, SMA, area PFt and
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visual area V5. Interestingly, the authors emphasize their finding that both action observation and imitation were associated with robust activation of the primary somatosensory cortex, while the primary motor cortex was recruited for action observation only when participants
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viewed actions with the intention to imitate them. They also remark that pooling of data from very different studies could have deleted effects related to local somatotopy. They conclude that the revealed common network for observation and imitation of actions reaches far beyond
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the ‘classical’ mirror neuron areas in ventral premotor and inferior parietal cortex (Caspers et al. 2010).
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In another meta-analysis, using the method of activation likelihood estimation,
Molenberghs et al provided a quantitative index of the consistency of patterns of activity measured in 125 human fMRI studies of action observation or action execution, in which the authors attributed their findings to mirror neuron functioning (Molenberghs et al. 2012). They revealed an extensive network of brain regions, with significant clusters of activation encroaching upon 34 separate Brodmann areas. More specifically, the significant clusters attributed to mirror neuron functioning, which were revealed by this meta-analysis, encompass 13
not only the classical mirror neuron areas based on monkey recordings, i.e., the inferior parietal lobule and inferior frontal gyrus, but also the middle frontal, dorsal premotor cortex, primary somatosensory cortex (BA 3), cingulate gyrus, superior parietal cortex, precuneus, insula, superior, middle and inferior temporal gyri, primary visual cortex, cerebellum and parts of the limbic system. The authors of the meta-analysis state that, despite the claims of some authors of the human fMRI studies, it is unlikely that regions such as the early visual cortex and superior temporal cortex possess mirror properties since we know that they do not exhibit
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motor-related activity. Interestingly, they also state that very few of these fMRI studies included both an “observe” and a corresponding “execute” condition, although only neurons
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responding under both conditions are considered to be mirror neurons. Sub-analyses in which (i) participants listened to action sounds, revealed additional activation clusters in and around the primary auditory cortex, (ii) participants watched an actor receiving somatosensory stimulation, revealed additional activation of the primary somatosensory cortex, and (iii)
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participants executed and/or observed expressions of emotion, revealed consistent activation
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of regions known to be involved in emotion processing, including the amygdala, insula and
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cingulate gyrus. The authors conclude that overlapping brain regions are activated in human participants when observing or executing certain actions, through simulation, and that the
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precise regions involved depend on the modality of the task (Molenberghs et al. 2012). The first quantitative cortical map of motor imagery was provided by Hetu et al, who
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combined the data from 75 fMRI and PET studies in an activation likelihood estimation metaanalysis (Hetu et al. 2013). The authors found that motor imagery consistently recruited a large cortical network as well as subcortical and cerebellar regions. With additional sub-analyses
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they found that the brain regions activated during motor imagery depend on the type of movement to be imagined (simple or complex), the imagery modality (kinesthetic or visual), the effector (body part involved in the movement) and the instructions given to the participants.
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Sub-analysis of 22 experiments including the total of 280 subjects performing tasks with transitive upper-limb movements revealed consistent activations, mostly bilateral, in the
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inferior frontal gyrus, precentral gyrus, postcentral gyrus (primary somatosensory cortex), middle frontal gyrus, SMA, middle cingulate cortex, anterior insula, superior and inferior parietal cortex, supramarginal gyrus and precuneus. Consistent activations were also found in the cerebellum, putamen and pallidum. In contrast to the meta-analysis of Grezes and Decety ( 2001), they did not find consistent involvement of the primary motor cortex. They argue that individual differences could explain why primary motor cortex activity was not consistently found. The authors also note that because they did not include studies that focused on action 14
execution per se in their meta-analyses, they could not directly compare the networks supporting motor imagery and motor execution. However, they conclude that several cortical areas known to play a role in actual motor behaviour were consistently engaged in motor imagery. They suggest that motor imagery recruits the system underlying actual movements, supporting the view that motor imagery and motor execution are very similar processes (Hetu et al. 2013). Recently, Hardwick et al conducted large scale activation likelihood estimation meta-
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analyses to identify the individual networks involved in motor imagery, action observation,
and movement execution. Moreover they provided quantitative comparison among these three
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networks (Hardwick et al.2018). They summarized data from 303 motor imagery, 595 action
observation and 142 movement execution experiments. They revealed that motor imagery and action observation recruited similar cortical networks. In detail, the action execution network included activations in the primary motor and somatosensory cortex, dorsal and ventral
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premotor cortex, supplementary (SMA) and cingulate (CMA) motor areas, inferior frontal
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gyrus (IFG), inferior parietal lobule (IPL), thalamus, putamen, and cerebellum. The action
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observation network, which was largely bilateral, included activations in the dorsal and ventral premotor cortex, pre-SMA, IFG, insula, IPL and SPL, temporal, fusiform and occipital gyri,
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but not in subcortical regions. Finally, the motor imagery network, also largely bilateral, included activations in the primary somatosensory cortex, dorsal and ventral premotor cortex,
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SMA and pre-SMA, CMA, IFG, dorsolateral prefrontal cortex, SPL and IPL, insula, basal ganglia, and cerebellum. Interestingly, conjunction analysis across all three tasks identified a consistent premotor, parietal, and somatosensory network of brain areas recruited across all
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conditions of motor imagery, action observation, and movement execution. The authors suggest that the smaller portion of the parietal cortex involved in the action execution network, compared to the larger portion involved in the motor imagery and action observation networks,
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may be due to the simplicity of motor tasks employed in the execution conditions compared to more complex tasks employed in the observation and imagery conditions. They also suggest
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that the consistent recruitment of the somatosensory cortex in all three conditions indicates that simulation of the action during its observation and imagery may implicate a sensory efference signal similar to that induced by execution of the action. Finally they suggest that the common network consistently activated in the three analyzed motor conditions does not necessarily reflect “functional equivalence”, and that a mixture of matched and unique processes may occur in regions activated in common during performance of overt and covert actions.
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Meta-analyses have certain strengths but also several limitations. Meta-analyses overcome the sample-inadequacy of individual neuroimaging studies typically involving only 12-20 subjects, by pooling data from several publications and consequently allowing thousands of subjects to participate. Also, meta-analyses provide quantitative results outstripping simple review articles. However meta-analyses face a number of limitations. For example, a serious concern is related to the variety of motor tasks examined in the numerous neuroimaging studies used in the meta-analyses presented above. These tasks range from very simple movements to
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complex actions and from non-goal directed movements to actions involving an object. As already mentioned, over the last two decades overt and covert actions in humans were
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examined separately from each other. Indeed, most of the available studies did not use all three action conditions; they did not require the participant to imagine, observe and execute the same action. In effect the problem is much bigger if we take into account the fact that simple actions were often used to study execution conditions (due to practical restrictions in the magnet)
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whereas more complex actions were used to study overt conditions. Moreover, beyond the
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nature of the task, the number, age, and gender of the participants, another important variable
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which could influence the results of meta-analyses is the type of instruction given to the participants in each one of the included studies. For example, the fact that the primary motor
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cortex was recruited during motor imagery in one meta-analysis (Grezes and Decety 2001) but not consistently activated in another one (Hetu et al. 2013) may be due to performance of either
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kinesthetic or visual imagery by the participants, depending on the instruction given by the experimenter. Also, coordinate-based meta-analyses use reported peak activations for the analysis, discarding further spatial information from the original studies. Therefore, pooling of
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data from very different studies diminishes or even deletes results related to somatotopy. Finally, meta-analyses using activation likelihood estimation reveal only the consistency of activations across studies while no information is provided about the strength of activations. In
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summary, the results of any given neuroimaging experiment used in a meta-analysis are influenced by various parameters specific for each study, including the experimental design,
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the implementation of the paradigm, the task requirements, the number age and gender of the participants as well as the analysis of the data. Meta-analyses cannot possibly take into account all these variables of the studies they include. This is the reason why in the following section we extensively refer to two human fMRI studies, which are the only ones to our knowledge investigating the whole brain in all three conditions of execution, observation and imagination of the very same motor task (hand action) in the very same subjects. The general conclusion provided by the five meta-analyses presented above (Grezes and Decety 2001; Caspers et al. 16
2010; Molenberghs et al. 2012; Hetu et al. 2013; Hardwick et al.2018) is that the areas involved in action perception and motor imagery extend well beyond the two classical mirror neuron regions, and therefore a process broader than mirroring is responsible for motor cognition. This conclusion is in agreement with our findings in the monkey studies. Moreover, our high resolution
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C-deoxyglucose studies provide further solid, detailed, somatotopic information
concerning the substratum of a covert hand action (action execution) and its overt equivalent
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(action observation).
1.2.2 Studying an overt action and its covert equivalents
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As already mentioned, over the last two decades overt and covert actions were investigated in
isolation from each other in humans, comparing only two types of conditions at a time, e.g. execution with observation of hand actions, or observation with
imagery, or
execution/imitation with imagery of hand actions. This is demonstrated by the fact that out of
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335 papers cited in two recent meta-analyses of action observation (Caspers et al. 2010) and
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motor imagery (Hetu et al. 2013) only 18 are cited in both (Vogt et al. 2013). However, only a
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study of activations induced by execution, observation and imagination of the very same hand action, across the entire human brain of the same subjects would allow for direct comparison
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of effects induced by all three motor conditions. To our knowledge, two studies in the literature fulfill these requirements. First, in an fMRI study Filimon et al investigated the effects of
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execution, observation and imagination of the same visually guided arm-reaching task in the entire human cortex of 14 subjects (Filimon et al. 2007). Second, we recently used fMRI to assess areas activated for an overt action and its covert equivalents throughout the whole human
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brain without any prior assumption of areas expected to be activated (Simos et al. 2017). We asked 21 participants to execute, observe and imagine (1st person motor imagery) a visually guided tracing task with the index of the right hand. Using human subjects we could search the
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effects of motor imagery reflecting high-level simulation process, in addition to the action perception reflecting low level simulation process (Barlassina and Gordon 2017) that we
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studied using monkeys. Needless to say that action imagination is of utmost importance because it allows one to generate sensory-motor images and employ them in a variety of motor tasks, to plan and recall personal experiences as well as to actively understand what someone else is experiencing. As already mentioned, all relevant meta-analyses include studies using a variety of tasks, subjects, and motor conditions. Given that different types of tasks and goals may involve different cortical areas, we would expect some effects to be washed out in the meta-analyses, 17
which therefore would provide fewer overlapping brain activations between overt and covert actions compared to the two studies presented above (Filimon et al. 2007; Simos et al. 2017). Moreover, given that a re-analysis of the Filimon et al data, eight years after the original publication, revealed more cortical areas activated by the covert actions and more overlapping activations between the overt action and its covert counterparts (Filimon et al. 2015), and also given that their task was simply reaching to visual targets whereas ours was a visually guided tracing task, we would expect our study to provide more activations and most probably more
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overlaps between the overt action and its covert equivalents. Indeed, we found that several brain areas were activated in common across all three motor conditions (Table 1). These areas
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included the upper limb representation of the primary motor and somatosensory cortical areas, the dorsal and ventral premotor cortex, the supplementary motor cortex (SMA-proper), BA 7
in the superior and BA 40 in the inferior parietal cortex, BA 8 in the middle frontal gyrus (MFG), BA 22 in the posterior part of superior temporal gyrus (pSTG) including the temporo-
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parietal junction (TPj), BA 37 in the posterior part of the middle temporal gyrus (pMTG)
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including the extrastriate body area (EBA), the extrastriate visual BA 19 in the cuneus, the
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lingual gyrus (LG) and the middle occipital gyrus (MOG), BA 7 in the posterior precuneus and BA 37 in the fusiform gyrus [see Supplementary Materials in (Simos et al. 2017)].
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Interestingly, the activations in MI- and SI-upper limb regions were contralateral to the moving arm and more intense and extensive for the overt compared to its covert actions, whereas
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parietal and temporal activations were more extensive bilaterally for the covert actions compared to their overt counterpart. The SI- and MI-upper limb activations indicate that sensory-motor consciousness during covert actions is tied to the primary somatosensory and
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primary motor cortical areas rather than only to high order non-sensory neural populations. These effects in humans resemble the action-execution/observation results in monkeys. They are also in considerable agreement with most of the results in the Filimon et al study (Filimon
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et al. 2007) and its subsequent re-analysis/re-evaluation (Filimon et al. 2015), with the exception of areas MI and SI which remained unaffected with their original univariate fMRI
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analyses in 2007 and were not re-analyzed by multi-voxel pattern analysis in 2015. Remarkably, our finding in both humans and non-human primates that the upper
limb/forelimb region in SI is less activated for observation than for execution of the same action, does not support the idea of predictive coding via neural suppression (Blakemore et al. 2000). Explicitly, it was suggested that if an action is efficient and no adjustment is needed, the motor system does not need to pay attention to the sensory feedback and therefore the somatic consequence of the movement is suppressed. In contrast, if there is an error in the 18
prediction, the sensory system is disinhibited and sends a robust feedback signal. In other words, strong somatic feedback means wrong prediction (error detection) whereas weak feedback means correct prediction. It was thought that predictive coding via neural suppression provides an answer to why we cannot tickle ourselves: our brain predicts the sensory consequences of our movement thus decreasing their perceptual impact, but can’t predict others’ movements so they are more intense perceptually (Blakemore et al. 2000). In contrast to this suggestion, we have demonstrated that the correct prediction which occurs during action
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execution elicits strong activation of the somatosensory cortex, and the deviation from somatic prediction which occurs during observation of others’ movements elicits weak SI activation.
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The activation of both the superior and inferior parietal cortex for action perception,
demonstrated in both studies (Filimon et al. 2015; Simos et al. 2017), is compatible with the established knowledge that the parietal lobule is a bridge between perception and action, with neurons involved in higher order sensorimotor integration during hand manipulation tasks
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(Mountcastle et al. 1975). It is also in agreement with studies reporting that patients with
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parietal lesions are unable to recognize the meaning of gestures (Heilman et al. 1982; Sirigu et
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al. 1995; Sirigu et al. 1996). These results are also consistent with previous reports that the superior parietal lobule is involved in monitoring internal motor signals and predicting the
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sensory consequences of movements (Desmurget et al. 1999), while the inferior parietal lobule is engaged in awareness of our actions (Desmurget et al. 2009) and conscious motor intention
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such as the subjective experience of ‘wanting to move’ (Desmurget and Sirigu 2012). Activation of the multimodal pMTG by all three motor tasks (Simos et al. 2017) is compatible with reports that this area carries high level representations (Kreiman et al. 2000; Quiroga et
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al. 2005; Cattaneo et al. 2010; Urgesi et al. 2014; Kemmerer 2015), and complements a previous study implicating this region in conceptual action representations (Kable et al. 2005). Activation of the EBA in all three motor conditions complements studies reporting its
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involvement in executed, observed and imagined body-part movements (Astavief et al. 2004; Arzy et al. 2006; Hamilton et al. 2006; Filimon et al. 2007; Gazzola and Keysers 2009).
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Actually, the EBA was selectively activated in contrast to the fusiform body area (FBA) which remained unaffected in our study, presumably because the former is known to respond to local body-parts whereas the latter to holistic images of the body (Taylor et al. 2007). Although initially the prevailing view was that the human EBA is a distinct visual area which overlaps substantially with hMT (Downing et al. 2007; Peelen and Downing 2007), later on more than one limb-selective regions were reported in both humans and monkeys. These regions are organized around EBA/hMT in humans and spatially match those in FST and STP in monkeys 19
(Weiner and Grill-Spector 2011). The latter description, in association with the reported functional analogy of human EBA with monkey area V3, shows that human results match very well our findings in monkeys (see previous section). Moreover, activation of the TPj for all three conditions agrees well with studies reporting involvement of this area in perception and imagination of actions (Ruby and Decety 2001; Farrer and Frith 2002; Decety and Sommerville 2003; Vogeley and Fink 2003; Blanke et al. 2005). Once again, this findings in humans match those in monkeys, because the area activated in the middle part of the superior temporal cortex
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(mid-STS) of monkeys has very similar connectivity profile with human TPj, and both these areas are associated with predicting the behaviour of others (Behrens et al. 2008; Mars et al.
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2013). Actually, human TPj was suggested to have evolved from an expansion and further
specialization of monkey mid-STS (Mars et al. 2013). Given the 25 million years of separate evolution across the old world monkey and the hominoid species (Stevens et al. 2013), it is not surprising that we are not absolutely certain about homologies of human and monkey brain
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areas. Finally, in addition to the above areas activated for all three motor conditions,
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conjunction analysis also revealed a set of areas activated in common only for execution and
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observation, but not for imagery (Simos et al. 2017). These areas included the right SI, left SII, right EBA/MT, and left posterior precuneus (BA7).
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The finding that fewer areas, overall, were found active in humans than in monkeys during action observation can be explained by the facts that (i) the fMRI has a much lower 14
C-DG method which we applied on monkeys, (ii) histological
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resolution than the
identification of activated areas was not feasible with the fMRI imaging, and therefore cortical areas could not be subdivided as were with the 14C-DG method, and (iii) the fMRI measures
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blood flow rather than neural activity. At this point we should note that a strong correlation between blood-oxygen-level dependent (BOLD) signals and local field potentials (LFPs), and a slightly weaker correlation between BOLD and multi-unit activity was initially reported
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(Logothetis et al. 2001; Logothetis and Wandell 2004), whereas later on weaker correlations even between BOLD and LFPs in the visual cortex of awake behaving monkeys were
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demonstrated (Goense and Logothetis 2008; Logothetis 2008). Also, uncoupling of neural activity and BOLD has often been reported (Ekstrom 2010), and differences in the neurovascular relationship have recently been demonstrated (Hall et al. 2016). Despite the above mentioned problems associated with fMRI studies, our results in monkeys for execution and observation of the same action were very similar to those in humans. Altogether, they demonstrate that action perception recruits the full array of somatosensory, visual and motor cortical circuits which support action execution in both monkeys and humans. 20
Not only action observation which reflects a low-level simulation process, but also motor imagery reflecting a high-level simulation process shares with its equivalent overt action common brain circuits, and presumably similar contextual representations (Filimon et al. 2015; Simos et al. 2017). In addition to the areas activated for all three motor conditions described above, conjunction analysis revealed a set of areas activated in common only for execution and imagery but not for observation (Simos et al. 2017). These areas included SMA-proper, cingulate cortex and BA22 bilaterally, the right/ipsilateral prefrontal cortical area 9/46d, the
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left/contralateral parietal BAs 5, 7, 40, and BA 37 in the fusiform gyrus. Overall, the results on motor imagery demonstrate that virtually the same cortical circuits support the execution and
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imagination of the same action in humans. Therefore if we imagine an action, its subsequent
execution should be facilitated, and this way motor skills can be learned not only by observation, as mentioned earlier, but also by mental training (Ridderinkhof and Brass 2015; Eaves et al. 2016). In fact, because they take place without any physical exercise, observational
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learning and mental training may turn out to be of great social value.
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Finally, we used the Network Based Statistic and Graph theory modeling (Bullmore and
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Sporns 2009; Rubinov and Sporns 2010; Zalesky et al. 2010) to perform functional connectivity analyses, in order to assess the relative importance (connection strength) of
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individual regions for information flow within each task-related network (Simos et al. 2017). Connectivity analyses corroborated the notion that a common sensory-motor fronto-parieto-
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temporal cortical network is engaged for execution, observation, and imagination of the very same action. Cases of divergence in connectivity targets of the areas activated in common in the three motor conditions are discussed in the action-attribution section below. We thought
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that all the above reported findings taken together could not be linked into a unified system of motor cognition other than that of the mental simulation of action (Jeannerod 2001; Savaki 2010). Our suggestion that mental simulation of the physical action takes place during action
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imagination and observation is also supported by reports, such as, that imagined actions are characterized by the same temporal regularities and responsiveness to physical laws as their
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overt equivalents (Decety et al. 1989; Sirigu et al. 1995), and that simulation of the kinematic characteristics of a movement occurs during its observation (Agosta et al. 2016). In summary, in the absence of any other convincing scheme, the mental simulation theory provides a reasonable interpretation of the available human and monkey data. This proposal of ours will be further discussed in a following section in relation to an influential contemporary aspect of motor cognition, the mirror neuron account. Also, the overlapping brain machinery involved in (i) action execution, (ii) the non-conscious automatic action observation, and (iii) 21
the conscious effortful motor imagery indicates that mental concepts may be generated by sensory-motor cognitive primitives, supporting embodied theories of motor cognition. This subject will also be presented analytically in a following section.
2. Attribution of action to the correct agent
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2.1 Non-human primates
In the previous section we demonstrated that to understand the actions of others is to recruit
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the entire action execution system including somatosensory, visual and motor representations of the action. Apparently, these shared representations, serving action execution and recognition, are self-relative but agent-neutral. The question remains why we do not confuse
others’ actions with ours, given that the same neuronal network is assigned the double duty of
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execution and observation of the same action. In other words, given that the same sensory-
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motor neuronal circuits are recruited in both motor conditions, how do we attribute the action
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to the correct agent, i.e. how do we attribute the action to the self during action execution and to the other during action observation? How do we distinguish between self-produced and
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externally-generated actions? How do we distinguish between actor and spectator, between self and other? Presumably, there must be self-referential agent-specific representations, i.e.
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representations that the actor does not share with the spectator, which serve the attribution of action to the correct agent. Here, we will present examples of the potential neural substrate and mechanism encoding agent-specific effects, based on the results of our studies in monkeys.
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First, the qualitative similarity between the execution and observation masks quantitative differences in the intensity of activation of different relays, differences which could be revealed only by a powerful quantitative imaging method such as the 14C-DG that we
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applied on monkeys. For example, although cortical areas MI-forelimb and SI-forelimb were activated in both motor conditions, these activations were weaker by 50% for action
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observation than for action-execution (Raos et al. 2004, 2007). We suggested that the stronger activation of MI-forelimb for action execution reflects the intended movement (input from the premotor cortex) and the actual motor command (MI neuronal activity), whereas the 50% weaker activation for action observation may reflect the intended movement alone (Fig. 2). Indeed, during action observation the command component is absent and therefore there is no overt movement. Consequently a sense of agency as well as a sense of reality versus simulation may be embedded in MI, which in fact was suggested to compute sensory information for the 22
upcoming movement (Georgopoulos 2000). Likewise, the stronger activation of SI-forelimb region for action execution reflects the predicted/anticipated somatosensory consequence of the movement (based on efferent copy from MI) and the actual re-afferent feedback (signal from the contracting muscles), whereas the 50% weaker activation for action observation may reflect the predicted consequence of the movement, alone, because of the lack of somatosensory feedback due to the absence of overt movement (Fig. 2). It may be that, when the predicted somatosensory signal matches the actual feedback from the periphery, we know
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that we are generating the action. Also, when there is a mismatch between the predicted
somatosensory signal (present) and the actual somatosensory feedback (absent), we recognize
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that we are observing an externally generated action. Consequently, the sense of agency may
at least partially derive from comparison of unimodal signals in primary sensory and motor cortical areas, reflecting (i) the intention to move and the movement command in MI, and (ii) the anticipated/predicted sensory feedback of movement and its actual sensory re-afferent
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feedback in SI. In a similar vein, and in accordance with previous suggestions (Wolpert et al.
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1995; Blakemore et al. 1998; Tsakiris et al. 2007; Apps and Tsakiris 2014), the weaker
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activations for observation compared to execution that we recorded in the parietal and insular somatorecipient areas may reflect the incongruity between the predicted/simulated somatic
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end-result of the action and its actual afferent somatic feedback, and may also contribute to the attribution of action to the external agent. In summary, the parameter of the sense of agency
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may be articulated in the degree (strength/weakness) of activations, within and throughout the distributed somatosensory-motor network activated in common for execution and observation of the same action. In this case, the degree of activation reflects the integration of bottom-up
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exteroceptive and top-down interoceptive signals. Second, other differential activations which we recorded between execution and observation, and which could contribute to the agency ascription, are those concerning
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lateralization of effects. The statistically significant effects induced by action observation in the parieto-frontal sensory-motor cortical areas were in general bilateral, whereas those
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induced by execution were mainly contralateral to the moving forelimb (Fig. 3). This finding signifies specificity of the effects related to hand-identity for action execution, and small importance of the information related to hand lateralization for perception of the same action executed by another subject. Therefore, we suggested that these differences in lateralization of activations between executive and perceptual networks may help attribute the action to the correct agent, i.e., to the self during action execution and to the other during action perception. Once again, based on our quantitative results on non-human primates, we suggested that the 23
sense of agency may be articulated within the core sensory-motor components of the circuitry supporting action execution and observation, rather than within a single, agency attribution dedicated, master area in the parietal (Sirigu et al. 1999; Ruby and Decety 2001; Farrer and Frith 2002; Blakemore and Frith 2003) or in the prefrontal cortex (McCabe et al. 2001; Vogeley et al. 2001; Platek et al. 2004) as previously thought. Third, at the level of the prefrontal and premotor cortical areas we found that the reciprocally connected areas, 9/46d of the dorsolateral prefrontal cortex and F7 of the dorsal
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premotor cortex were exclusively activated for action observation (Raos et al. 2007; Raos and Savaki 2017). As already mentioned, area F7 activated by 9/46d specifically for observation
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may subsequently inhibit the forelimb representation at the level of the spinal cord (Kuypers 1981; Keizer and Kuypers 1989; Dum and Strick 1991; He et al. 1993) which, indeed, was
found depressed exclusively for action observation (Stamos et al. 2010). This inhibition of the forelimb region in the spinal cord, specifically for action observation, may be responsible for
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suppressing execution of the particular observed movement by the onlooker, although his/her
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execution-related sensory-motor system is activated. This prefronto-premoto-spinal circuitry,
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engaged only during action observation, is agent-specific and may also contribute to the attribution of action to the correct agent, in this case to the external agent and not to the self.
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We suggest that area 9/46v (9/46d) activated exclusively for execution (observation) within the dorsolateral prefrontal cortex may monitor and manipulate within working memory
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multimodal information, incoming not only from the premotor but also from the association parieto-temporal cortical areas (Petrides and Pandya 1984; Petrides 2005). This information from the parieto-temporal cortex may be related to the congruence (incongruity) between the
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intended (mentally simulated) movement and its actual (anticipated) somatosensory and visual consequences. This congruence (incongruity) monitored by the association cortical regions and relayed to the premoto-spinal circuitry may contribute to the attribution of action to the correct
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agent, that is, to the self (external subject) during action execution (observation). The above suggestion agrees with previous reports that the experience of agency may be based on
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comparison of motor commands with the re-afferent feedback from the moving muscles and the external events caused by these commands (Haggard et al. 2002; Johnson and Haggard 2005). Fourth, in the temporal cortex we found that multimodal area STP which is implicated in the integration of different sensory modalities (Boussaoud et al. 1990; Mistlin and Perrett 1990) was activated much more for observation than for execution in our study (Kilintari et al. 2014). This finding is in accordance with the neurophysiological results of Perrett’s group, who 24
demonstrated greater involvement of STSa/STP neurons in the perception of movements made by other subjects’ body parts than by the monkey’s own forelimb (Perrett et al. 1985a; Wachsmuth et al. 1994; Jellema et al. 2000). In fact, the much greater activation of the highorder multimodal area STP for observation than for execution, in contrast to the much bigger activation of the lower order unimodal parieto-frontal cortical areas for execution than for observation, constitutes additional differential effect which may contribute to agency attribution.
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In summary, our results indicate that agent-specific differences supporting attribution of
action to the correct agent may be distributed throughout the neural circuit responsible for
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action execution and observation, and may be encoded in differences in the degree and lateralization of the effects. There is no need for the execution/observation circuit to report to
a master area of agency ascription. Where such an area would report to? Infinite regress would be lurking in the background. All our results taken together, action generation, action
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perception, and action attribution may all be supported by different states of the same system,
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rather than by different systems (Savaki 2010). Once in place the action-execution system can
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function as simulator for motor cognition, and can also house the agency attribution mechanism. There is no obvious reason why an action would embody different brain substrata
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for its planning, execution, recognition, imagination and assignment.
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2.2 Humans
Human experiences, ‘perceptual’ as well as ‘imaginary’, are fundamentally private. Nevertheless we can infer their existence in other people from our own experiences. It does not come as a surprise that understanding other subjects’ experiences involves the brain circuits of
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our own relevant experiences. The question is why we do not confuse our real with our imagined experiences, or our own with others’ experiences? Apparently, there is a difference
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between knowledge of other minds and self-knowledge, which is responsible for attributing the experience to the correct agent, and which must be encoded either within or on the side of the
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brain circuit sub-serving the experience in question. The two fMRI experiments presented above demonstrate that the neuronal network supporting execution of action is also activated for observation as well as for imagination (1st person motor imagery) of the same action. The pertinent questions are: (i) why humans do not confuse others’ actions with their own, and (ii) why humans do not confuse their own physical actions with their imagined ones? In other words, why we do not confuse covert with overt actions given that the network of the overt action is also assigned the duty of its covert counterparts? Of course, the semi-quantitative 25
nature of activations, the lack of histological sections and the much lower resolution of the fMRI results in humans compared to the 14C-DG results in monkeys, do not allow for thorough conclusions and therefore comprehensive suggestions concerning agency attribution in humans. In any case, agent-specific effects such as possible differences in the pattern of activations among the three conditions, and/or possible divergence of connectivity targets of areas activated in common could distinguish between self-executed and observed actions as well as between self-executed and self-imagined actions. Here, we will present examples of
presented in a previous section (Filimon et al. 2015; Simos et al. 2017).
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both types of the above mentioned agent-specific effects provided by the two human studies
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First, in humans we found certain differences between the effects induced by overt and covert actions, within core areas of the execution-related system, similar to those in non-human primates described in the previous section. For instance, the fact that the overt action engages a larger fragment of the contralateral primary motor and somatosensory cortical areas
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(preserving hand-identity) compared to its covert equivalents, whereas covert actions engage
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larger bilateral part of the adjacent prefrontal/premotor and parieto-temporal cortical regions
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compared to the overt action, may contribute to attributing the action to the correct agent (Simos et al. 2017). Also, our finding that the right hemisphere is more involved in covert than
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overt actions complements not only our non-human primate results (Raos et al. 2007; Evangeliou et al. 2009), but also previous reports about the involvement of the right hemisphere
Daprati et al. 2010).
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in agency attribution (Ruby and Decety 2001; Decety and Sommerville 2003; Farrer et al. 2003;
Second, the targets of connectivity of the core somatosensory-motor areas activated in
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common diverged across the three motor conditions, with the network of connections of the covert actions being more complex than the execution-related network (Simos et al. 2017). These different patterns of functional connectivity may contribute to the distinction between
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the overt action and its covert counterparts, and therefore to the attribution of action to the correct agent. For instance the central position of the right IFG in functional connectivity, that
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we found during motor imagery, may reflect the suppression of movements during mere imagination of action, and may contribute to the distinction between imagined and real action, in a similar manner to that already described for action observation in non-human primates. Indeed, IFG has been associated with response inhibition (Rubia et al. 2003; Aron et al. 2004). Also the dense functional connections between the right MFG and bilateral IPL, that we found during motor imagery, may be related to the obligation of the participant to mentally act in spite of the incongruence between intended/predicted and actual sensory effects (Fink et al. 26
1999). Moreover, the central role of the right EBA in observation, evaluated by functional connectivity analysis, may be related to the attribution of action to the external agent as opposed to the self. Indeed, EBA has been associated with the actor-spectator distinction (Jeannerod 2004; Myers and Sowden 2008). Third, the enrollment of the pre-SMA and the IFG in motor imagery but not in action execution (Simos et al. 2017) may contribute to the attribution of action to the imaginary as opposed to the actual world. Actually, the IFG is known to play a role in action imagination
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(Stephan et al. 1995; Decety 1996; Grafton et al. 1996), and the pre-SMA which projects to the
prefrontal cortex (Picard and Strick 1996) is known to play a role in movement ideation
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(Gerardin et al. 2000) and agency-related processes (Crivelli and Balconi 2017). Activation of high order multimodal area(s) in the prefrontal lobe, unaccompanied by activation of the execution-related core sensory-motor areas, could indicate amodal representation of motor cognitive concepts. In contrast, the fact that the primary somatosensory-motor cortical areas
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are activated together with higher order areas for both overt and covert actions indicates modal
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representation of motor cognition, as will be analytically presented in a following section.
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Fourth, even within areas of overlapping functional activations the pattern of activation across voxels is sufficiently different to discriminate between overt and covert hand actions
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(Filimon et al. 2015). More specifically, multi-voxel pattern analysis allowed identification of voxels predictive of a particular condition, although activated by all three motor conditions. In
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the frontal lobe, voxels potentially distinguishing between execution, observation and imagery of reaching were located in the inferior frontal gyrus, ventral premotor, dorsal premotor and middle frontal cortical areas. In the parietal lobe, discriminative voxels were contained in the
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inferior parietal lobule, intraparietal sulcus, superior parietal gyrus and the precuneus. These findings suggest that information distinguishing between overt and covert hand actions may be represented throughout the fronto-parietal sensory-motor cortical network.
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In summary, the human data although of lower resolution than the non-human ones
further support our hypothesis that agent-specific differences, underlying attribution of action
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to the correct subject, are distributed throughout the sensory-motor neural circuit responsible for action execution and observation. Moreover, they suggest that information about the attribution of our own actions to the real or imaginary world may also be distributed throughout the very same sensory-motor circuit. All in all the action-execution system, depending on diverse states of its activation and functional connectivity patterns, can serve motor cognitive functions such as action perception and motor imagery, and also can house the agency attribution mechanism. 27
3. Mental simulation theory vs mirror neuron aspect of motor cognition
In this section we discuss the above mentioned findings in relation to a dominant contemporary view of motor cognition, the mirror neuron aspect of action understanding, and in reference to the two main neuro-philosophical theories of cognition, the mental simulation theory and the
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theory-theory. The theory-theory posits that we understand the behaviour of others by theoretical reasoning according to causal laws of behaviour, i.e. by pulling mental concepts out
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of a body of implicit knowledge that we all possess and by applying mental generalizations. The simulation theory implies that we understand the behaviour of other agents by impersonating their mental life, i.e. by pretending to have their beliefs and desires and by practically simulating their behaviour. In predicting or interpreting others’ actions we simulate
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them by using part of our own sensory-motor system ‘off-line’. This system can function off-
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line disengaged from its natural inputs from perception, can be fed with pretend inputs, and
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uncoupled from its natural command for action can predict possible outputs, e.g. how an external agent may act in a given situation (Gordon 1986). Simulation assumes a prior
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understanding of the intention that the interpreter attributes to the external agent, and may be introspectively vivid or semi-automatic with little salient phenomenology (Goldman 1989). A
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crucial assumption of the mental simulation theory in cognitive sciences is that the same brain mechanisms support the control of behaviour as well as the imagination and the comprehension of observed behaviour. This double-duty of behaviour-execution and behaviour-
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comprehension requires common underlying neural substratum and mechanisms (Cruz and Gordon 2006). In favour of this assumption, early on was found that the temporal characteristics (Decety et al. 1989; Sirigu et al. 1996) and the programming rules
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(Georgopoulos and Massey 1987) of overt actions are retained in their covert counterparts. In favour of this assumption are also our brain imaging results on non-human and human primates,
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as analytically presented in the two previous sections. The classical mirror neuron system consists of the premotor area F5 in monkeys (di
Pellegrino 1992; Gallese et al. 1996) which corresponds to the ventral premotor cortex (PMv) in humans (Petrides 2005), and the parietal area PF/PFG in monkeys (Gallese et al. 2002; Fogassi et al. 2005) which corresponds to BA40 of the inferior parietal lobe (IPL) in humans (Buccino et al. 2001). However, it is not clear why the primary motor (Tkach et al. 2007; Dushanova and Donoghue 2010; Vigneswaran et al. 2013) and the dorsal premotor cortex of 28
the monkey (Cisek and Kalaska 2004; Tkach et al. 2007) as well as the medial temporal lobe of humans (Mukamel et al. 2010), which also contain neurons with mirror-properties, are not considered parts of the mirror neuron system (Rizzolatti and Sinigaglia 2010; Rizzolatti et al. 2014; Rizzolatti and Sinigaglia 2016). Also it is not clear why the primary and secondary somatosensory areas, although activated for action observation, are not considered part of the human mirror neuron system and are dismissed with the unfounded explanation that their activation is due to an “additional mechanism” triggered by the mirror mechanism, or why the
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activation of the dorsal premotor and the superior parietal cortical areas is also dismissed as reflecting “motor preparation” and not mirror mechanism (Rizzolatti and Sinigaglia 2010).
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Moreover, it is not clear why the superior temporal sulcus (STS) was at first considered part of
the mirror neuron system (Rizzolatti et al. 2001) and later excluded from it (Rizzolatti and Sinigaglia 2010). These are a few examples of the scientifically unsubstantiated and therefore arbitrary inclusion/exclusion of brain areas in the mirror neuron system. In brief, the ground on
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the basis of which only two areas, the F5/PMv and the PF/IPL, are included in the mirror neuron
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system by Rizzolatti’s group over the years is shaky, based on statements which lack empirical
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support.
Another confusing issue is the functional role of mirror neurons. It is not clear what is
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encoded in the activity of mirror neurons when triggered by the observation of an action performed by another subject. It is not clear what it is that is “matched” between observation
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and execution of an action at the neuronal level. Mirror neurons in area F5 were reported to code for specific hand and mouth actions such as grasping, holding and breaking, as was the case for the movement related neurons of the same area (Rizzolatti et al. 1988; Gallese et al.
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1996). The mirror neuron aspect of action understanding proposes that a vocabulary of motor acts, limited in the mirror neurons of area F5, is responsible for our ability to understand the meaning of motor events (Rizzolatti et al. 2014). However, even today it is not clear whether
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action mirroring reflects low-level resonance mechanisms or high-level action understanding (Csibra 2007). Nor is it known whether mirror neurons in areas F5 and PF/PFG encode a
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“vocabulary” of actions or goals/intentions (Fogassi et al. 2005; Bonini et al. 2010), “the subjective value” of observed actions paired with reward (Caggiano et al. 2012) or something “more idiosyncratic” to the particular action situation (Nelissen et al. 2005). Interestingly, intentions, corresponding to the motor plan prior to motor command, have been considered the best candidate for shared representations of action (de Vignemont and Haggard 2008). However, goals and intentions are used interchangeably in the mirror neuron literature, and the experiments concluding that mirror neurons encode “goals/intention” have been heavily 29
criticized due to lack of control of several variables. For example, it was argued that mirror responses in those experiments may reflect the identity of the object to be grasped (Ruggiero and Catmur 2018) and/or the kinematics of movement (Papadourakis and Raos 2017) rather than the intention of the actor proposed by the authors (Fogassi et al. 2005). Moreover, it was
argued that given the range of meanings associated with a certain action and the range of actions associated with a certain goal, there must be a clear distinction between goals and implemented motor routines to achieve the goals (Hickok 2009). Furthermore, several
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inconsistencies exist in the relevant literature, such as, “we understand action because the
motor representation of that action is activated in our brain” although mirror neurons
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supposedly encode goals and not actions (Rizzolatti et al. 2001; Fogassi et al. 2005). Obviously, if mirror neurons encode goals and not motor aspects of the actions then the basis of action
understanding cannot be the activation of motor representations in mirror neurons (Hickok 2014).
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Anyhow, the proposed function of mirror neurons by the group of Rizolatti, i.e. encoding
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a motor vocabulary of actions and/or goals/intentions, is reminiscent of the theory-theory
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(Carruthers and Smith 1996) rather than the simulation theory (Gordon 1986; Heal 1986) because in order to understand others’ behaviour, according to the theory-theory we pull mental
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concepts out of a body of implicit knowledge (an encrypted vocabulary of behaviours) we all possess, whereas according to the simulation theory we mentally rehearse the observed
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behaviour and its decision making (engaging our own sensory-motor system). Although, the mirror neuron account of action understanding echoed the theory-theory (Simos et al. 2017), the mirror neuron theorists attempted to interpret their findings in the context of a mental
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simulation process (Gallese and Goldman 1998). Actually, the constant attempt of mirror neuron theorists to equate mirroring with simulation mechanisms, although it has been made
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clear that mirroring fails to satisfy the definition of simulation process (Gallagher 2007; Jacob 2008; Herschbach 2012; Spaulding 2012), is detrimental to the field. In contrast to ‘mirroring’ which involves the motor system by definition (Rizzolatti and Craighero 2004), ‘simulation’
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involves the motor system reflecting the action plan as well as the sensory system reflecting the somatosensory experience of the action and its visual guidance and update (Savaki 2010). As the time went by, a considerable amount of human neuroimaging data demonstrated significant activations for action observation in several brain regions beyond the two classical mirror neuron areas. For example, 15 years after the discovery of mirror neurons, frontal areas BA6, SFG, MFG, pre-cingulate, BA44, parietal areas SI, SII, SPL, the supramarginal gyrus of 30
the inferior parietal lobule, and the temporal areas MTG, ITG were considered part of the human mirror neuron system in addition to the original PMv and IPL (Gazzola et al. 2007). These experimental data which resisted interpretation by the original mirror neuron account of motor cognition, i.e. these anomalies, revealed that something was wrong with the structure of the theory. In response to these anomalies there were attempts to incorporate the new empirical data to the original theory, without abandoning any of its basic features. Although anomalies provided reasons to abandon central attributes of the theory in order to make theoretical
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concepts and empirical findings appear logical and economical, mirror neuron theorists ignored
some of the anomalies and incorporated others into the original theory without redefining its
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conceptual structure. In their attempt to (i) preserve the important role in action understanding (the mirror mechanism) only for the two originally described (classical) mirror neuron areas, and simultaneously in order to (ii) incorporate in the original circuit the constantly increasing areas involved in action observation, the mirror neuron theorists reused the “direct matching
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hypothesis”. This hypothesis retained the simulation process, which supposedly explained
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activations outside the two classical mirror neuron areas in terms of additional mechanisms,
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such as “sensory predictions from internal models” and “motor preparation”, triggered by the basic mirror mechanism (Gallese and Goldman 1998; Gallese 2007; Rizzolatti and Sinigaglia
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2010; Rizzolatti et al. 2014). In reality, two parallel processes were described: one which reflects movements and is driven by motor simulation, and another one that mirrors the goals
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of the action, is not driven by motor simulation and plays primary role in action understanding (Rizzolatti and Sinigaglia 2010). Thereafter, as a matter of fact, the mirror neuron account of action recognition referred to a hybrid theory, with the two original/classical mirror neuron
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areas (PMv and IPL) coding the inherent vocabulary of actions/goals, reflecting the theorytheory, and with the remaining/auxiliary areas involved in a simulation process reflecting the mental simulation theory. Nevertheless, the role of the proposed simulation process, in addition
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to the assumed encoding of a vocabulary of movements and goals, was not clear. In an attempt to explain it, they claimed that the parietal mirror neurons that discriminate action goals (e.g.
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grasping-to-place from grasping-to-eat) code large motor chains rather than individual movements, and activation of one segment of a chain triggers activation of the entire chain and therefore simulation of the observed action to ensure its understanding (Fogassi et al. 2005). However, to know which chain to activate, you have to know the end result of the action before its simulation. And if you know beforehand the end-result, what does simulation add to the understanding of the observed action (Churchland 2011)? Indeed if you already know which is
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the action and its goal, consequent simulation adds nothing to your knowledge. Simulation of the action is a step behind. Practically, the main objection to the extended mirror neuron account of action understanding is its complexity. It is more than twenty years since this theory appeared, yet mirror neuron theorists have failed (i) to specify the extent of the mirror neuron system, and (ii) to demonstrate that PMv and IPL play a more central role in action understanding than any other brain area containing mirror neurons and/or involved in action observation. The mirror
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neuron theorists cannot explain why they think that some of the areas that contain mirror neurons (i.e. PMv and IPL) are equipped with the “mirror mechanism” whereas some others
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(e.g. PMd and MI) are not (Rizzolatti et al. 2014). Overall, the proliferation of areas thought to
comprise the mirror neuron system and their capricious separation into core and auxiliary areas, the unsettled role of mirror neurons in action recognition and the constant modification of putative mechanisms supposedly employed by its core components (PMv and IPL) and its
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auxiliary areas (functioning on the side, triggered by core components) are symptoms of crisis
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of the mirror neuron view of motor cognition. Related to this crisis are fundamental problems
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with mirror neuron theorizing, reflected in a voluminous body of developed speculations and unjustified conclusions, invoking these neurons in accounts of empathy, evolution of language,
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autism, sexual orientation, political attitudes, business leadership, obesity, mass hysteria, contagious yawning, misattribution of anger in the music of avant-garde jazz saxophonists,
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development of Jungian collective unconscious archetypes and self-agency, to mention only a few [drawn from publications indexed in PubMed by (Hickok 2014)]. Also a sign of this crisis are the deflationary accounts of the possible roles played by the mirror neuron in recent
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literature (Brass et al. 2007; Dinstein et al. 2008; Hickok 2009; Heyes 2010; Savaki 2010; Oosterhof et al. 2013; Watson et al. 2013; Catmur 2015; Vannuscorps and Caramazza 2016), a let-down relative to early expectations, e.g. that mirror neurons would turn out to be the
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“neurons that shaped civilization” (Ramachandran 2009). The above mentioned assumptions, concerning the different functions of hard core and
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auxiliary areas of the mirror neuron system (Rizzolatti et al. 2014) which were divorced from empirical facts (Hickok 2009), and the inexplicable empirical findings (anomalies) which enforced expansion of the original circuit without revision of the original theoretical assumptions (Savaki 2010) destroyed the simplicity of the mirror neuron concept and turned it into a “degenerate” theory in the technical sense by Lakatos. Degenerate scientific theories are those, whose changes to their hypotheses are made out of the necessity to explain new troublesome evidence (anomalies), and whose predictions of novel facts are refuted so that they 32
are no longer testable (Lakatos and Musgrave 1970; Lakatos 1980). Indeed, attempts to revise the mirror neuron account, to make it compatible with new data of brain correlates of motor cognition, render it unfalsifiable. Notably, falsifiability is the demarcation criterion for a theory to be ranked as scientific (Popper 1992). In its effort to assimilate the ever increasing number of areas found activated for action observation, the mirror neuron theory invokes more and more arbitrary and convoluted mechanisms. This was precisely the problem of the geocentric Ptolemaic system of planetary motion. It collapsed under the burden of adding too many
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epicycles. To use Lakatos’ words, a core of assumptions cannot be abandoned or altered
without dethroning the theory altogether. On the bright side, anomalies create the crises that
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ultimately cause revolutions. Responses to anomalies supposed to provide the basis for incommensurability and revolutionary changes, which usually employ different conceptual systems (Kuhn 1970).
Awaiting such a revolutionary change, the mirror neuron aspect of motor cognition can
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be seriously challenged, nowadays, only by an alternative view which could better explain the
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available biological data. We think that such an alternative view of motor cognition was
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provided by our systematic and exhaustive quantitative investigation of the entire monkey cortex and spinal cord, and was further supported by our human study. Our results, as well as
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the segmented ones included in five relevant meta-analyses (Grezes and Decety 2001; Caspers et al. 2010; Molenberghs et al. 2012; Hetu et al. 2013; Hardwick et al.2018), demonstrate that
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areas engaged in action perception and motor imagery extend well beyond the two classical mirror neuron regions. They support the idea that a process broader than mirroring is responsible for motor cognition, as suggested earlier (Goldman and Sebanz 2005). We have no
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reason to believe that the recruitment of the forelimb-region in the primary motor, somatosensory, premotor and somatorecipient parieto-temporal cortical areas, during forelimbrelated covert actions and their overt counterparts, is less important for action perception and
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motor imagery than the recruitment of PMv and IPL as the mirror neuron theory claims. Consequently, our findings contradict the notion that mirror neurons in two brain areas alone
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code the meaning of actions. They support the notion that we decode the actions of others and we imagine our actions by activating our own action system off-line (without leading to movements) to internally rehearse or mentally simulate the observed or imagined actions. Striving to bring theory and fact into closer agreement, we suggest that to assign meaning to the actions of other people and to imagine our own actions we use, as a theory of mind, the simulation theory (Gordon 1986; Heal 1986). This theory is simple, self-consistent, plausible and compatible with all available biological data to our knowledge. All in all, the existing 33
finding that the sensory-motor apparatus serving an overt action also serves its covert counterparts, supports the simulation theory which assumes that a common mechanism underlies the control as well as the conception of actions. Of course, new empirical data could undermine the aspect of motor cognition we propose, just as the current data establish it. Instead of the original claim that mirror neurons are innate and result from an evolutionary adaptation for action-understanding (Rizzolatti and Arbib 1998; Rizzolatti and Craighero 2004), one could more plausibly suppose that mirror neurons are the by-product of
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associative learning (Keysers and Perrett 2004; Catmur et al. 2007; Heyes 2010). Interestingly, mirror properties have been shown to develop through sensorimotor learning and are neither
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innate nor fixed once acquired. This notion is supported by the findings that, the mirror properties depend on the experienced contingency rather than the similarity between executed and observed actions, and that even in adulthood, the mirror neuron system can be reconfigured by sensorimotor learning and can become counter-mirror (Catmur et al. 2007; Cavallo et al.
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2014; Catmur et al. 2017). At this point we should note that the associative property of mirror
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neurons is compatible with their participation in action perception (Cook et al. 2014) and in
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mental simulation of action (Savaki 2010) during which learned sensory-motor associations of the action are recruited. Moreover, our suggestion that mental simulation of an action along
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with its sensory effects underlies motor cognition is analogous to the notion that motor cognition is embodied in action, and supports modal theories of knowledge (see following
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section of discussion) proposing that sensory–motor simulators implement fully functional conceptual systems (Barsalou 1999). After all, what is simulated is bodily experience (Barsalou 2008b).
Finally, hybrid theories have also been proposed. As we already described, the mental
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representation of an action is a ‘pseudo-pragmatic’ percept, with all its constituents corresponding to those of the physical overt action. A hybrid theory of cognition would consist
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of propositional elements in addition to the pseudo-pragmatic ones. Authors have defended hybrid accounts of mindreading, with the mental simulation theory playing the main role and
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the theory-theory a minor but non-negligible role (Heal 2003). Such an account of motor cognition cannot be excluded, although there is no empirical evidence that at least simple actions, such as the ones used in our studies, contain a propositional element requiring a role played by the theory-theory.
4. Embodied cognition 34
According to rationalists, such as Plato, Descartes and Kant, concepts are a priori or innate, and propositions are formed and justified only by reasoning. Following up on rationalism, in the early decades of cognitive psychology most theories of human knowledge implied centralized and abstract cognition involving propositional forms of knowledge, while sensorymotor systems were thought to serve only as peripheral devices. On the other hand, according to empiricists, such as Aristotle, Locke and Hume, concepts are a posteriori, and rationally
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acceptable propositions are justifiable only through experience. Influenced by empiricism,
certain theories imply that human cognition is grounded in perceptual processes and motor
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control, and the mind should be approached in the context of its relationship to a body
interacting with the environment (Barsalou 1999; Lakoff and Johnson 1999). According to these theories, in order to study cognition we must consider the situation and the situated cognizer in combination, because the mind and cognition extend not only beyond the brain into
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the body, but also beyond the body into the surrounding world (Clark and Chalmers 1998;
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Shapiro 2011). The mind emerges from the subjective experience of the body in the world
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(Leitan and Chaffey 2014), and cognition is distributed across the united system of mind, brain, body and environment (Wilson 2002). Nowadays, there is convergent neurobiological evidence
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supporting the empiricists’ view, and more specifically the perceptuo-motor theories of embodied human cognition. Accordingly, concepts derive from bodily experiences, and
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sensory-motor systems of the brain contribute significantly to their formation. The bodily experiences represented in these sensory-motor systems are ‘simulated’ during cognition (Barsalou 1999). Indeed, our high resolution cortical maps and our quantitative results
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throughout the entire monkey brain, as well as our human data, demonstrate an overlap of the sensory-motor neural nets supporting execution, observation and imagination of the same action. These results indicate the existence of close relations between the conceptual action
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representations underlying action perception and imagination on one hand and the executionrelated sensory-motor networks on the other. This finding is in favor of the embodiment view
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that concepts are essentially grounded in perception and action (Barsalou et al. 2003). In agreement with our finding that overt and covert actions share sensory-motor
representations, it was suggested that motor cognitive processes required for covert actions are embodied in the sense that they use simulations of sensory-motor processes, through reactivation of the neural circuitry which supports the physical overt action (Dijkstra and Post 2015; Korner et al. 2015). It was also reported that a given representation qualifies as embodied if it utilizes body-related codes, which are often associated with activations in sensory-motor 35
cortical areas (Goldman 2012a). A typical example of embodied cognition is the application of motor codes to nonstandard tasks, e.g. using bodily motor codes not to guide one’s own actions but to represent the actions of others (Goldman 2014). In fact, our finding that during covert actions the observer or cognizer engages in redeployment of bodily sensory-motor codes, which originally underlie action execution, supports the proposal that reuse of neural circuits for a variety of cognitive purposes is a fundamental principle of brain organization. Accordingly, neural circuits originally established for one purpose (e.g. action execution) are
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redeployed during development in the service of other functions (e.g. its covert counterparts), without losing their original purpose (Anderson 2010). In brief, our findings support neural
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reuse theories which claim that low-level bodily sensory-motor codes are reused in different arrangements to serve different cognitive domains. Perceptual, motoric and conceptual cognitive processes cannot maintain body-neutrality, because minds and brains have bodies.
More explicitly, we demonstrated that generation, recognition and imagination of the
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same action are supported by largely the same neural substrata. Activation of multimodal areas
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that combine information across modalities in the prefrontal lobe, unaccompanied by activation
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of the execution-related sensory-motor system, could indicate amodal representation of motor cognitive concepts. Instead, the fact that most of the components of the execution-related
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system, including the upper limb representation in the primary somatosensory and primary motor cortical areas, are activated together for both overt and covert actions indicates that
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multimodal as well as unimodal cortical areas are involved in conceptual processing. The high order prefrontal cortical regions engaged in covert actions could act as conceptual convergence zones (Damasio 1989) within the activated sensory-motor circuitry. They may be integrating,
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rather than storing, distributed modality-specific conceptual features and they may re-instate them in the core modality-specific sensory-motor cortical regions (Simmons and Barsalou 2003; Kiefer et al. 2007; Kiefer and Pulvermuller 2012). Therefore, the unimodal and the
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multimodal cortical areas activated in our studies may be equally involved in conceptual processing.
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Because sensory cortical areas were involved in action, and motor cortical areas in
perception and imagination in our studies, we suggest that sensory effects are combined with motor commands in integrated action codes. Indeed, it has been proposed that perceptual cognitions may be automatically grouped together with motoric cognitions, assigning the status of embodied cognitions (Barsalou 1999, 2008a). If actions become integrated with their somatosensory and visual effects, then observation of an effect may trigger the associated action. In other words, visual perception of an action executed by another subject may restore 36
in the observer’s brain the associated sensory-motor representations established during action execution as a consequence of correlated neuronal activity. This process may serve the understanding of observed action. Similarly imagination of an action, which presupposes knowledge of its effects, may recruit pertinent conceptual memory traces encoded in the sensory-motor representations of the action. These representations may run simulations of external situations ‘off-line’, i.e. decoupled from physical inputs and outputs, in order to
functional conceptual representation, a building block of cognition.
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support motor cognition. In this manner, a distributed sensory-motor system implements a
In summary, the results described in previous sections support modal rather than
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amodal/symbolic conceptual representations. They support the idea that conceptual representations may be multimodal simulations, distributed across modality-specific systems
(Barsalou 1999; Barsalou et al. 2003; Barsalou 2008a). They are compatible with the report that on-line aspects of embodied cognition (e.g. action execution) include activities related with
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task-relevant external situation and involve a mind/brain upholding a body interacting with a
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real-world situation by sensory-motor mechanisms. On the other hand, off-line aspects of
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embodied cognition include activities with external resources distant (e.g. action observation) or altogether absent (e.g. motor imagery) and involve mental simulation of the body interacting
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with the external world, also by sensory-motor mechanisms, serving the embodied motor cognition (Wilson 2002). All things considered, mental simulation of external events and off-
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line embodied cognition may reflect a general underlying principle of the human brainpower. According to embodied cognition theorists, intelligence lies less in the individual brain and more in the dynamic interaction of the brains and bodies with the wider world (Anderson 2003).
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It was recently argued that neuroimaging data do not allow discrimination between embodied cognition and amodal/symbolic accounts of conceptual representations, and that conceptual processing relies on abstract rather than sensory-motor representations (Caramazza
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et al. 2014; p.5). The basic argument was that “the overlap between sensorimotor mechanisms and semantic knowledge does not seem to occur within areas involved in low-level
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sensorimotor processing”. According to this argument, areas thought to carry low-level representations such as the primary sensorimotor cortex should not be engaged in covert actions such as action imagery which is based on semantic knowledge. In contrast to this argument, several studies in the literature demonstrate that the primary motor and primary somatosensory cortical areas are activated for covert actions [(Hari et al. 1998; Grezes et al. 1999; Avikainen et al. 2002; Raos et al. 2004, 2007; Tkach et al. 2007; Lorey et al. 2013), see also meta-analyses by (Grezes and Decety 2001; Caspers et al. 2010; Molenberghs et al. 2012; Hetu et al. 2013; 37
Hardwick et al.2018). In fact, our finding that the forelimb-regions within both the primary motor and the primary somatosensory cortical areas are specifically/somatotopically activated (i) in monkeys observing another subject executing an action with the arm/hand, as well as (ii) in humans during observation and imagination of hand-related actions, indicates that sensorymotor representations may function as conceptual semantic representations; that abstract mental concepts may be rooted in sensory-motor workings. In brief, our finding that the cortical network implicated in semantic representation of actions (covert actions) is isomorphic with
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that involved in factual experience (overt action) is a strong account of embodied simulation applied to neural structures (Meteyard and Vigliocco 2008).
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It may be argued that a cognitive process is not necessarily equivalent to the neural
activity which accompanies it, and that the type of content represented in each brain area involved in a physical or mental operation is not immediately accessible. However the region of forelimb representation, in premotor and motor as well as in primary somatosensory and
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other somato-recipient cortical areas, found to be specifically activated for observation of an
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action involving the forelimb (Raos et al. 2004, 2007; Evangeliou et al. 2009; Raos and Savaki
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2016) indicates that the activated circuit encodes some kind of meaning of the observed action. Also, the fact that motor imagery is characterized by the same temporal regularities and
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responsiveness to physical laws as action execution (Decety et al. 1989; Sirigu et al. 1995) cannot be unrelated to the meaning of the action. Moreover, activation of the somatosensory-
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motor cortex somatotopically during understanding action words/sentences related to body parts (Hauk et al. 2004; Pulvenmuller 2005; Kemmerer 2015) indicates that action concepts are stored in a motoric code, and understanding speech resonates mental simulation of its corporal content. Further support to embodied cognition is provided by the neurobiological
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evidence that not only understanding others’ actions, but also understanding others’ sensations, emotions and intentions rely on the sensory-motor representations of the corresponding actual
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experiences (Haggard and Clark 2003; Keysers et al. 2004; Avenanti et al. 2005; Blakemore et al. 2005; Bufalari et al. 2007). Many more examples of embodied cognition could be cited,
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such as, that the size of graspable objects is perceived as a proportion of the hand's size, highlighting that the body is used as a perceptual ruler (Linkenauger et al. 2010), and that distance is scaled by walking effort if observers are walkers, by throwing effort if they are throwers, and by reaching ability if they are reachers (Witt et al. 2010). Therefore, in contrast to the idea that cognition relies on amodal conceptual representations, several biological data indicate that motor cognition engages modal representations in a single sensory-motor network supporting both covert actions and their overt equivalents. We suggest that all areas of this 38
circuitry are necessary and none of them, on its own, sufficient for motor cognition. The precise nature of conceptual process in each area of the network and the weight of each processing node within it require further investigation. It was also argued that activation of sensory-motor representations may be a consequence and not an integral part of retrieving semantic knowledge (Mahon and Caramazza 2008; Caramazza et al. 2014). It was suggested that action mirroring follows (rather than causes) the
understanding of others’ mental states (Csibra 2007; Jacob 2008). Although it is tempting to
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think that our findings equate simulation with meaning, we cannot exclude the possibility that
mental re-enactment is an epiphenomenon rather than a process necessary and sufficient for
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meaning construction. Actually, any neural correlate of behaviour recorded by neuroimaging
may be an epiphenomenon, and meaning could be represented by abstract symbols which have no perceptual or motor substance, in brain areas which only secondarily activate the sensorymotor system. In such a disembodied cognition scheme, this activation need not be related to
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the meaning of the action, and is due to information spreading to the sensory-motor system
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without playing any conceptual role (Mahon and Caramazza 2008). We can counter this
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argument by pointing that, since mental events are facets of brain activity, doing, conceiving and mentalizing are all brain processes. Brain activation starts before the experience (Libet et
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al. 1983; Libet 1985). Brain activity instantiates conscious mental states rather than causes them. However, there is a brain-to-mind rather than mind-to-brain causation (Haggard and
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Eimer 1999). Consequently, if activity is spreading to the sensory-motor system and is playing no conceptual role as suggested earlier (Mahon and Caramazza 2008; Caramazza et al. 2014), there must be a master brain area which is informed about the course of the action ‘in advance’
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and orders the sensory-motor system to simulate the action. To date we have no empirical evidence in support of a master area commanding the sensory-motor system to act or mentalize. Moreover, if such an area existed the subsequent sensory-motor simulation would be redundant
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and therefore non-economical. Finally, what is it that would drive the master area? Once again we would be trapped in an infinite regress, unless we accept the presence of a Platonic ghost
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in the machine. In contrast, the presented results indicate that perception and mentalization are instantiated by neural activity in the sensory-motor system. In conclusion, current experimental data that the action-execution system can serve motor cognitive functions such as action perception and motor imagery and also can house the agency attribution mechanism, support the embodied condition theory implying that cognitive workings are grounded in sensory-motor processing, and that cognitive abilities depend on the mechanism of simulation. They contradict the neo-Cartesian dualism assuming separation 39
between mind and body or between mental states and physical substratum. They do not support the existence of a single high order master area with inferential role in processing motor concepts. However, although there is no empirical evidence indicating that mental reenactment of an action during practice of its covert equivalents is an epiphenomenon, there is also no proof that mental re-enactment is a necessary and sufficient mechanism of
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understanding and imagining actions.
Acknowledgments
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We acknowledge support by the project “Advanced Research Activities in Biomedical and Agroalimentary Technologies” (MIS 5002469) which is implemented under the “Action for the Strategic Development on the Research and Technological Sector”, funded by the
Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-
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2020) and co-financed by Greece and the European Union (European Regional Development
A
CC
EP
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M
A
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Fund). We also thank Adonis Moschovakis and Keith Frankish for their comments.
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Captions to figures Fig. 1 Schematic illustration of medial (upper) and lateral (lower) view of the left hemisphere of an inflated monkey brain. Light gray areas represent unfolded sulci: crowns of sulci are depicted by solid gray lines and
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fundi by dashed lines. Areas activated only for action execution (compared to the motion control) are color coded red; areas activated only for action observation (compared to the
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motion control) are in green; overlapping activations common for execution and observation
are in orange. a, anterior; AIP, anterior intraparietal; As, arcuate sulcus; c, caudal; CM, caudal
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auditory belt medial part; CGs, cingulate sulcus; Cs, central sulcus; d, dorsal; FST, fundus of
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superior temporal area; F2-f, forelimb region of the dorsal premotor cortical area F2; F5, F7
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ventral and dorsal premotor cortical areas; i, intermediate; Ia, Ig, insula agranular, granular;
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IPs, intraparietal sulcus; 5IPp, the caudal-most intraparietal region of the medial bank; Lf, Lateral Sylvian fissure; LIP, lateral intraparietal; Ls, lunate sulcus; m, medial; MI-f, forelimb
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region of the primary motor cortex; MIP, medial intraparietal; MST, medial superior temporal area; MT, middle temporal extrastriate area V5; p, posterior, PEip, PE intraparietal; PGm, area 7-medial; POs, parieto-occipital sulcus; Ps, principal sulcus; PV, parietal ventral area; r, rostral;
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Ri, retroinsula; SI-f, forelimb region of the primary somatosensory cortex; SII, secondary
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somatosensory cortex; STs, superior temporal sulcus; TPO, temporo-parieto-occipital association area; TPOr and PGa, parts of the superior temporal polysensory area STP; TS1,
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TS2, temporalis superior 1, 2; v, ventral; VIP, ventral intraparietal; VS, ventral somatosensory area; V1, striate visual cortex BA 17; V3d-op (ot), occipito-parietal (occipito-temporal) portion of the extrastriate area V3d. Accumulated data from our original publications Evangeliou et al (2009); Kilintari et al (2011, 2014); Raos et al (2004, 2007, 2014, 2016, 2017).
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Fig. 2 Plots of local cerebral glucose utilization (LCGU) values in the forelimb regions of MI and SI. LCGU values in the experimental monkeys represent average of the right hemispheres (contralateral to the moving forelimb). Each plot represents average LCGU values (lines in color) and 95% confidence intervals (shaded area around lines) per 100 μm along the
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anteroposterior extent of the MI/SI regions of forelimb representation. The length of the plotted cortical field (rostrocaudal extent shown in mm in the abscissa) is illustrated in orange in the
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brain sketch on the left. It includes the primary motor cortex in the frontal convexity (MIconvexity) and within the anterior bank of the central sulcus (MI-bank) as well as the primary somatosensory cortex (SI) in the posterior bank of the sulcus, as indicated by the orange bar
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above the plots. The vertical line, displaying the LCGU values in μmoles/100g/min, is
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positioned at the level of the fundus of the central sulcus. The blue plot displays the average
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LCGU values in the motion control hemispheres (Cm); the green and red plots display the average of LCGU values in the right hemispheres of the observation (Or) and execution (Er)
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groups of monkeys, respectively. The weaker, though statistically significant compared to the motion control, activation in MI-forelimb and SI-forelimb regions for action observation (green plot) may reflect the intended movement and the anticipated somatosensory (ss) consequence
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of the movement, respectively. The stronger activation in the same regions for action execution
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(red plot) reflects the motor command in addition to the intended movement in the MI-forelimb region, and the actual re-afferent feedback from the moving muscles in addition to the
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anticipated somatosensory effect in the SI-forelimb region. Figure modified from Fig.1 of Raos et al (2004) with the addition of the motor effects in the frontal convexity.
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Fig. 3 Plots of percentage LCGU differences along the rostrocaudal extent of the cortical field illustrated by the orange ribbon in the brain drawing above the plots. Red plots illustrate the percent differences between the action execution group of monkeys and the motion-control Cm group (E/Cm). Green plots illustrate the differences between the action
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observation monkeys and the Cm (O/Cm). Plots with solid lines correspond to the right hemispheres (Er, Or) contralaterally to the moving arm. Plots with dotted lines correspond to
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the left ipsilateral hemispheres (El, Ol). Red and green shaded areas indicate 95% confidence
intervals. Baseline corresponds to 0% LCGU difference from the Cm. The plots include the
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forelimb representations in MI and SI cortical areas and the adjacent parietal areas PE and
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PEip, as indicated by the orange bar above the plots and below the brain sketch. Zero
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rostrocaudal extent in the abscissa represents the point of alignment of the horizontal brain
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sections in the fronto-parietal reconstructed maps, i.e., the anterior crown of the Cs. Figure
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modified from Fig.6 of Raos et al (2007) and Fig.9 of Evangeliou et al (2009).
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Table. 1: T values and size of significantly activated clusters compared to baseline activity, in the three tasks, in humans.
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OBSERVATION Size T 6 7.29 12 4.44 29 3.73 10 4.57 6 3.98 15 6.17 25 6.31 20 4.85 55 8.47 40 5.28 56 4.90 6 4.70 9 4.71 21 4.90 169 5.90 165 7.10 125 6.23 154 5.60 83 5.62 59 6.46 204 6.50 380 6.50 15 5.32 25 3.90 27 6.55 40 6.76 18 4.05 21 6.48 30 4.60
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IMAGERY Size T 138 4.70 179 5.13 56 3.60 26 6.30 130 9.75 18 5.70 140 6.82 190 5.17 480 8.12 190 9.03 227 6.60 510 6.70 80 6.80 190 6.20 815 8.84 437 8.95 694 7.35 330 6.55 214 7.37 184 5.55 127 6.40 163 6.40 56 6.68 98 6.68 24 6.50 134 5.55 40 5.54 120 6.00 96 6.20
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EXECUTION Size T 310 13.36 370 6.56 105 5.55 30 7.50 290 9.95 73 5.38 95 6.92 49 6.06 240 10.27 72 4.85 65 4.24 450 6.20 36 6.80 29 5.37 550 6.69 280 6.34 620 9.70 180 5.64 76 5.86 44 5.78 180 7.39 32 7.39 28 5.68 54 6.23 36 5.08 56 5.73 57 8.00 79 6.35 29 6.40
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H L L R L L R L R L L R L L R L R L R L R L R L R L L R R L
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BA 2 3 2/3 40 4 4 6 6 6 6 6 6 8 8 7 7 40 40 37 37 37 37 22 22 19 7 19 37 19
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Brain area SI-Upper Limb SI-Upper Limb SI-Upper Limb SII MI-Upper Limb MI-Upper Limb PMv PMv PMd PMd PMd SMAd-proper MFG MFG SPL SPL IPLd IPLd Poster. MTG Poster. MTG EBA/MT EBA/MT Poster. STG/TPj Poster. STG/TPj Cuneus Poster. Precuneus LG FG MOG
Activated clusters in each task as compared to fixation (p = 0.001 uncorrected). All activations were also significant at FDR p = 0.05. BA: Brodmann area; H: hemisphere; Size: cluster size in
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number of voxels. d: dorsal; EBA: Extrastriate Body Area; FG: fusiform gyrus; IPL: inferior
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parietal lobule; L: left; LG: lingual gyrus; MFG: middle frontal gyrus; MI: primary motor cortex; MOG: middle occipital gyrus; MTG: middle temporal gyrus; MT: Middle Temporal
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cortex; PM: premotor cortex,; R: right; SI: primary somatosensory cortex; SMA: supplementary motor area; SPL: superior parietal lobule; STG: superior temporal gyrus; TPj: temporo-parietal Junction; v: ventral. Table modified from Supplementary Table 1 in Simos et al (2017).
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S0301-0082(18)30190-4 https://doi.org/10.1016/j.pneurobio.2019.01.007 PRONEU 1603
To appear in:
Progress in Neurobiology
Received date: Revised date: Accepted date:
1 November 2018 21 January 2019 28 January 2019
Please cite this article as: Savaki HE, Raos V, Action Perception and Motor Imagery: Mental Practice of Action, Progress in Neurobiology (2019), https://doi.org/10.1016/j.pneurobio.2019.01.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Title: Action Perception and Motor Imagery: Mental Practice of Action
Authors: Helen E. Savaki and Vassilis Raos Department of Basic Sciences, School of Medicine, University of Crete P.O. Box 2208, 71003, Iraklion, Crete, Greece.
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Computational Neuroscience Group, Institute of Applied and Computational Mathematics,
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Foundation for Research and Technology-Hellas, Plastira N 100 str, 71003, Iraklion, Crete,
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Greece.
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Corresponding author: Helen Savaki
71003, Iraklion, Crete, Greece.
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Department of Basic Sciences, School of Medicine, University of Crete P.O. Box 2208,
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tel.: +302810394513, fax: +302810394530, e-mail: [email protected]
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Total number of words in the manuscript, excluding references and figure legends:
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16212
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Hihglights
Mental simulation of actions underlies motor cognition. Conceptual representations are grounded in sensory-motor codes. Perception and mentalization are instantiated by sensory-motor neural activity.
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Abstract
Motor cognition is related to the planning and generation of actions as well as to the recognition and imagination of motor acts. Recently, there is evidence that the motor system participates not only in overt actions but also in mental processes supporting covert actions. Within this framework, we have investigated the cortical areas engaged in execution, observation, and
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imagination of the same action, by the use of the high resolution quantitative 14C-deoxyglucose method in monkeys and by fMRI in humans, throughout the entire primate brain. Our data
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demonstrated that observing or imagining an action excites virtually the same sensory-motor cortical network which supports execution of that same action. In general agreement with the results of five relevant meta-analyses that we discuss extensively, our results imply mental practice, i.e. internal rehearsal of the action including movements and their sensory effects. We
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suggest that we actively perceive and imagine actions by selecting and running off-line restored
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sensory-motor memories, by mentally simulating the actions. We provide empirical evidence
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that mental simulation of actions underlies motor cognition, and conceptual representations are grounded in sensory-motor codes. Motor cognition may, therefore, be embodied and modal.
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Finally, we consider questions regarding agency attribution and the possible causal or
Abbreviations
anterior intraparietal blood-oxygen-level dependent caudomedial area of the auditory belt cingulate motor area extrastriate body area fusiform body area fundus of superior temporal area inferior frontal gyrus inferior parietal lobule local field potentials lingual gyrus middle frontal gyrus primary motor cortex middle part of the superior temporal cortex forelimb region in primary motor cortex middle occipital gyrus medial superior temporal area middle temporal area
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AIP BOLD CM CMA EBA FBA FST IFG IPL LFPs LG MFG MI mid-STS MI-forelimb MOG MST MT
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epiphenomenal role the involved sensory-motor network could play in motor cognition.
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posterior part of the middle temporal gyrus ventral premotor cortex pre-supplementary motor area posterior part of superior temporal gyrus parietal ventral area retroinsula primary somatosensory cortex forelimb region in primary somatosensory cortex secondary somatosensory area supplementary motor area Superior parietal lobule superior temporal polysensory area superior temporal sulcus temporo-parietal junction temporo-parieto-occipital association area ventral intraparietal area ventral somatosensory area
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pMTG PMv preSMA pSTG PV Ri SI SI-forelimb SII SMA SPL STP STS TPj TPO VIP VS
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Keywords: action observation, agency attribution, embodied cognition, mental
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simulation, motor imagery.
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Motor cognition can be studied in the framework of action representation, which is the basis of covert actions, such as action perception and motor imagery. We use the term action representation to refer to an internal cognitive construct that symbolizes an action in the real world. Action perception is often unconscious, automatic, and stimulus-driven. On the other
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hand motor imagery, that is the process by which an individual experiences acting within a mental world, resembles perceptual experience but occurs in the absence of current sensory
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stimulation. It also occurs in the absence of overt motor action, but is conscious and under voluntary control.
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One of the most influential contemporary theories of motor cognition refers to embodied
cognition theory (Rosch et al. 1991), with motor cognitive abilities depending on and achieved by the mechanism of mental simulation (Gordon 1986; Heal 1986), i.e. by mental re-enactment of action execution. The consensus among the embodied cognition theorists is that cognitive processes are deeply grounded in our bodily interactions with the environment (Barsalou 2008a; Bergen and Wheeler 2010; Borghi and Cimatti 2010), and that simulation plays a significant role in embodied cognition (Garbarini and Adenzato 2004; Hostetter and Alibali 3
2008; Bergen and Wheeler 2010). Low-level simulation processes are supposed to be unconscious, automatic and stimulus-driven, whereas high-level simulation processes are typically conscious, under voluntary control, stimulus-independent (Barlassina and Gordon 2017) and guided by imagination (Goldman 2012b). Accordingly, action perception reflects a low-level simulation process, whereas motor imagery reflects a high-level simulation process (Barlassina and Gordon 2017). Interestingly, the embodied cognition theory assumes that both phenomena, i.e. the non-conscious automatic action observation and the conscious effortful
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motor imagery share common representations (Barsalou 2008a; Barlassina and Gordon 2017).
Taking into account the fragmented results of relevant meta-analyses and our
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experimental findings, namely that overt and covert actions share common neural circuits, we
provide empirical support to the embodied cognition theory. We argue that covert actions are embodied because they re-activate the neural circuitry supporting their overt counterparts. We maintain that an overt action is mentally simulated during the practice of its covert equivalent.
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However, the question remains whether simulation is necessary and sufficient for meaning
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construction or simply an epiphenomenon. And if not, what function does it serve, and what
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other processes are involved in making meaning (Bergen 2015)? Hopefully, the use of neurophysiology to support or contradict neuro-psychological and neuro-philosophical notions
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of cognition, such as the grounding of concepts in sensory-motor brain systems, will contribute
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to the advancement of theories of knowledge.
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1. Studying overt and covert actions
1.1 Non-human primates: execution and observation of the same action In the field of investigation of the cognitive sciences, it is known that not only humans but also
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non-human primates attribute mental states to other subjects and have an intentional understanding of their social world (Premack and Woodruff 1978; Flombaum and Santos
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2005). In fact, non-human primates can represent the mental states of conspecifics, and often act according to a manipulative strategy (Byrne and Whiten 1988). In a series of studies during the last fifteen years, we demonstrated that the same occipito-temporo-parieto-frontal cerebral cortical circuits are involved in monkeys executing a reaching-to-grasp action and in monkeys observing another subject who generates the same action (Raos et al. 2004, 2007; Evangeliou et al. 2009; Kilintari et al. 2011, 2014; Raos and Savaki 2016, 2017).
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More specifically, we studied the effects of execution and observation of the same reaching-to-grasp action in the entire cerebral cortex of non-human primates, using a powerful quantitative high resolution imaging technique, the
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C-deoxyglucose (14C-DG) method
(Sokoloff et al. 1977; Savaki et al. 1980). The advantages of this method over fMRI imaging are: (i) the 14C-DG method assesses directly brain activity whereas fMRI estimates changes in brain activity indirectly via alterations of blood oxygenation level, (ii) the
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C-DG is a
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quantitative method measuring local cerebral glucose consumption in mol/100g/min whereas fMRI is semi-quantitative estimating relative differences, (iii) the resolution is much higher with the
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C-DG, using brain sections of 20 microns, and (iv) the
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C-DG allows for
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cytoarchitectonic identification of the activated areas in histological sections adjacent to the
autoradiographic ones. To exclude the potential effects of unspecific arousal, attention, gaze fixation, eye movements due to scanning the object, visual stimulation by the object and by the biological motion of the reaching forelimb, in addition to the fixation-control monkeys, we
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used motion-control monkeys. These monkeys observed an aimless arm movement, similar to
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the reaching component of the experimental paradigm reach-to-grasp. Eye-movements were
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recorded with infrared oculometer, and muscle activity was recorded in the biceps and wrist extensor (Raos et al. 2004, 2007). We reconstructed two-dimensional quantitative functional
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maps, which we normalized geometrically based on the determined cytoarchitectonic borders of cortical areas. This allowed for the generation of averaged maps per condition and for
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quantitative comparison of activations in homologous brain areas. At the level of the central sulcus we found that specifically the forelimb region of the primary motor (MI) and primary somatosensory (SI) cortical areas were significantly activated,
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not only for grasping execution, but also for grasping observation (Raos et al. 2004). Our quantitative
high-resolution
neuroimaging
study,
combined
with
cytoarchitectonic
identification of the activated cortical areas, resolved a conflict in the literature related to the
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involvement of MI in action observation (Grafton et al. 1996; Rizzolatti et al. 1996; Decety et al. 1997; Hari et al. 1998; Iacoboni et al. 1999). This conflict was largely due to the lower
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sensitivity of the techniques employed earlier. Indeed, our high-resolution neuroimaging finding that MI is somatotopically activated during action observation was later confirmed by neurophysiological recordings from single neurons in the MI of monkeys (Tkach et al. 2007; Dushanova and Donoghue 2010; Vigneswaran et al. 2013). Therefore, the specific activation of the forelimb region in MI cortex (MI-forelimb) during observation of a hand-action in our study provided strong evidence for the involvement of MI in action perception, similar to that
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observed during generation of the same action. Our finding that MI is engaged in action perception is compatible with previous reports demonstrating that MI plays a crucial role in the processing of cognitive information related to motor function, such as mental rotation (Georgopoulos et al. 1989) and context recall (Carpenter et al. 1999). Differences in degree and lateralization of MI-forelimb activations between execution and observation are discussed in a following section: attribution of action to the correct agent. Moreover, activation of the forelimb region in the SI cortex (SI-forelimb), during observation of the same hand-action
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performed by another subject, was similar to the re-afferent feedback accompanying its overt
counterpart. Therefore, we thought that there could be a correspondence between action’s
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percept and its stored mental construct, which was acquired during its execution. We thought that movements along with their somatosensory effects may be stored as motor and
somatosensory representations in the primary motor and primary somatosensory cortical areas, respectively, and both of them may be recalled during observation of the action. We suggested
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that, given the absence of any recorded arm muscle activity, the somatotopic activation of MI
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and SI during action observation may involve mental re-enactment of the action by the observer
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(Raos et al. 2004). In the absence of any actual movement and consequently in the absence of any actual re-afferent input, SI-forelimb activation could involve mental rehearsal of previous
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knowledge about the somatosensory effect of the action. In a following study (Raos et al. 2007) we demonstrated conclusively and for the first
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time the extensive overlap of the lateral and medial frontal and cingulate somatosensory-motor cortical circuits supporting action execution and action observation (Fig. 1, common activations colored orange). Virtually the same cortical circuitry was recruited for both
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generation and perception of the same action, including, in addition to MI- and SI-forelimb regions, the premotor areas F2-forelimb and F5, the supplementary somatosensory cortical area SSA, anterior cingulate areas 24d, 24c, 24ab, the medial cortical areas 8m and 9m, and specific
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regions within areas F3/SMA (supplementary motor area) and F6/preSMA as well as regions within cingulate motor areas CMA-dorsal and CMA-ventral (Matelli et al. 1991; He et al. 1995;
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Geyer et al. 2000; Morecraft et al. 2004). Remarkably, the forelimb regions of the primary somatosensory and motor cortical areas were more activated in execution than in observation, and premotor area F7 was activated only for action observation. The latter findings are discussed in detail in following sections. Of course, the overlapping activations for action execution and action observation do not necessarily indicate involvement of the same cell populations in the two conditions. However, the extensive overlap of specific somatosensorymotor areas activated somatotopically for both action execution and action observation further 6
supported our hypothesis that internal re-enactment of action in the observer’s brain/mind may underlie the perception of others’ acts. We suggested that the somatotopic motor and premotor activations elicited by action observation reflect rehearsal of the physical act, whereas the somatotopic primary and supplementary somatosensory activations reflect the recall of learned sensory–motor associations concerning the anticipated somatosensory feedback. In other words when we observe another subject reaching-to-grasp an object, we recruit not only our action-memory related to the intended reaching of the arm and shaping of the hand for efficient
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grasping of the object, but also our perception-memory related to the anticipated somatosensory
(proprioceptive/tactile) consequences of the intended reaching/grasping movement. As if we
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perceive the act while we execute it, and we mentally rehearse the act while we observe it.
Later on we demonstrated, also for the first time, that the overlap of the two cortical networks underlying execution and observation of the same action was also very extensive at the level of the parietal/intraparietal cortex (Evangeliou et al. 2009). It included the superior
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parietal areas PE-lateral and PE-caudal which receive somatosensory afferents (Jones et al.
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1978) and process information about movement kinematics (Kalaska et al. 1990; Ashe and
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Georgopoulos 1994), the inferior parietal area PF which receives somatosensory input and projects to premotor arm-related areas (Petrides and Pandya 1984), the intraparietal areas PEip,
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anterior intraparietal (AIP), and ventral intraparietal (5VIP and 7VIP) (Colby et al. 1993; Matelli et al. 1998), the medial parietal areas PGm/7m (Cavada and Goldman-Rakic 1989) and
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retrosplenial 29/30 (Morris et al. 1999), as well as the parieto-occipital area V6 which contains direction-selective (Galletti et al. 1996) and real-motion (Galletti and Fattori 2003) cells. Consequently, far from being restricted to the medial and lateral somatosensory-motor frontal
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cortical areas, the action observation/execution corresponding system also involves extensive regions of the superior and inferior parietal, lateral, medial and intraparietal cortex associated with sensorimotor and visuomotor processes, operating at the interface of perception, action
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and cognition (Fig. 1). These findings further supported our hypothesis about mental reenactment of the observed action by the onlooker.
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At the level of the lateral sulcus, we examined all the high order somatosensory
association areas (Raos and Savaki 2016). We found that both execution and observation of the same action activated the secondary somatosensory area (SII) and its adjacent somatorecipient parietal ventral area (PV), both characterized by somatotopic organization (Krubitzer et al. 1995). Execution as well as observation activated also the ventral somatosensory area (VS) and the retroinsula (Ri) both of which display a rather systematic representation of the body (Robinson and Burton 1980; Cusick et al. 1989), and the 7
caudomedial area (CM) of the auditory belt which is characterized by convergence of auditory and somatosensory inputs (Fu et al. 2003). These findings complement a previous suggestion that prediction/anticipation during observation may turn motor commands into expected sensory consequences (Kilner et al. 2004; Kilner et al. 2007). Activation of the somatorecipient insular areas not only for action execution but also for action observation, demonstrated for the first time in our study (Fig. 1), further supported our hypothesis that the perception of actions performed by an external agent presupposes knowledge about the action-effect relationships,
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and that understanding others' actions consists of running off-line previously stored sensory-
motor programs. In fact, the recognition process involves correspondence between the
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observed action and past similar experience, implicating perception, memory and decisionmaking. Interestingly, it was reported that neurons in higher order somatosensory areas
combine past and present sensory information for decision-making (Romo et al. 2002; de Lafuente and Romo 2005; Romo and de Lafuente 2013). Remarkably, the gradual increase in
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activity from the primary to the association somatosensory areas that we found during action
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observation, matches the reported gradual build-up of subjective sensory experience across the
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cortical regions of somatosensory hierarchy (de Lafuente and Romo 2006). Therefore we suggested that the activation of lower and higher order somatosensory areas for action
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observation indicates that the somesthetic consequences of movements, which are generated in a bottom-up manner during action execution, are also triggered in a top-down manner during
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action observation to represent the predicted sensory consequences of the perceived movement. Having demonstrated that the motor cognitive process associated with action-observation involves a strong somatosensory component, characterized by the somatotopic activation of
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the primary somatosensory SI cortex (Raos et al. 2004), the supplementary somatosensory cortex SSA (Raos et al. 2007), the secondary somatosensory cortex SII and several somatorecipient parietal (Evangeliou et al. 2009) and insular (Raos and Savaki 2016) cortical
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areas, we examined whether this process also involves a visual component. To this end we studied the effects of action execution and observation throughout the striate and extrastriate
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cortex of the occipital operculum, lunate, and inferior occipital sulci (Kilintari et al. 2011). We found that the extrastriate cortical visual areas V3A and V3d-occipitotemporal (component of the ventral visual stream) which are associated with central vision, as well as area V3doccipitoparietal (part of the dorsal visual stream) associated with peripheral vision (Baizer et al. 1991) were activated for both motor conditions (Fig. 1). Interestingly, area V3 is known to process visual information related to the requirements of the motor system to control the action. Its activation in our study could not be attributed to differences in oculomotor behaviour 8
(Galletti and Battaglini 1989) because the monkeys were fixating throughout the task. Given that area V3 is known to represent the 3D-object to be reached and manipulated as well as its spatial location (Galletti and Battaglini 1989; Tsao et al. 2003 Konen and Kastner 2008; Arcaro and Kastner 2015), we suggested that its activation indicates that both action generation and action observation utilize information about visuospatial-related and object-related features of the object to be reached and grasped. Of note is that this area in monkeys was suggested to correspond to the human extrastriate body area (EBA) which is associated with functions
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similar to those ascribed to monkey V3, such as limb movements to visual targets, mental simulation of goal-directed movements (Astavief et al. 2004) and processing of visual features
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of action representations (de Lange et al. 2005; Milton et al. 2007; Bakker et al. 2008). In fact,
visuospatial information required for reaching and 3D object-related information useful for grasping may be relayed from the activated area V3, via its parietal projections (Andersen et al. 1990; Felleman and Van Essen 1991; Passarelli et al. 2011) which were found to be activated
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for both motor conditions in one of our earlier studies (Evangeliou et al. 2009), to the
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premotor/motor cortical areas (Petrides and Pandya 1984; Matelli et al. 1998; Petrides and
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Pandya 1999; Gamberini et al. 2009) which were also activated for both execution and observation in one of our previous studies (Raos et al. 2007). Accordingly, we proposed that a
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single visuo-motor neural system represents both physical and mental practice. It is recruited in a bottom-up manner when sensory driven, and in a top-down manner when mentally driven,
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presumably generated by a forward model (Wolpert and Ghahramani 2000). This sensorymotor system extends beyond the parietofrontal somatosensory-motor which we described earlier, to include visual in addition to the somatosensory information concerning the action. It
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reflects the physical as well as the mental visuomotor representation of the act, providing further support to a process-driven (simulation like) rather than a theory-driven mechanism underlying action perception.
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At the level of the temporal cortex, all the main components of the motion complex (MT-
dorsal and ventral, MST and FST) were activated for action observation, but not for action
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execution when compared to the motion-control (Kilintari et al. 2014). In contrast, both execution and observation of the same action were found to engage the superior temporal polysensory area (STP), including the temporo-parieto-occipital association area TPO-rostral and the PGa, as well as area TS2 (Fig. 1). Further support to our suggestion, that activation of certain temporal cortical areas for action observation reflects covert rehearsal of the observed action by the onlooker, is provided by previous reports (Goebel et al. 1998; Saygin et al. 2010). Interestingly the STP, which is implicated in visual and somatosensory processing and its role 9
in expectation has been emphasized (Mistlin and Perrett 1990), was much more activated for observation than for execution in our study (Kilintari et al. 2014). This finding agrees well with neurophysiological data demonstrating that neurons in area STSa/STP respond to the perception of movements made by body parts of other subjects rather than by the monkey’s own hand (Perrett et al. 1985b; Perrett et al. 1989; Hietanen and Perrett 1993; Jellema et al. 2000; Keysers and Perrett 2004). Also, this finding indicates greater involvement of a high order polysensory area in action observation than in execution, in contrast to the smaller
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involvement of lower order cortical areas such as the primary sensory-motor cortex discussed above. The latter differences indicate stronger involvement of association compared to primary
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cortical regions in observation compared to execution.
Finally, we analyzed the lateral prefrontal cortex consisting of a dorsolateral component [areas 8, 9, 10, 46d, and 9/46-dorsal (9/46d) (Petrides and Pandya 1999)] and a ventrolateral constituent [areas 9/46-ventral (9/46v), 45, and dorsal 47/12 (Petrides and Pandya 2002)] as
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well as the orbitofrontal cortex including areas 10, 11, 47/12 and 13 (Barbas 2007; Petrides
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2007). We found that execution and observation activated in common most of the lateral
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prefrontal and orbitofrontal cortical areas examined: 8-convexity and 8-bank, 9-lateral, 46dorsal, 46-ventral, 46-principal both upper and lower banks, 47/12-dorsal, 47/12-ventral, 45-
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convexity, 45-bank and area 10 (Raos and Savaki 2017). Interestingly, most of these areas have been involved in on-line monitoring and manipulating visual information related to actions
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(Petrides 1994). Moreover, activation of the supramodal frontopolar area 10 for both execution and observation is compatible with its involvement in coordinating attention between externally presented and internally represented information (Burgess et al. 2009 ; Medalla and
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Barbas 2014). All the above results indicate that the prefrontal cortex integrates information in the service of both action generation and action perception. Markedly, area 9/46v was activated exclusively for execution whereas 9/46d was activated only for observation (Fig. 1, execution
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and observation effect in red and green respectively). The latter finding could provide an explanation to the reason why activation of the motor system during observation of an action
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does not result in overt movements. This explanation has as follows: It is known that the dorsal part of the dorsolateral prefrontal cortex projects to the dorsal premotor cortex (Matelli et al. 1986; Barbas and Pandya 1987; Pandya and Yeterian 1996) and that its F7-projection zone occupies the broadest territory (Wang et al. 2002). As mentioned above, the dorsal premotor area F7 was also exclusively activated for action observation but not for action execution (Raos et al. 2007). Interestingly, the rostral part of the premotor cortex, including area F7, is associated with the selection of particular movements between alternatives based on pre10
learned conditional rules (Petrides 1987; 1997). It is also known that area F7 has an inhibitory effect (Moll and Kuypers 1977; Sawaguchi et al. 1996) and reaches the spinal cord both directly and indirectly (Keizer and Kuypers 1989). In our study on the effects of action execution and observation in the spinal cord, we demonstrated that observation decreases activity bilaterally in the spinal section representing the forelimb (the cervical enlargement) whereas execution activates the same area ipsilaterally to the moving arm (Stamos et al. 2010). We then suggested that the depression of activity in the spinal segment of forelimb representation for action
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observation could explain the suppression of its particular overt equivalent, despite the activation of the observer’s primary motor cortex. In other words, our results taken together
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indicate that, area 9/46d which is activated exclusively for action observation (Raos and Savaki
2017) may excite area F7 which is also exclusively activated for action observation (Raos et al. 2007) and the latter may inhibit the spinal cord which is exclusively depressed for action observation (Stamos et al. 2010), explaining why execution of the specific observed movement
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is prevented although the motor system in the observer’s brain is activated (Savaki 2010).
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In summary, with our experiments in non-human primates we established that the brain
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system engaged in action execution by an acting monkey is re-employed by a monkey merely observing the same action. This system includes frontal premotor and motor, parieto-temporal
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somatosensory, and temporo-occipital visual cortical regions as well as prefrontal and occipitoparieto-temporal association areas (Fig. 1). Apparently, an action such as reaching-to-grasp an
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object with one hand does not simply involve perception of the object and generation of the correct movement. It also involves memory related to the object’s features and affordances, to the planned arm movement for successful reaching, to the intended shaping of the hand for
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efficient grasping, to the visual guidance of reaching/grasping, and to the anticipated somatosensory consequences of the intended action. All our results taken together indicate that both generation and perception of an action activate similar motor, somatosensory, visual and
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association networks, which presumably recruit shared contextual representations. Perception of an action performed by another subject may trigger the effects of our previous knowledge
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about the act and its predicted consequences. Therefore, action perception may correspond to simulation of its overt counterpart. We may decode the actions of others by activating our own action system. We may understand observed actions by performing them mentally. We may learn how to act and how to recognize others’ actions by acquiring movement-effect associations. And this may be the neuronal basis of observational learning. Activation of the neural circuitry supporting action execution, during observation of the same action performed by another subject, could facilitate its activation during following performances. In other 11
words, simulation of the action during its observation could facilitate its subsequent execution, suggesting that motor skills can be learned by observation. Indeed, subjects merely watching an experimenter performing a task can learn to execute it as efficiently as after practicing the task themselves (Heyes and Foster 2002). Based on our results, we developed a model exhibiting learning from observation, with generalization capacities and interesting predictions regarding the mechanisms employed for observation in the cortex (Hourdakis et al. 2011).
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1.2 Human studies on overt and covert actions
In this section we first report the results of five meta-analyses of human neuroimaging data
concerning the cortical areas engaged in action generation, action perception and motor
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imagery, investigated mostly in isolation from each other, and we discuss their strengths and limitations. Then we present the results of two human fMRI studies, which are the only ones to our knoweledge investigating the whole brain in all three conditions of execution,
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observation and imagination of the very same hand action in the very same subjects, therefore
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overcoming most of the limitations of the meta-analyses.
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1.2.1 Meta-analyses
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In a descriptive meta-analysis of neuroimaging data concerning the cortical areas engaged in action generation, action perception and 1st person motor imagery, Grezes and
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Decety state that there is evidence of “functional equivalence” among these three motor conditions, indicating shared motor representations (Grezes and Decety 2001). The focus of their meta-analysis was to evaluate the extent of overlap of the activations elicited by overt and covert actions in several different studies. In Table I, the authors list 22 studies used for their
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analysis. These include 8 execution, 6 imagination and 8 observation studies, using a variety of tasks of different goal-directed hand movements. Specifically, only 6 of them used the same
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task to study either execution and observation (3 studies) or observation and imagination (3). Of course pooling of data from different studies which use a variety of experimental designs,
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motor conditions, tasks, subjects and instructions is problematic, because different types of tasks and goals may involve different cortical areas. Nevertheless, this meta-analysis demonstrated that there is a good overlap between action execution, observation and imagination in the supplementary motor area, the dorsal premotor cortex, the supramarginal gyrus, the superior parietal lobe and the superior occipital gyrus, i.e. in areas extending well beyond the two ones containing mirror neurons. Interestingly, this meta-analysis revealed that motor imagery recruits the primary motor cortex, while both imagery and observation recruit 12
the pre-SMA and the dorsolateral frontal gyrus. Overall, the authors conclude that the overlapping activations for action execution, observation and imagination, across studies, support a functional equivalence between overt and covert actions. However, this equivalence is not strict if we take into consideration some scattered non-overlapping foci associated with the three motor conditions across the analyzed studies (Grezes and Decety 2001). In a meta-analysis of 139 fMRI/PET experiments, using the method of activation likelihood estimation, Caspers et al identified brain areas consistently activated for action
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observation and imitation (Caspers et al. 2010). They revealed a largely bilateral network, including areas in the prefrontal, premotor, parietal, temporal and occipital cortex, activated
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for both action observation and imitation. Additional sub-analyses for different effectors (body part involved in the movement) revealed a comparable brain network to that of the general analysis across all experiments. More specifically, brain regions consistently activated across the 104 experiments using tasks of action observation (including different effectors and
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instructions) were symmetrically distributed in both hemispheres within frontal areas BA
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44/45, lateral dorsal premotor cortex (BA 6), supplementary motor area (SMA), rostral inferior
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parietal lobule (area PFt), primary somatosensory cortex (BA 1/2), superior parietal (area 7A), intraparietal cortex, posterior middle temporal gyrus at the transition to visual area V5, and
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fusiform face area/fusiform body area (Caspers et al. 2010). Brain regions activated across the 35 action imitation experiments included bilateral BA 44, BA 6, BA2, SMA, area PFt and
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visual area V5. Interestingly, the authors emphasize their finding that both action observation and imitation were associated with robust activation of the primary somatosensory cortex, while the primary motor cortex was recruited for action observation only when participants
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viewed actions with the intention to imitate them. They also remark that pooling of data from very different studies could have deleted effects related to local somatotopy. They conclude that the revealed common network for observation and imitation of actions reaches far beyond
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the ‘classical’ mirror neuron areas in ventral premotor and inferior parietal cortex (Caspers et al. 2010).
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In another meta-analysis, using the method of activation likelihood estimation,
Molenberghs et al provided a quantitative index of the consistency of patterns of activity measured in 125 human fMRI studies of action observation or action execution, in which the authors attributed their findings to mirror neuron functioning (Molenberghs et al. 2012). They revealed an extensive network of brain regions, with significant clusters of activation encroaching upon 34 separate Brodmann areas. More specifically, the significant clusters attributed to mirror neuron functioning, which were revealed by this meta-analysis, encompass 13
not only the classical mirror neuron areas based on monkey recordings, i.e., the inferior parietal lobule and inferior frontal gyrus, but also the middle frontal, dorsal premotor cortex, primary somatosensory cortex (BA 3), cingulate gyrus, superior parietal cortex, precuneus, insula, superior, middle and inferior temporal gyri, primary visual cortex, cerebellum and parts of the limbic system. The authors of the meta-analysis state that, despite the claims of some authors of the human fMRI studies, it is unlikely that regions such as the early visual cortex and superior temporal cortex possess mirror properties since we know that they do not exhibit
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motor-related activity. Interestingly, they also state that very few of these fMRI studies included both an “observe” and a corresponding “execute” condition, although only neurons
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responding under both conditions are considered to be mirror neurons. Sub-analyses in which (i) participants listened to action sounds, revealed additional activation clusters in and around the primary auditory cortex, (ii) participants watched an actor receiving somatosensory stimulation, revealed additional activation of the primary somatosensory cortex, and (iii)
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participants executed and/or observed expressions of emotion, revealed consistent activation
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of regions known to be involved in emotion processing, including the amygdala, insula and
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cingulate gyrus. The authors conclude that overlapping brain regions are activated in human participants when observing or executing certain actions, through simulation, and that the
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precise regions involved depend on the modality of the task (Molenberghs et al. 2012). The first quantitative cortical map of motor imagery was provided by Hetu et al, who
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combined the data from 75 fMRI and PET studies in an activation likelihood estimation metaanalysis (Hetu et al. 2013). The authors found that motor imagery consistently recruited a large cortical network as well as subcortical and cerebellar regions. With additional sub-analyses
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they found that the brain regions activated during motor imagery depend on the type of movement to be imagined (simple or complex), the imagery modality (kinesthetic or visual), the effector (body part involved in the movement) and the instructions given to the participants.
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Sub-analysis of 22 experiments including the total of 280 subjects performing tasks with transitive upper-limb movements revealed consistent activations, mostly bilateral, in the
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inferior frontal gyrus, precentral gyrus, postcentral gyrus (primary somatosensory cortex), middle frontal gyrus, SMA, middle cingulate cortex, anterior insula, superior and inferior parietal cortex, supramarginal gyrus and precuneus. Consistent activations were also found in the cerebellum, putamen and pallidum. In contrast to the meta-analysis of Grezes and Decety ( 2001), they did not find consistent involvement of the primary motor cortex. They argue that individual differences could explain why primary motor cortex activity was not consistently found. The authors also note that because they did not include studies that focused on action 14
execution per se in their meta-analyses, they could not directly compare the networks supporting motor imagery and motor execution. However, they conclude that several cortical areas known to play a role in actual motor behaviour were consistently engaged in motor imagery. They suggest that motor imagery recruits the system underlying actual movements, supporting the view that motor imagery and motor execution are very similar processes (Hetu et al. 2013). Recently, Hardwick et al conducted large scale activation likelihood estimation meta-
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analyses to identify the individual networks involved in motor imagery, action observation,
and movement execution. Moreover they provided quantitative comparison among these three
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networks (Hardwick et al.2018). They summarized data from 303 motor imagery, 595 action
observation and 142 movement execution experiments. They revealed that motor imagery and action observation recruited similar cortical networks. In detail, the action execution network included activations in the primary motor and somatosensory cortex, dorsal and ventral
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premotor cortex, supplementary (SMA) and cingulate (CMA) motor areas, inferior frontal
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gyrus (IFG), inferior parietal lobule (IPL), thalamus, putamen, and cerebellum. The action
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observation network, which was largely bilateral, included activations in the dorsal and ventral premotor cortex, pre-SMA, IFG, insula, IPL and SPL, temporal, fusiform and occipital gyri,
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but not in subcortical regions. Finally, the motor imagery network, also largely bilateral, included activations in the primary somatosensory cortex, dorsal and ventral premotor cortex,
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SMA and pre-SMA, CMA, IFG, dorsolateral prefrontal cortex, SPL and IPL, insula, basal ganglia, and cerebellum. Interestingly, conjunction analysis across all three tasks identified a consistent premotor, parietal, and somatosensory network of brain areas recruited across all
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conditions of motor imagery, action observation, and movement execution. The authors suggest that the smaller portion of the parietal cortex involved in the action execution network, compared to the larger portion involved in the motor imagery and action observation networks,
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may be due to the simplicity of motor tasks employed in the execution conditions compared to more complex tasks employed in the observation and imagery conditions. They also suggest
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that the consistent recruitment of the somatosensory cortex in all three conditions indicates that simulation of the action during its observation and imagery may implicate a sensory efference signal similar to that induced by execution of the action. Finally they suggest that the common network consistently activated in the three analyzed motor conditions does not necessarily reflect “functional equivalence”, and that a mixture of matched and unique processes may occur in regions activated in common during performance of overt and covert actions.
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Meta-analyses have certain strengths but also several limitations. Meta-analyses overcome the sample-inadequacy of individual neuroimaging studies typically involving only 12-20 subjects, by pooling data from several publications and consequently allowing thousands of subjects to participate. Also, meta-analyses provide quantitative results outstripping simple review articles. However meta-analyses face a number of limitations. For example, a serious concern is related to the variety of motor tasks examined in the numerous neuroimaging studies used in the meta-analyses presented above. These tasks range from very simple movements to
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complex actions and from non-goal directed movements to actions involving an object. As already mentioned, over the last two decades overt and covert actions in humans were
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examined separately from each other. Indeed, most of the available studies did not use all three action conditions; they did not require the participant to imagine, observe and execute the same action. In effect the problem is much bigger if we take into account the fact that simple actions were often used to study execution conditions (due to practical restrictions in the magnet)
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whereas more complex actions were used to study overt conditions. Moreover, beyond the
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nature of the task, the number, age, and gender of the participants, another important variable
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which could influence the results of meta-analyses is the type of instruction given to the participants in each one of the included studies. For example, the fact that the primary motor
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cortex was recruited during motor imagery in one meta-analysis (Grezes and Decety 2001) but not consistently activated in another one (Hetu et al. 2013) may be due to performance of either
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kinesthetic or visual imagery by the participants, depending on the instruction given by the experimenter. Also, coordinate-based meta-analyses use reported peak activations for the analysis, discarding further spatial information from the original studies. Therefore, pooling of
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data from very different studies diminishes or even deletes results related to somatotopy. Finally, meta-analyses using activation likelihood estimation reveal only the consistency of activations across studies while no information is provided about the strength of activations. In
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summary, the results of any given neuroimaging experiment used in a meta-analysis are influenced by various parameters specific for each study, including the experimental design,
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the implementation of the paradigm, the task requirements, the number age and gender of the participants as well as the analysis of the data. Meta-analyses cannot possibly take into account all these variables of the studies they include. This is the reason why in the following section we extensively refer to two human fMRI studies, which are the only ones to our knowledge investigating the whole brain in all three conditions of execution, observation and imagination of the very same motor task (hand action) in the very same subjects. The general conclusion provided by the five meta-analyses presented above (Grezes and Decety 2001; Caspers et al. 16
2010; Molenberghs et al. 2012; Hetu et al. 2013; Hardwick et al.2018) is that the areas involved in action perception and motor imagery extend well beyond the two classical mirror neuron regions, and therefore a process broader than mirroring is responsible for motor cognition. This conclusion is in agreement with our findings in the monkey studies. Moreover, our high resolution
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C-deoxyglucose studies provide further solid, detailed, somatotopic information
concerning the substratum of a covert hand action (action execution) and its overt equivalent
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(action observation).
1.2.2 Studying an overt action and its covert equivalents
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As already mentioned, over the last two decades overt and covert actions were investigated in
isolation from each other in humans, comparing only two types of conditions at a time, e.g. execution with observation of hand actions, or observation with
imagery, or
execution/imitation with imagery of hand actions. This is demonstrated by the fact that out of
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335 papers cited in two recent meta-analyses of action observation (Caspers et al. 2010) and
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motor imagery (Hetu et al. 2013) only 18 are cited in both (Vogt et al. 2013). However, only a
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study of activations induced by execution, observation and imagination of the very same hand action, across the entire human brain of the same subjects would allow for direct comparison
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of effects induced by all three motor conditions. To our knowledge, two studies in the literature fulfill these requirements. First, in an fMRI study Filimon et al investigated the effects of
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execution, observation and imagination of the same visually guided arm-reaching task in the entire human cortex of 14 subjects (Filimon et al. 2007). Second, we recently used fMRI to assess areas activated for an overt action and its covert equivalents throughout the whole human
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brain without any prior assumption of areas expected to be activated (Simos et al. 2017). We asked 21 participants to execute, observe and imagine (1st person motor imagery) a visually guided tracing task with the index of the right hand. Using human subjects we could search the
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effects of motor imagery reflecting high-level simulation process, in addition to the action perception reflecting low level simulation process (Barlassina and Gordon 2017) that we
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studied using monkeys. Needless to say that action imagination is of utmost importance because it allows one to generate sensory-motor images and employ them in a variety of motor tasks, to plan and recall personal experiences as well as to actively understand what someone else is experiencing. As already mentioned, all relevant meta-analyses include studies using a variety of tasks, subjects, and motor conditions. Given that different types of tasks and goals may involve different cortical areas, we would expect some effects to be washed out in the meta-analyses, 17
which therefore would provide fewer overlapping brain activations between overt and covert actions compared to the two studies presented above (Filimon et al. 2007; Simos et al. 2017). Moreover, given that a re-analysis of the Filimon et al data, eight years after the original publication, revealed more cortical areas activated by the covert actions and more overlapping activations between the overt action and its covert counterparts (Filimon et al. 2015), and also given that their task was simply reaching to visual targets whereas ours was a visually guided tracing task, we would expect our study to provide more activations and most probably more
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overlaps between the overt action and its covert equivalents. Indeed, we found that several brain areas were activated in common across all three motor conditions (Table 1). These areas
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included the upper limb representation of the primary motor and somatosensory cortical areas, the dorsal and ventral premotor cortex, the supplementary motor cortex (SMA-proper), BA 7
in the superior and BA 40 in the inferior parietal cortex, BA 8 in the middle frontal gyrus (MFG), BA 22 in the posterior part of superior temporal gyrus (pSTG) including the temporo-
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parietal junction (TPj), BA 37 in the posterior part of the middle temporal gyrus (pMTG)
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including the extrastriate body area (EBA), the extrastriate visual BA 19 in the cuneus, the
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lingual gyrus (LG) and the middle occipital gyrus (MOG), BA 7 in the posterior precuneus and BA 37 in the fusiform gyrus [see Supplementary Materials in (Simos et al. 2017)].
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Interestingly, the activations in MI- and SI-upper limb regions were contralateral to the moving arm and more intense and extensive for the overt compared to its covert actions, whereas
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parietal and temporal activations were more extensive bilaterally for the covert actions compared to their overt counterpart. The SI- and MI-upper limb activations indicate that sensory-motor consciousness during covert actions is tied to the primary somatosensory and
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primary motor cortical areas rather than only to high order non-sensory neural populations. These effects in humans resemble the action-execution/observation results in monkeys. They are also in considerable agreement with most of the results in the Filimon et al study (Filimon
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et al. 2007) and its subsequent re-analysis/re-evaluation (Filimon et al. 2015), with the exception of areas MI and SI which remained unaffected with their original univariate fMRI
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analyses in 2007 and were not re-analyzed by multi-voxel pattern analysis in 2015. Remarkably, our finding in both humans and non-human primates that the upper
limb/forelimb region in SI is less activated for observation than for execution of the same action, does not support the idea of predictive coding via neural suppression (Blakemore et al. 2000). Explicitly, it was suggested that if an action is efficient and no adjustment is needed, the motor system does not need to pay attention to the sensory feedback and therefore the somatic consequence of the movement is suppressed. In contrast, if there is an error in the 18
prediction, the sensory system is disinhibited and sends a robust feedback signal. In other words, strong somatic feedback means wrong prediction (error detection) whereas weak feedback means correct prediction. It was thought that predictive coding via neural suppression provides an answer to why we cannot tickle ourselves: our brain predicts the sensory consequences of our movement thus decreasing their perceptual impact, but can’t predict others’ movements so they are more intense perceptually (Blakemore et al. 2000). In contrast to this suggestion, we have demonstrated that the correct prediction which occurs during action
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execution elicits strong activation of the somatosensory cortex, and the deviation from somatic prediction which occurs during observation of others’ movements elicits weak SI activation.
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The activation of both the superior and inferior parietal cortex for action perception,
demonstrated in both studies (Filimon et al. 2015; Simos et al. 2017), is compatible with the established knowledge that the parietal lobule is a bridge between perception and action, with neurons involved in higher order sensorimotor integration during hand manipulation tasks
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(Mountcastle et al. 1975). It is also in agreement with studies reporting that patients with
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parietal lesions are unable to recognize the meaning of gestures (Heilman et al. 1982; Sirigu et
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al. 1995; Sirigu et al. 1996). These results are also consistent with previous reports that the superior parietal lobule is involved in monitoring internal motor signals and predicting the
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sensory consequences of movements (Desmurget et al. 1999), while the inferior parietal lobule is engaged in awareness of our actions (Desmurget et al. 2009) and conscious motor intention
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such as the subjective experience of ‘wanting to move’ (Desmurget and Sirigu 2012). Activation of the multimodal pMTG by all three motor tasks (Simos et al. 2017) is compatible with reports that this area carries high level representations (Kreiman et al. 2000; Quiroga et
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al. 2005; Cattaneo et al. 2010; Urgesi et al. 2014; Kemmerer 2015), and complements a previous study implicating this region in conceptual action representations (Kable et al. 2005). Activation of the EBA in all three motor conditions complements studies reporting its
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involvement in executed, observed and imagined body-part movements (Astavief et al. 2004; Arzy et al. 2006; Hamilton et al. 2006; Filimon et al. 2007; Gazzola and Keysers 2009).
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Actually, the EBA was selectively activated in contrast to the fusiform body area (FBA) which remained unaffected in our study, presumably because the former is known to respond to local body-parts whereas the latter to holistic images of the body (Taylor et al. 2007). Although initially the prevailing view was that the human EBA is a distinct visual area which overlaps substantially with hMT (Downing et al. 2007; Peelen and Downing 2007), later on more than one limb-selective regions were reported in both humans and monkeys. These regions are organized around EBA/hMT in humans and spatially match those in FST and STP in monkeys 19
(Weiner and Grill-Spector 2011). The latter description, in association with the reported functional analogy of human EBA with monkey area V3, shows that human results match very well our findings in monkeys (see previous section). Moreover, activation of the TPj for all three conditions agrees well with studies reporting involvement of this area in perception and imagination of actions (Ruby and Decety 2001; Farrer and Frith 2002; Decety and Sommerville 2003; Vogeley and Fink 2003; Blanke et al. 2005). Once again, this findings in humans match those in monkeys, because the area activated in the middle part of the superior temporal cortex
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(mid-STS) of monkeys has very similar connectivity profile with human TPj, and both these areas are associated with predicting the behaviour of others (Behrens et al. 2008; Mars et al.
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2013). Actually, human TPj was suggested to have evolved from an expansion and further
specialization of monkey mid-STS (Mars et al. 2013). Given the 25 million years of separate evolution across the old world monkey and the hominoid species (Stevens et al. 2013), it is not surprising that we are not absolutely certain about homologies of human and monkey brain
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areas. Finally, in addition to the above areas activated for all three motor conditions,
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conjunction analysis also revealed a set of areas activated in common only for execution and
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observation, but not for imagery (Simos et al. 2017). These areas included the right SI, left SII, right EBA/MT, and left posterior precuneus (BA7).
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The finding that fewer areas, overall, were found active in humans than in monkeys during action observation can be explained by the facts that (i) the fMRI has a much lower 14
C-DG method which we applied on monkeys, (ii) histological
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resolution than the
identification of activated areas was not feasible with the fMRI imaging, and therefore cortical areas could not be subdivided as were with the 14C-DG method, and (iii) the fMRI measures
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blood flow rather than neural activity. At this point we should note that a strong correlation between blood-oxygen-level dependent (BOLD) signals and local field potentials (LFPs), and a slightly weaker correlation between BOLD and multi-unit activity was initially reported
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(Logothetis et al. 2001; Logothetis and Wandell 2004), whereas later on weaker correlations even between BOLD and LFPs in the visual cortex of awake behaving monkeys were
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demonstrated (Goense and Logothetis 2008; Logothetis 2008). Also, uncoupling of neural activity and BOLD has often been reported (Ekstrom 2010), and differences in the neurovascular relationship have recently been demonstrated (Hall et al. 2016). Despite the above mentioned problems associated with fMRI studies, our results in monkeys for execution and observation of the same action were very similar to those in humans. Altogether, they demonstrate that action perception recruits the full array of somatosensory, visual and motor cortical circuits which support action execution in both monkeys and humans. 20
Not only action observation which reflects a low-level simulation process, but also motor imagery reflecting a high-level simulation process shares with its equivalent overt action common brain circuits, and presumably similar contextual representations (Filimon et al. 2015; Simos et al. 2017). In addition to the areas activated for all three motor conditions described above, conjunction analysis revealed a set of areas activated in common only for execution and imagery but not for observation (Simos et al. 2017). These areas included SMA-proper, cingulate cortex and BA22 bilaterally, the right/ipsilateral prefrontal cortical area 9/46d, the
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left/contralateral parietal BAs 5, 7, 40, and BA 37 in the fusiform gyrus. Overall, the results on motor imagery demonstrate that virtually the same cortical circuits support the execution and
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imagination of the same action in humans. Therefore if we imagine an action, its subsequent
execution should be facilitated, and this way motor skills can be learned not only by observation, as mentioned earlier, but also by mental training (Ridderinkhof and Brass 2015; Eaves et al. 2016). In fact, because they take place without any physical exercise, observational
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learning and mental training may turn out to be of great social value.
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Finally, we used the Network Based Statistic and Graph theory modeling (Bullmore and
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Sporns 2009; Rubinov and Sporns 2010; Zalesky et al. 2010) to perform functional connectivity analyses, in order to assess the relative importance (connection strength) of
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individual regions for information flow within each task-related network (Simos et al. 2017). Connectivity analyses corroborated the notion that a common sensory-motor fronto-parieto-
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temporal cortical network is engaged for execution, observation, and imagination of the very same action. Cases of divergence in connectivity targets of the areas activated in common in the three motor conditions are discussed in the action-attribution section below. We thought
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that all the above reported findings taken together could not be linked into a unified system of motor cognition other than that of the mental simulation of action (Jeannerod 2001; Savaki 2010). Our suggestion that mental simulation of the physical action takes place during action
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imagination and observation is also supported by reports, such as, that imagined actions are characterized by the same temporal regularities and responsiveness to physical laws as their
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overt equivalents (Decety et al. 1989; Sirigu et al. 1995), and that simulation of the kinematic characteristics of a movement occurs during its observation (Agosta et al. 2016). In summary, in the absence of any other convincing scheme, the mental simulation theory provides a reasonable interpretation of the available human and monkey data. This proposal of ours will be further discussed in a following section in relation to an influential contemporary aspect of motor cognition, the mirror neuron account. Also, the overlapping brain machinery involved in (i) action execution, (ii) the non-conscious automatic action observation, and (iii) 21
the conscious effortful motor imagery indicates that mental concepts may be generated by sensory-motor cognitive primitives, supporting embodied theories of motor cognition. This subject will also be presented analytically in a following section.
2. Attribution of action to the correct agent
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2.1 Non-human primates
In the previous section we demonstrated that to understand the actions of others is to recruit
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the entire action execution system including somatosensory, visual and motor representations of the action. Apparently, these shared representations, serving action execution and recognition, are self-relative but agent-neutral. The question remains why we do not confuse
others’ actions with ours, given that the same neuronal network is assigned the double duty of
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execution and observation of the same action. In other words, given that the same sensory-
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motor neuronal circuits are recruited in both motor conditions, how do we attribute the action
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to the correct agent, i.e. how do we attribute the action to the self during action execution and to the other during action observation? How do we distinguish between self-produced and
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externally-generated actions? How do we distinguish between actor and spectator, between self and other? Presumably, there must be self-referential agent-specific representations, i.e.
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representations that the actor does not share with the spectator, which serve the attribution of action to the correct agent. Here, we will present examples of the potential neural substrate and mechanism encoding agent-specific effects, based on the results of our studies in monkeys.
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First, the qualitative similarity between the execution and observation masks quantitative differences in the intensity of activation of different relays, differences which could be revealed only by a powerful quantitative imaging method such as the 14C-DG that we
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applied on monkeys. For example, although cortical areas MI-forelimb and SI-forelimb were activated in both motor conditions, these activations were weaker by 50% for action
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observation than for action-execution (Raos et al. 2004, 2007). We suggested that the stronger activation of MI-forelimb for action execution reflects the intended movement (input from the premotor cortex) and the actual motor command (MI neuronal activity), whereas the 50% weaker activation for action observation may reflect the intended movement alone (Fig. 2). Indeed, during action observation the command component is absent and therefore there is no overt movement. Consequently a sense of agency as well as a sense of reality versus simulation may be embedded in MI, which in fact was suggested to compute sensory information for the 22
upcoming movement (Georgopoulos 2000). Likewise, the stronger activation of SI-forelimb region for action execution reflects the predicted/anticipated somatosensory consequence of the movement (based on efferent copy from MI) and the actual re-afferent feedback (signal from the contracting muscles), whereas the 50% weaker activation for action observation may reflect the predicted consequence of the movement, alone, because of the lack of somatosensory feedback due to the absence of overt movement (Fig. 2). It may be that, when the predicted somatosensory signal matches the actual feedback from the periphery, we know
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that we are generating the action. Also, when there is a mismatch between the predicted
somatosensory signal (present) and the actual somatosensory feedback (absent), we recognize
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that we are observing an externally generated action. Consequently, the sense of agency may
at least partially derive from comparison of unimodal signals in primary sensory and motor cortical areas, reflecting (i) the intention to move and the movement command in MI, and (ii) the anticipated/predicted sensory feedback of movement and its actual sensory re-afferent
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feedback in SI. In a similar vein, and in accordance with previous suggestions (Wolpert et al.
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1995; Blakemore et al. 1998; Tsakiris et al. 2007; Apps and Tsakiris 2014), the weaker
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activations for observation compared to execution that we recorded in the parietal and insular somatorecipient areas may reflect the incongruity between the predicted/simulated somatic
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end-result of the action and its actual afferent somatic feedback, and may also contribute to the attribution of action to the external agent. In summary, the parameter of the sense of agency
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may be articulated in the degree (strength/weakness) of activations, within and throughout the distributed somatosensory-motor network activated in common for execution and observation of the same action. In this case, the degree of activation reflects the integration of bottom-up
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exteroceptive and top-down interoceptive signals. Second, other differential activations which we recorded between execution and observation, and which could contribute to the agency ascription, are those concerning
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lateralization of effects. The statistically significant effects induced by action observation in the parieto-frontal sensory-motor cortical areas were in general bilateral, whereas those
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induced by execution were mainly contralateral to the moving forelimb (Fig. 3). This finding signifies specificity of the effects related to hand-identity for action execution, and small importance of the information related to hand lateralization for perception of the same action executed by another subject. Therefore, we suggested that these differences in lateralization of activations between executive and perceptual networks may help attribute the action to the correct agent, i.e., to the self during action execution and to the other during action perception. Once again, based on our quantitative results on non-human primates, we suggested that the 23
sense of agency may be articulated within the core sensory-motor components of the circuitry supporting action execution and observation, rather than within a single, agency attribution dedicated, master area in the parietal (Sirigu et al. 1999; Ruby and Decety 2001; Farrer and Frith 2002; Blakemore and Frith 2003) or in the prefrontal cortex (McCabe et al. 2001; Vogeley et al. 2001; Platek et al. 2004) as previously thought. Third, at the level of the prefrontal and premotor cortical areas we found that the reciprocally connected areas, 9/46d of the dorsolateral prefrontal cortex and F7 of the dorsal
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premotor cortex were exclusively activated for action observation (Raos et al. 2007; Raos and Savaki 2017). As already mentioned, area F7 activated by 9/46d specifically for observation
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may subsequently inhibit the forelimb representation at the level of the spinal cord (Kuypers 1981; Keizer and Kuypers 1989; Dum and Strick 1991; He et al. 1993) which, indeed, was
found depressed exclusively for action observation (Stamos et al. 2010). This inhibition of the forelimb region in the spinal cord, specifically for action observation, may be responsible for
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suppressing execution of the particular observed movement by the onlooker, although his/her
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execution-related sensory-motor system is activated. This prefronto-premoto-spinal circuitry,
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engaged only during action observation, is agent-specific and may also contribute to the attribution of action to the correct agent, in this case to the external agent and not to the self.
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We suggest that area 9/46v (9/46d) activated exclusively for execution (observation) within the dorsolateral prefrontal cortex may monitor and manipulate within working memory
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multimodal information, incoming not only from the premotor but also from the association parieto-temporal cortical areas (Petrides and Pandya 1984; Petrides 2005). This information from the parieto-temporal cortex may be related to the congruence (incongruity) between the
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intended (mentally simulated) movement and its actual (anticipated) somatosensory and visual consequences. This congruence (incongruity) monitored by the association cortical regions and relayed to the premoto-spinal circuitry may contribute to the attribution of action to the correct
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agent, that is, to the self (external subject) during action execution (observation). The above suggestion agrees with previous reports that the experience of agency may be based on
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comparison of motor commands with the re-afferent feedback from the moving muscles and the external events caused by these commands (Haggard et al. 2002; Johnson and Haggard 2005). Fourth, in the temporal cortex we found that multimodal area STP which is implicated in the integration of different sensory modalities (Boussaoud et al. 1990; Mistlin and Perrett 1990) was activated much more for observation than for execution in our study (Kilintari et al. 2014). This finding is in accordance with the neurophysiological results of Perrett’s group, who 24
demonstrated greater involvement of STSa/STP neurons in the perception of movements made by other subjects’ body parts than by the monkey’s own forelimb (Perrett et al. 1985a; Wachsmuth et al. 1994; Jellema et al. 2000). In fact, the much greater activation of the highorder multimodal area STP for observation than for execution, in contrast to the much bigger activation of the lower order unimodal parieto-frontal cortical areas for execution than for observation, constitutes additional differential effect which may contribute to agency attribution.
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In summary, our results indicate that agent-specific differences supporting attribution of
action to the correct agent may be distributed throughout the neural circuit responsible for
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action execution and observation, and may be encoded in differences in the degree and lateralization of the effects. There is no need for the execution/observation circuit to report to
a master area of agency ascription. Where such an area would report to? Infinite regress would be lurking in the background. All our results taken together, action generation, action
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perception, and action attribution may all be supported by different states of the same system,
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rather than by different systems (Savaki 2010). Once in place the action-execution system can
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function as simulator for motor cognition, and can also house the agency attribution mechanism. There is no obvious reason why an action would embody different brain substrata
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for its planning, execution, recognition, imagination and assignment.
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2.2 Humans
Human experiences, ‘perceptual’ as well as ‘imaginary’, are fundamentally private. Nevertheless we can infer their existence in other people from our own experiences. It does not come as a surprise that understanding other subjects’ experiences involves the brain circuits of
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our own relevant experiences. The question is why we do not confuse our real with our imagined experiences, or our own with others’ experiences? Apparently, there is a difference
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between knowledge of other minds and self-knowledge, which is responsible for attributing the experience to the correct agent, and which must be encoded either within or on the side of the
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brain circuit sub-serving the experience in question. The two fMRI experiments presented above demonstrate that the neuronal network supporting execution of action is also activated for observation as well as for imagination (1st person motor imagery) of the same action. The pertinent questions are: (i) why humans do not confuse others’ actions with their own, and (ii) why humans do not confuse their own physical actions with their imagined ones? In other words, why we do not confuse covert with overt actions given that the network of the overt action is also assigned the duty of its covert counterparts? Of course, the semi-quantitative 25
nature of activations, the lack of histological sections and the much lower resolution of the fMRI results in humans compared to the 14C-DG results in monkeys, do not allow for thorough conclusions and therefore comprehensive suggestions concerning agency attribution in humans. In any case, agent-specific effects such as possible differences in the pattern of activations among the three conditions, and/or possible divergence of connectivity targets of areas activated in common could distinguish between self-executed and observed actions as well as between self-executed and self-imagined actions. Here, we will present examples of
presented in a previous section (Filimon et al. 2015; Simos et al. 2017).
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both types of the above mentioned agent-specific effects provided by the two human studies
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First, in humans we found certain differences between the effects induced by overt and covert actions, within core areas of the execution-related system, similar to those in non-human primates described in the previous section. For instance, the fact that the overt action engages a larger fragment of the contralateral primary motor and somatosensory cortical areas
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(preserving hand-identity) compared to its covert equivalents, whereas covert actions engage
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larger bilateral part of the adjacent prefrontal/premotor and parieto-temporal cortical regions
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compared to the overt action, may contribute to attributing the action to the correct agent (Simos et al. 2017). Also, our finding that the right hemisphere is more involved in covert than
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overt actions complements not only our non-human primate results (Raos et al. 2007; Evangeliou et al. 2009), but also previous reports about the involvement of the right hemisphere
Daprati et al. 2010).
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in agency attribution (Ruby and Decety 2001; Decety and Sommerville 2003; Farrer et al. 2003;
Second, the targets of connectivity of the core somatosensory-motor areas activated in
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common diverged across the three motor conditions, with the network of connections of the covert actions being more complex than the execution-related network (Simos et al. 2017). These different patterns of functional connectivity may contribute to the distinction between
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the overt action and its covert counterparts, and therefore to the attribution of action to the correct agent. For instance the central position of the right IFG in functional connectivity, that
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we found during motor imagery, may reflect the suppression of movements during mere imagination of action, and may contribute to the distinction between imagined and real action, in a similar manner to that already described for action observation in non-human primates. Indeed, IFG has been associated with response inhibition (Rubia et al. 2003; Aron et al. 2004). Also the dense functional connections between the right MFG and bilateral IPL, that we found during motor imagery, may be related to the obligation of the participant to mentally act in spite of the incongruence between intended/predicted and actual sensory effects (Fink et al. 26
1999). Moreover, the central role of the right EBA in observation, evaluated by functional connectivity analysis, may be related to the attribution of action to the external agent as opposed to the self. Indeed, EBA has been associated with the actor-spectator distinction (Jeannerod 2004; Myers and Sowden 2008). Third, the enrollment of the pre-SMA and the IFG in motor imagery but not in action execution (Simos et al. 2017) may contribute to the attribution of action to the imaginary as opposed to the actual world. Actually, the IFG is known to play a role in action imagination
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(Stephan et al. 1995; Decety 1996; Grafton et al. 1996), and the pre-SMA which projects to the
prefrontal cortex (Picard and Strick 1996) is known to play a role in movement ideation
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(Gerardin et al. 2000) and agency-related processes (Crivelli and Balconi 2017). Activation of high order multimodal area(s) in the prefrontal lobe, unaccompanied by activation of the execution-related core sensory-motor areas, could indicate amodal representation of motor cognitive concepts. In contrast, the fact that the primary somatosensory-motor cortical areas
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are activated together with higher order areas for both overt and covert actions indicates modal
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representation of motor cognition, as will be analytically presented in a following section.
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Fourth, even within areas of overlapping functional activations the pattern of activation across voxels is sufficiently different to discriminate between overt and covert hand actions
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(Filimon et al. 2015). More specifically, multi-voxel pattern analysis allowed identification of voxels predictive of a particular condition, although activated by all three motor conditions. In
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the frontal lobe, voxels potentially distinguishing between execution, observation and imagery of reaching were located in the inferior frontal gyrus, ventral premotor, dorsal premotor and middle frontal cortical areas. In the parietal lobe, discriminative voxels were contained in the
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inferior parietal lobule, intraparietal sulcus, superior parietal gyrus and the precuneus. These findings suggest that information distinguishing between overt and covert hand actions may be represented throughout the fronto-parietal sensory-motor cortical network.
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In summary, the human data although of lower resolution than the non-human ones
further support our hypothesis that agent-specific differences, underlying attribution of action
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to the correct subject, are distributed throughout the sensory-motor neural circuit responsible for action execution and observation. Moreover, they suggest that information about the attribution of our own actions to the real or imaginary world may also be distributed throughout the very same sensory-motor circuit. All in all the action-execution system, depending on diverse states of its activation and functional connectivity patterns, can serve motor cognitive functions such as action perception and motor imagery, and also can house the agency attribution mechanism. 27
3. Mental simulation theory vs mirror neuron aspect of motor cognition
In this section we discuss the above mentioned findings in relation to a dominant contemporary view of motor cognition, the mirror neuron aspect of action understanding, and in reference to the two main neuro-philosophical theories of cognition, the mental simulation theory and the
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theory-theory. The theory-theory posits that we understand the behaviour of others by theoretical reasoning according to causal laws of behaviour, i.e. by pulling mental concepts out
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of a body of implicit knowledge that we all possess and by applying mental generalizations. The simulation theory implies that we understand the behaviour of other agents by impersonating their mental life, i.e. by pretending to have their beliefs and desires and by practically simulating their behaviour. In predicting or interpreting others’ actions we simulate
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them by using part of our own sensory-motor system ‘off-line’. This system can function off-
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line disengaged from its natural inputs from perception, can be fed with pretend inputs, and
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uncoupled from its natural command for action can predict possible outputs, e.g. how an external agent may act in a given situation (Gordon 1986). Simulation assumes a prior
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understanding of the intention that the interpreter attributes to the external agent, and may be introspectively vivid or semi-automatic with little salient phenomenology (Goldman 1989). A
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crucial assumption of the mental simulation theory in cognitive sciences is that the same brain mechanisms support the control of behaviour as well as the imagination and the comprehension of observed behaviour. This double-duty of behaviour-execution and behaviour-
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comprehension requires common underlying neural substratum and mechanisms (Cruz and Gordon 2006). In favour of this assumption, early on was found that the temporal characteristics (Decety et al. 1989; Sirigu et al. 1996) and the programming rules
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(Georgopoulos and Massey 1987) of overt actions are retained in their covert counterparts. In favour of this assumption are also our brain imaging results on non-human and human primates,
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as analytically presented in the two previous sections. The classical mirror neuron system consists of the premotor area F5 in monkeys (di
Pellegrino 1992; Gallese et al. 1996) which corresponds to the ventral premotor cortex (PMv) in humans (Petrides 2005), and the parietal area PF/PFG in monkeys (Gallese et al. 2002; Fogassi et al. 2005) which corresponds to BA40 of the inferior parietal lobe (IPL) in humans (Buccino et al. 2001). However, it is not clear why the primary motor (Tkach et al. 2007; Dushanova and Donoghue 2010; Vigneswaran et al. 2013) and the dorsal premotor cortex of 28
the monkey (Cisek and Kalaska 2004; Tkach et al. 2007) as well as the medial temporal lobe of humans (Mukamel et al. 2010), which also contain neurons with mirror-properties, are not considered parts of the mirror neuron system (Rizzolatti and Sinigaglia 2010; Rizzolatti et al. 2014; Rizzolatti and Sinigaglia 2016). Also it is not clear why the primary and secondary somatosensory areas, although activated for action observation, are not considered part of the human mirror neuron system and are dismissed with the unfounded explanation that their activation is due to an “additional mechanism” triggered by the mirror mechanism, or why the
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activation of the dorsal premotor and the superior parietal cortical areas is also dismissed as reflecting “motor preparation” and not mirror mechanism (Rizzolatti and Sinigaglia 2010).
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Moreover, it is not clear why the superior temporal sulcus (STS) was at first considered part of
the mirror neuron system (Rizzolatti et al. 2001) and later excluded from it (Rizzolatti and Sinigaglia 2010). These are a few examples of the scientifically unsubstantiated and therefore arbitrary inclusion/exclusion of brain areas in the mirror neuron system. In brief, the ground on
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the basis of which only two areas, the F5/PMv and the PF/IPL, are included in the mirror neuron
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system by Rizzolatti’s group over the years is shaky, based on statements which lack empirical
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support.
Another confusing issue is the functional role of mirror neurons. It is not clear what is
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encoded in the activity of mirror neurons when triggered by the observation of an action performed by another subject. It is not clear what it is that is “matched” between observation
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and execution of an action at the neuronal level. Mirror neurons in area F5 were reported to code for specific hand and mouth actions such as grasping, holding and breaking, as was the case for the movement related neurons of the same area (Rizzolatti et al. 1988; Gallese et al.
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1996). The mirror neuron aspect of action understanding proposes that a vocabulary of motor acts, limited in the mirror neurons of area F5, is responsible for our ability to understand the meaning of motor events (Rizzolatti et al. 2014). However, even today it is not clear whether
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action mirroring reflects low-level resonance mechanisms or high-level action understanding (Csibra 2007). Nor is it known whether mirror neurons in areas F5 and PF/PFG encode a
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“vocabulary” of actions or goals/intentions (Fogassi et al. 2005; Bonini et al. 2010), “the subjective value” of observed actions paired with reward (Caggiano et al. 2012) or something “more idiosyncratic” to the particular action situation (Nelissen et al. 2005). Interestingly, intentions, corresponding to the motor plan prior to motor command, have been considered the best candidate for shared representations of action (de Vignemont and Haggard 2008). However, goals and intentions are used interchangeably in the mirror neuron literature, and the experiments concluding that mirror neurons encode “goals/intention” have been heavily 29
criticized due to lack of control of several variables. For example, it was argued that mirror responses in those experiments may reflect the identity of the object to be grasped (Ruggiero and Catmur 2018) and/or the kinematics of movement (Papadourakis and Raos 2017) rather than the intention of the actor proposed by the authors (Fogassi et al. 2005). Moreover, it was
argued that given the range of meanings associated with a certain action and the range of actions associated with a certain goal, there must be a clear distinction between goals and implemented motor routines to achieve the goals (Hickok 2009). Furthermore, several
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inconsistencies exist in the relevant literature, such as, “we understand action because the
motor representation of that action is activated in our brain” although mirror neurons
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supposedly encode goals and not actions (Rizzolatti et al. 2001; Fogassi et al. 2005). Obviously, if mirror neurons encode goals and not motor aspects of the actions then the basis of action
understanding cannot be the activation of motor representations in mirror neurons (Hickok 2014).
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Anyhow, the proposed function of mirror neurons by the group of Rizolatti, i.e. encoding
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a motor vocabulary of actions and/or goals/intentions, is reminiscent of the theory-theory
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(Carruthers and Smith 1996) rather than the simulation theory (Gordon 1986; Heal 1986) because in order to understand others’ behaviour, according to the theory-theory we pull mental
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concepts out of a body of implicit knowledge (an encrypted vocabulary of behaviours) we all possess, whereas according to the simulation theory we mentally rehearse the observed
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behaviour and its decision making (engaging our own sensory-motor system). Although, the mirror neuron account of action understanding echoed the theory-theory (Simos et al. 2017), the mirror neuron theorists attempted to interpret their findings in the context of a mental
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simulation process (Gallese and Goldman 1998). Actually, the constant attempt of mirror neuron theorists to equate mirroring with simulation mechanisms, although it has been made
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clear that mirroring fails to satisfy the definition of simulation process (Gallagher 2007; Jacob 2008; Herschbach 2012; Spaulding 2012), is detrimental to the field. In contrast to ‘mirroring’ which involves the motor system by definition (Rizzolatti and Craighero 2004), ‘simulation’
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involves the motor system reflecting the action plan as well as the sensory system reflecting the somatosensory experience of the action and its visual guidance and update (Savaki 2010). As the time went by, a considerable amount of human neuroimaging data demonstrated significant activations for action observation in several brain regions beyond the two classical mirror neuron areas. For example, 15 years after the discovery of mirror neurons, frontal areas BA6, SFG, MFG, pre-cingulate, BA44, parietal areas SI, SII, SPL, the supramarginal gyrus of 30
the inferior parietal lobule, and the temporal areas MTG, ITG were considered part of the human mirror neuron system in addition to the original PMv and IPL (Gazzola et al. 2007). These experimental data which resisted interpretation by the original mirror neuron account of motor cognition, i.e. these anomalies, revealed that something was wrong with the structure of the theory. In response to these anomalies there were attempts to incorporate the new empirical data to the original theory, without abandoning any of its basic features. Although anomalies provided reasons to abandon central attributes of the theory in order to make theoretical
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concepts and empirical findings appear logical and economical, mirror neuron theorists ignored
some of the anomalies and incorporated others into the original theory without redefining its
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conceptual structure. In their attempt to (i) preserve the important role in action understanding (the mirror mechanism) only for the two originally described (classical) mirror neuron areas, and simultaneously in order to (ii) incorporate in the original circuit the constantly increasing areas involved in action observation, the mirror neuron theorists reused the “direct matching
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hypothesis”. This hypothesis retained the simulation process, which supposedly explained
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activations outside the two classical mirror neuron areas in terms of additional mechanisms,
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such as “sensory predictions from internal models” and “motor preparation”, triggered by the basic mirror mechanism (Gallese and Goldman 1998; Gallese 2007; Rizzolatti and Sinigaglia
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2010; Rizzolatti et al. 2014). In reality, two parallel processes were described: one which reflects movements and is driven by motor simulation, and another one that mirrors the goals
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of the action, is not driven by motor simulation and plays primary role in action understanding (Rizzolatti and Sinigaglia 2010). Thereafter, as a matter of fact, the mirror neuron account of action recognition referred to a hybrid theory, with the two original/classical mirror neuron
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areas (PMv and IPL) coding the inherent vocabulary of actions/goals, reflecting the theorytheory, and with the remaining/auxiliary areas involved in a simulation process reflecting the mental simulation theory. Nevertheless, the role of the proposed simulation process, in addition
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to the assumed encoding of a vocabulary of movements and goals, was not clear. In an attempt to explain it, they claimed that the parietal mirror neurons that discriminate action goals (e.g.
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grasping-to-place from grasping-to-eat) code large motor chains rather than individual movements, and activation of one segment of a chain triggers activation of the entire chain and therefore simulation of the observed action to ensure its understanding (Fogassi et al. 2005). However, to know which chain to activate, you have to know the end result of the action before its simulation. And if you know beforehand the end-result, what does simulation add to the understanding of the observed action (Churchland 2011)? Indeed if you already know which is
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the action and its goal, consequent simulation adds nothing to your knowledge. Simulation of the action is a step behind. Practically, the main objection to the extended mirror neuron account of action understanding is its complexity. It is more than twenty years since this theory appeared, yet mirror neuron theorists have failed (i) to specify the extent of the mirror neuron system, and (ii) to demonstrate that PMv and IPL play a more central role in action understanding than any other brain area containing mirror neurons and/or involved in action observation. The mirror
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neuron theorists cannot explain why they think that some of the areas that contain mirror neurons (i.e. PMv and IPL) are equipped with the “mirror mechanism” whereas some others
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(e.g. PMd and MI) are not (Rizzolatti et al. 2014). Overall, the proliferation of areas thought to
comprise the mirror neuron system and their capricious separation into core and auxiliary areas, the unsettled role of mirror neurons in action recognition and the constant modification of putative mechanisms supposedly employed by its core components (PMv and IPL) and its
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auxiliary areas (functioning on the side, triggered by core components) are symptoms of crisis
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of the mirror neuron view of motor cognition. Related to this crisis are fundamental problems
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with mirror neuron theorizing, reflected in a voluminous body of developed speculations and unjustified conclusions, invoking these neurons in accounts of empathy, evolution of language,
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autism, sexual orientation, political attitudes, business leadership, obesity, mass hysteria, contagious yawning, misattribution of anger in the music of avant-garde jazz saxophonists,
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development of Jungian collective unconscious archetypes and self-agency, to mention only a few [drawn from publications indexed in PubMed by (Hickok 2014)]. Also a sign of this crisis are the deflationary accounts of the possible roles played by the mirror neuron in recent
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literature (Brass et al. 2007; Dinstein et al. 2008; Hickok 2009; Heyes 2010; Savaki 2010; Oosterhof et al. 2013; Watson et al. 2013; Catmur 2015; Vannuscorps and Caramazza 2016), a let-down relative to early expectations, e.g. that mirror neurons would turn out to be the
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“neurons that shaped civilization” (Ramachandran 2009). The above mentioned assumptions, concerning the different functions of hard core and
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auxiliary areas of the mirror neuron system (Rizzolatti et al. 2014) which were divorced from empirical facts (Hickok 2009), and the inexplicable empirical findings (anomalies) which enforced expansion of the original circuit without revision of the original theoretical assumptions (Savaki 2010) destroyed the simplicity of the mirror neuron concept and turned it into a “degenerate” theory in the technical sense by Lakatos. Degenerate scientific theories are those, whose changes to their hypotheses are made out of the necessity to explain new troublesome evidence (anomalies), and whose predictions of novel facts are refuted so that they 32
are no longer testable (Lakatos and Musgrave 1970; Lakatos 1980). Indeed, attempts to revise the mirror neuron account, to make it compatible with new data of brain correlates of motor cognition, render it unfalsifiable. Notably, falsifiability is the demarcation criterion for a theory to be ranked as scientific (Popper 1992). In its effort to assimilate the ever increasing number of areas found activated for action observation, the mirror neuron theory invokes more and more arbitrary and convoluted mechanisms. This was precisely the problem of the geocentric Ptolemaic system of planetary motion. It collapsed under the burden of adding too many
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epicycles. To use Lakatos’ words, a core of assumptions cannot be abandoned or altered
without dethroning the theory altogether. On the bright side, anomalies create the crises that
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ultimately cause revolutions. Responses to anomalies supposed to provide the basis for incommensurability and revolutionary changes, which usually employ different conceptual systems (Kuhn 1970).
Awaiting such a revolutionary change, the mirror neuron aspect of motor cognition can
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be seriously challenged, nowadays, only by an alternative view which could better explain the
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available biological data. We think that such an alternative view of motor cognition was
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provided by our systematic and exhaustive quantitative investigation of the entire monkey cortex and spinal cord, and was further supported by our human study. Our results, as well as
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the segmented ones included in five relevant meta-analyses (Grezes and Decety 2001; Caspers et al. 2010; Molenberghs et al. 2012; Hetu et al. 2013; Hardwick et al.2018), demonstrate that
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areas engaged in action perception and motor imagery extend well beyond the two classical mirror neuron regions. They support the idea that a process broader than mirroring is responsible for motor cognition, as suggested earlier (Goldman and Sebanz 2005). We have no
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reason to believe that the recruitment of the forelimb-region in the primary motor, somatosensory, premotor and somatorecipient parieto-temporal cortical areas, during forelimbrelated covert actions and their overt counterparts, is less important for action perception and
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motor imagery than the recruitment of PMv and IPL as the mirror neuron theory claims. Consequently, our findings contradict the notion that mirror neurons in two brain areas alone
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code the meaning of actions. They support the notion that we decode the actions of others and we imagine our actions by activating our own action system off-line (without leading to movements) to internally rehearse or mentally simulate the observed or imagined actions. Striving to bring theory and fact into closer agreement, we suggest that to assign meaning to the actions of other people and to imagine our own actions we use, as a theory of mind, the simulation theory (Gordon 1986; Heal 1986). This theory is simple, self-consistent, plausible and compatible with all available biological data to our knowledge. All in all, the existing 33
finding that the sensory-motor apparatus serving an overt action also serves its covert counterparts, supports the simulation theory which assumes that a common mechanism underlies the control as well as the conception of actions. Of course, new empirical data could undermine the aspect of motor cognition we propose, just as the current data establish it. Instead of the original claim that mirror neurons are innate and result from an evolutionary adaptation for action-understanding (Rizzolatti and Arbib 1998; Rizzolatti and Craighero 2004), one could more plausibly suppose that mirror neurons are the by-product of
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associative learning (Keysers and Perrett 2004; Catmur et al. 2007; Heyes 2010). Interestingly, mirror properties have been shown to develop through sensorimotor learning and are neither
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innate nor fixed once acquired. This notion is supported by the findings that, the mirror properties depend on the experienced contingency rather than the similarity between executed and observed actions, and that even in adulthood, the mirror neuron system can be reconfigured by sensorimotor learning and can become counter-mirror (Catmur et al. 2007; Cavallo et al.
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2014; Catmur et al. 2017). At this point we should note that the associative property of mirror
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neurons is compatible with their participation in action perception (Cook et al. 2014) and in
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mental simulation of action (Savaki 2010) during which learned sensory-motor associations of the action are recruited. Moreover, our suggestion that mental simulation of an action along
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with its sensory effects underlies motor cognition is analogous to the notion that motor cognition is embodied in action, and supports modal theories of knowledge (see following
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section of discussion) proposing that sensory–motor simulators implement fully functional conceptual systems (Barsalou 1999). After all, what is simulated is bodily experience (Barsalou 2008b).
Finally, hybrid theories have also been proposed. As we already described, the mental
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representation of an action is a ‘pseudo-pragmatic’ percept, with all its constituents corresponding to those of the physical overt action. A hybrid theory of cognition would consist
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of propositional elements in addition to the pseudo-pragmatic ones. Authors have defended hybrid accounts of mindreading, with the mental simulation theory playing the main role and
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the theory-theory a minor but non-negligible role (Heal 2003). Such an account of motor cognition cannot be excluded, although there is no empirical evidence that at least simple actions, such as the ones used in our studies, contain a propositional element requiring a role played by the theory-theory.
4. Embodied cognition 34
According to rationalists, such as Plato, Descartes and Kant, concepts are a priori or innate, and propositions are formed and justified only by reasoning. Following up on rationalism, in the early decades of cognitive psychology most theories of human knowledge implied centralized and abstract cognition involving propositional forms of knowledge, while sensorymotor systems were thought to serve only as peripheral devices. On the other hand, according to empiricists, such as Aristotle, Locke and Hume, concepts are a posteriori, and rationally
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acceptable propositions are justifiable only through experience. Influenced by empiricism,
certain theories imply that human cognition is grounded in perceptual processes and motor
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control, and the mind should be approached in the context of its relationship to a body
interacting with the environment (Barsalou 1999; Lakoff and Johnson 1999). According to these theories, in order to study cognition we must consider the situation and the situated cognizer in combination, because the mind and cognition extend not only beyond the brain into
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the body, but also beyond the body into the surrounding world (Clark and Chalmers 1998;
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Shapiro 2011). The mind emerges from the subjective experience of the body in the world
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(Leitan and Chaffey 2014), and cognition is distributed across the united system of mind, brain, body and environment (Wilson 2002). Nowadays, there is convergent neurobiological evidence
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supporting the empiricists’ view, and more specifically the perceptuo-motor theories of embodied human cognition. Accordingly, concepts derive from bodily experiences, and
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sensory-motor systems of the brain contribute significantly to their formation. The bodily experiences represented in these sensory-motor systems are ‘simulated’ during cognition (Barsalou 1999). Indeed, our high resolution cortical maps and our quantitative results
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throughout the entire monkey brain, as well as our human data, demonstrate an overlap of the sensory-motor neural nets supporting execution, observation and imagination of the same action. These results indicate the existence of close relations between the conceptual action
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representations underlying action perception and imagination on one hand and the executionrelated sensory-motor networks on the other. This finding is in favor of the embodiment view
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that concepts are essentially grounded in perception and action (Barsalou et al. 2003). In agreement with our finding that overt and covert actions share sensory-motor
representations, it was suggested that motor cognitive processes required for covert actions are embodied in the sense that they use simulations of sensory-motor processes, through reactivation of the neural circuitry which supports the physical overt action (Dijkstra and Post 2015; Korner et al. 2015). It was also reported that a given representation qualifies as embodied if it utilizes body-related codes, which are often associated with activations in sensory-motor 35
cortical areas (Goldman 2012a). A typical example of embodied cognition is the application of motor codes to nonstandard tasks, e.g. using bodily motor codes not to guide one’s own actions but to represent the actions of others (Goldman 2014). In fact, our finding that during covert actions the observer or cognizer engages in redeployment of bodily sensory-motor codes, which originally underlie action execution, supports the proposal that reuse of neural circuits for a variety of cognitive purposes is a fundamental principle of brain organization. Accordingly, neural circuits originally established for one purpose (e.g. action execution) are
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redeployed during development in the service of other functions (e.g. its covert counterparts), without losing their original purpose (Anderson 2010). In brief, our findings support neural
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reuse theories which claim that low-level bodily sensory-motor codes are reused in different arrangements to serve different cognitive domains. Perceptual, motoric and conceptual cognitive processes cannot maintain body-neutrality, because minds and brains have bodies.
More explicitly, we demonstrated that generation, recognition and imagination of the
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same action are supported by largely the same neural substrata. Activation of multimodal areas
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that combine information across modalities in the prefrontal lobe, unaccompanied by activation
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of the execution-related sensory-motor system, could indicate amodal representation of motor cognitive concepts. Instead, the fact that most of the components of the execution-related
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system, including the upper limb representation in the primary somatosensory and primary motor cortical areas, are activated together for both overt and covert actions indicates that
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multimodal as well as unimodal cortical areas are involved in conceptual processing. The high order prefrontal cortical regions engaged in covert actions could act as conceptual convergence zones (Damasio 1989) within the activated sensory-motor circuitry. They may be integrating,
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rather than storing, distributed modality-specific conceptual features and they may re-instate them in the core modality-specific sensory-motor cortical regions (Simmons and Barsalou 2003; Kiefer et al. 2007; Kiefer and Pulvermuller 2012). Therefore, the unimodal and the
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multimodal cortical areas activated in our studies may be equally involved in conceptual processing.
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Because sensory cortical areas were involved in action, and motor cortical areas in
perception and imagination in our studies, we suggest that sensory effects are combined with motor commands in integrated action codes. Indeed, it has been proposed that perceptual cognitions may be automatically grouped together with motoric cognitions, assigning the status of embodied cognitions (Barsalou 1999, 2008a). If actions become integrated with their somatosensory and visual effects, then observation of an effect may trigger the associated action. In other words, visual perception of an action executed by another subject may restore 36
in the observer’s brain the associated sensory-motor representations established during action execution as a consequence of correlated neuronal activity. This process may serve the understanding of observed action. Similarly imagination of an action, which presupposes knowledge of its effects, may recruit pertinent conceptual memory traces encoded in the sensory-motor representations of the action. These representations may run simulations of external situations ‘off-line’, i.e. decoupled from physical inputs and outputs, in order to
functional conceptual representation, a building block of cognition.
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support motor cognition. In this manner, a distributed sensory-motor system implements a
In summary, the results described in previous sections support modal rather than
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amodal/symbolic conceptual representations. They support the idea that conceptual representations may be multimodal simulations, distributed across modality-specific systems
(Barsalou 1999; Barsalou et al. 2003; Barsalou 2008a). They are compatible with the report that on-line aspects of embodied cognition (e.g. action execution) include activities related with
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task-relevant external situation and involve a mind/brain upholding a body interacting with a
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real-world situation by sensory-motor mechanisms. On the other hand, off-line aspects of
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embodied cognition include activities with external resources distant (e.g. action observation) or altogether absent (e.g. motor imagery) and involve mental simulation of the body interacting
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with the external world, also by sensory-motor mechanisms, serving the embodied motor cognition (Wilson 2002). All things considered, mental simulation of external events and off-
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line embodied cognition may reflect a general underlying principle of the human brainpower. According to embodied cognition theorists, intelligence lies less in the individual brain and more in the dynamic interaction of the brains and bodies with the wider world (Anderson 2003).
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It was recently argued that neuroimaging data do not allow discrimination between embodied cognition and amodal/symbolic accounts of conceptual representations, and that conceptual processing relies on abstract rather than sensory-motor representations (Caramazza
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et al. 2014; p.5). The basic argument was that “the overlap between sensorimotor mechanisms and semantic knowledge does not seem to occur within areas involved in low-level
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sensorimotor processing”. According to this argument, areas thought to carry low-level representations such as the primary sensorimotor cortex should not be engaged in covert actions such as action imagery which is based on semantic knowledge. In contrast to this argument, several studies in the literature demonstrate that the primary motor and primary somatosensory cortical areas are activated for covert actions [(Hari et al. 1998; Grezes et al. 1999; Avikainen et al. 2002; Raos et al. 2004, 2007; Tkach et al. 2007; Lorey et al. 2013), see also meta-analyses by (Grezes and Decety 2001; Caspers et al. 2010; Molenberghs et al. 2012; Hetu et al. 2013; 37
Hardwick et al.2018). In fact, our finding that the forelimb-regions within both the primary motor and the primary somatosensory cortical areas are specifically/somatotopically activated (i) in monkeys observing another subject executing an action with the arm/hand, as well as (ii) in humans during observation and imagination of hand-related actions, indicates that sensorymotor representations may function as conceptual semantic representations; that abstract mental concepts may be rooted in sensory-motor workings. In brief, our finding that the cortical network implicated in semantic representation of actions (covert actions) is isomorphic with
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that involved in factual experience (overt action) is a strong account of embodied simulation applied to neural structures (Meteyard and Vigliocco 2008).
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It may be argued that a cognitive process is not necessarily equivalent to the neural
activity which accompanies it, and that the type of content represented in each brain area involved in a physical or mental operation is not immediately accessible. However the region of forelimb representation, in premotor and motor as well as in primary somatosensory and
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other somato-recipient cortical areas, found to be specifically activated for observation of an
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action involving the forelimb (Raos et al. 2004, 2007; Evangeliou et al. 2009; Raos and Savaki
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2016) indicates that the activated circuit encodes some kind of meaning of the observed action. Also, the fact that motor imagery is characterized by the same temporal regularities and
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responsiveness to physical laws as action execution (Decety et al. 1989; Sirigu et al. 1995) cannot be unrelated to the meaning of the action. Moreover, activation of the somatosensory-
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motor cortex somatotopically during understanding action words/sentences related to body parts (Hauk et al. 2004; Pulvenmuller 2005; Kemmerer 2015) indicates that action concepts are stored in a motoric code, and understanding speech resonates mental simulation of its corporal content. Further support to embodied cognition is provided by the neurobiological
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evidence that not only understanding others’ actions, but also understanding others’ sensations, emotions and intentions rely on the sensory-motor representations of the corresponding actual
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experiences (Haggard and Clark 2003; Keysers et al. 2004; Avenanti et al. 2005; Blakemore et al. 2005; Bufalari et al. 2007). Many more examples of embodied cognition could be cited,
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such as, that the size of graspable objects is perceived as a proportion of the hand's size, highlighting that the body is used as a perceptual ruler (Linkenauger et al. 2010), and that distance is scaled by walking effort if observers are walkers, by throwing effort if they are throwers, and by reaching ability if they are reachers (Witt et al. 2010). Therefore, in contrast to the idea that cognition relies on amodal conceptual representations, several biological data indicate that motor cognition engages modal representations in a single sensory-motor network supporting both covert actions and their overt equivalents. We suggest that all areas of this 38
circuitry are necessary and none of them, on its own, sufficient for motor cognition. The precise nature of conceptual process in each area of the network and the weight of each processing node within it require further investigation. It was also argued that activation of sensory-motor representations may be a consequence and not an integral part of retrieving semantic knowledge (Mahon and Caramazza 2008; Caramazza et al. 2014). It was suggested that action mirroring follows (rather than causes) the
understanding of others’ mental states (Csibra 2007; Jacob 2008). Although it is tempting to
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think that our findings equate simulation with meaning, we cannot exclude the possibility that
mental re-enactment is an epiphenomenon rather than a process necessary and sufficient for
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meaning construction. Actually, any neural correlate of behaviour recorded by neuroimaging
may be an epiphenomenon, and meaning could be represented by abstract symbols which have no perceptual or motor substance, in brain areas which only secondarily activate the sensorymotor system. In such a disembodied cognition scheme, this activation need not be related to
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the meaning of the action, and is due to information spreading to the sensory-motor system
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without playing any conceptual role (Mahon and Caramazza 2008). We can counter this
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argument by pointing that, since mental events are facets of brain activity, doing, conceiving and mentalizing are all brain processes. Brain activation starts before the experience (Libet et
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al. 1983; Libet 1985). Brain activity instantiates conscious mental states rather than causes them. However, there is a brain-to-mind rather than mind-to-brain causation (Haggard and
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Eimer 1999). Consequently, if activity is spreading to the sensory-motor system and is playing no conceptual role as suggested earlier (Mahon and Caramazza 2008; Caramazza et al. 2014), there must be a master brain area which is informed about the course of the action ‘in advance’
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and orders the sensory-motor system to simulate the action. To date we have no empirical evidence in support of a master area commanding the sensory-motor system to act or mentalize. Moreover, if such an area existed the subsequent sensory-motor simulation would be redundant
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and therefore non-economical. Finally, what is it that would drive the master area? Once again we would be trapped in an infinite regress, unless we accept the presence of a Platonic ghost
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in the machine. In contrast, the presented results indicate that perception and mentalization are instantiated by neural activity in the sensory-motor system. In conclusion, current experimental data that the action-execution system can serve motor cognitive functions such as action perception and motor imagery and also can house the agency attribution mechanism, support the embodied condition theory implying that cognitive workings are grounded in sensory-motor processing, and that cognitive abilities depend on the mechanism of simulation. They contradict the neo-Cartesian dualism assuming separation 39
between mind and body or between mental states and physical substratum. They do not support the existence of a single high order master area with inferential role in processing motor concepts. However, although there is no empirical evidence indicating that mental reenactment of an action during practice of its covert equivalents is an epiphenomenon, there is also no proof that mental re-enactment is a necessary and sufficient mechanism of
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understanding and imagining actions.
Acknowledgments
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We acknowledge support by the project “Advanced Research Activities in Biomedical and Agroalimentary Technologies” (MIS 5002469) which is implemented under the “Action for the Strategic Development on the Research and Technological Sector”, funded by the
Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-
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2020) and co-financed by Greece and the European Union (European Regional Development
A
CC
EP
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M
A
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Fund). We also thank Adonis Moschovakis and Keith Frankish for their comments.
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Captions to figures Fig. 1 Schematic illustration of medial (upper) and lateral (lower) view of the left hemisphere of an inflated monkey brain. Light gray areas represent unfolded sulci: crowns of sulci are depicted by solid gray lines and
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fundi by dashed lines. Areas activated only for action execution (compared to the motion control) are color coded red; areas activated only for action observation (compared to the
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motion control) are in green; overlapping activations common for execution and observation
are in orange. a, anterior; AIP, anterior intraparietal; As, arcuate sulcus; c, caudal; CM, caudal
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auditory belt medial part; CGs, cingulate sulcus; Cs, central sulcus; d, dorsal; FST, fundus of
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superior temporal area; F2-f, forelimb region of the dorsal premotor cortical area F2; F5, F7
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ventral and dorsal premotor cortical areas; i, intermediate; Ia, Ig, insula agranular, granular;
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IPs, intraparietal sulcus; 5IPp, the caudal-most intraparietal region of the medial bank; Lf, Lateral Sylvian fissure; LIP, lateral intraparietal; Ls, lunate sulcus; m, medial; MI-f, forelimb
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region of the primary motor cortex; MIP, medial intraparietal; MST, medial superior temporal area; MT, middle temporal extrastriate area V5; p, posterior, PEip, PE intraparietal; PGm, area 7-medial; POs, parieto-occipital sulcus; Ps, principal sulcus; PV, parietal ventral area; r, rostral;
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Ri, retroinsula; SI-f, forelimb region of the primary somatosensory cortex; SII, secondary
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somatosensory cortex; STs, superior temporal sulcus; TPO, temporo-parieto-occipital association area; TPOr and PGa, parts of the superior temporal polysensory area STP; TS1,
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TS2, temporalis superior 1, 2; v, ventral; VIP, ventral intraparietal; VS, ventral somatosensory area; V1, striate visual cortex BA 17; V3d-op (ot), occipito-parietal (occipito-temporal) portion of the extrastriate area V3d. Accumulated data from our original publications Evangeliou et al (2009); Kilintari et al (2011, 2014); Raos et al (2004, 2007, 2014, 2016, 2017).
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Fig. 2 Plots of local cerebral glucose utilization (LCGU) values in the forelimb regions of MI and SI. LCGU values in the experimental monkeys represent average of the right hemispheres (contralateral to the moving forelimb). Each plot represents average LCGU values (lines in color) and 95% confidence intervals (shaded area around lines) per 100 μm along the
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anteroposterior extent of the MI/SI regions of forelimb representation. The length of the plotted cortical field (rostrocaudal extent shown in mm in the abscissa) is illustrated in orange in the
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brain sketch on the left. It includes the primary motor cortex in the frontal convexity (MIconvexity) and within the anterior bank of the central sulcus (MI-bank) as well as the primary somatosensory cortex (SI) in the posterior bank of the sulcus, as indicated by the orange bar
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above the plots. The vertical line, displaying the LCGU values in μmoles/100g/min, is
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positioned at the level of the fundus of the central sulcus. The blue plot displays the average
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LCGU values in the motion control hemispheres (Cm); the green and red plots display the average of LCGU values in the right hemispheres of the observation (Or) and execution (Er)
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groups of monkeys, respectively. The weaker, though statistically significant compared to the motion control, activation in MI-forelimb and SI-forelimb regions for action observation (green plot) may reflect the intended movement and the anticipated somatosensory (ss) consequence
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of the movement, respectively. The stronger activation in the same regions for action execution
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(red plot) reflects the motor command in addition to the intended movement in the MI-forelimb region, and the actual re-afferent feedback from the moving muscles in addition to the
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anticipated somatosensory effect in the SI-forelimb region. Figure modified from Fig.1 of Raos et al (2004) with the addition of the motor effects in the frontal convexity.
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Fig. 3 Plots of percentage LCGU differences along the rostrocaudal extent of the cortical field illustrated by the orange ribbon in the brain drawing above the plots. Red plots illustrate the percent differences between the action execution group of monkeys and the motion-control Cm group (E/Cm). Green plots illustrate the differences between the action
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observation monkeys and the Cm (O/Cm). Plots with solid lines correspond to the right hemispheres (Er, Or) contralaterally to the moving arm. Plots with dotted lines correspond to
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the left ipsilateral hemispheres (El, Ol). Red and green shaded areas indicate 95% confidence
intervals. Baseline corresponds to 0% LCGU difference from the Cm. The plots include the
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forelimb representations in MI and SI cortical areas and the adjacent parietal areas PE and
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PEip, as indicated by the orange bar above the plots and below the brain sketch. Zero
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rostrocaudal extent in the abscissa represents the point of alignment of the horizontal brain
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sections in the fronto-parietal reconstructed maps, i.e., the anterior crown of the Cs. Figure
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modified from Fig.6 of Raos et al (2007) and Fig.9 of Evangeliou et al (2009).
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EP
CC
A TE D
IP T
SC R
U
N
A
M
Table. 1: T values and size of significantly activated clusters compared to baseline activity, in the three tasks, in humans.
IP T
OBSERVATION Size T 6 7.29 12 4.44 29 3.73 10 4.57 6 3.98 15 6.17 25 6.31 20 4.85 55 8.47 40 5.28 56 4.90 6 4.70 9 4.71 21 4.90 169 5.90 165 7.10 125 6.23 154 5.60 83 5.62 59 6.46 204 6.50 380 6.50 15 5.32 25 3.90 27 6.55 40 6.76 18 4.05 21 6.48 30 4.60
SC R
IMAGERY Size T 138 4.70 179 5.13 56 3.60 26 6.30 130 9.75 18 5.70 140 6.82 190 5.17 480 8.12 190 9.03 227 6.60 510 6.70 80 6.80 190 6.20 815 8.84 437 8.95 694 7.35 330 6.55 214 7.37 184 5.55 127 6.40 163 6.40 56 6.68 98 6.68 24 6.50 134 5.55 40 5.54 120 6.00 96 6.20
U
N
EXECUTION Size T 310 13.36 370 6.56 105 5.55 30 7.50 290 9.95 73 5.38 95 6.92 49 6.06 240 10.27 72 4.85 65 4.24 450 6.20 36 6.80 29 5.37 550 6.69 280 6.34 620 9.70 180 5.64 76 5.86 44 5.78 180 7.39 32 7.39 28 5.68 54 6.23 36 5.08 56 5.73 57 8.00 79 6.35 29 6.40
A
H L L R L L R L R L L R L L R L R L R L R L R L R L L R R L
M
BA 2 3 2/3 40 4 4 6 6 6 6 6 6 8 8 7 7 40 40 37 37 37 37 22 22 19 7 19 37 19
TE D
Brain area SI-Upper Limb SI-Upper Limb SI-Upper Limb SII MI-Upper Limb MI-Upper Limb PMv PMv PMd PMd PMd SMAd-proper MFG MFG SPL SPL IPLd IPLd Poster. MTG Poster. MTG EBA/MT EBA/MT Poster. STG/TPj Poster. STG/TPj Cuneus Poster. Precuneus LG FG MOG
Activated clusters in each task as compared to fixation (p = 0.001 uncorrected). All activations were also significant at FDR p = 0.05. BA: Brodmann area; H: hemisphere; Size: cluster size in
EP
number of voxels. d: dorsal; EBA: Extrastriate Body Area; FG: fusiform gyrus; IPL: inferior
CC
parietal lobule; L: left; LG: lingual gyrus; MFG: middle frontal gyrus; MI: primary motor cortex; MOG: middle occipital gyrus; MTG: middle temporal gyrus; MT: Middle Temporal
A
cortex; PM: premotor cortex,; R: right; SI: primary somatosensory cortex; SMA: supplementary motor area; SPL: superior parietal lobule; STG: superior temporal gyrus; TPj: temporo-parietal Junction; v: ventral. Table modified from Supplementary Table 1 in Simos et al (2017).
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