Beyond bodily anticipation: Internal simulations in social interaction

Accepted Manuscript Beyond bodily anticipation: internal simulations in social interaction Henrik Svensson, Serge Thill PII: DOI: Reference:

S1389-0417(16)30089-4 http://dx.doi.org/10.1016/j.cogsys.2016.06.003 COGSYS 500

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Cognitive Systems Research

Received Date: Accepted Date:

25 May 2016 19 June 2016

Please cite this article as: Svensson, H., Thill, S., Beyond bodily anticipation: internal simulations in social interaction, Cognitive Systems Research (2016), doi: http://dx.doi.org/10.1016/j.cogsys.2016.06.003

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Beyond bodily anticipation: internal simulations in social interaction. Henrik Svensson, Serge Thill∗ School of Informatics, University of Sk¨ ovde, 54128 Sk¨ ovde, Sweden

Abstract There is a long history of implementing internal simulation mechanisms in robotics, typically for the purposes to predict the outcomes of motor commands before executing them. In the literature on human cognition, however, the relevance of such mechanisms goes beyond that of prediction: they also provide foundational aspects of social cognition and interaction. In this paper, we present a review of internal simulation mechanisms from this perspective. We contrast the roles they play in human cognition, in particular in the context of social interaction, with robotic implementations. We further discuss work in social robotics, emphasising in particular that a substantial effort currently goes into evaluating social robot systems, but that social robots to date are still limited in their abilities. We further discuss episodic simulations, which are functionally distinct from the type of internal simulations we consider here, and note their role in prospective thought in particular. Overall, we conclude that one of the necessary next steps on the road to social robots may be to develop social abilities from the bottom up using internal simulations. By reviewing how these aspects all tie together in human cognition, we hope to clarify how this may be achieved. Keywords: Internal simulations, social cognition, self-other distinction, social robotics ∗ Corresponding

author Email addresses: [email protected] (Henrik Svensson), [email protected] (Serge Thill)

Preprint submitted to Cog Sys Res

May 25, 2016

1. Introduction The context of social interaction is crucial for understanding the development and functioning of human cognition. Fogel et al. (2002), for example, argues that “being-in-relation, participating in an interpersonal relationship, is a fundamental, irreducible, primary, way of being. Individuals are born into interpersonal relationships. We never, not for a single moment of life, exist outside of relationships even when we are physically alone. Our thoughts, our movements, the artefacts carried with us are all grounded in cultural processes that were conceived, composed, and codified by individuals-in-relation (Fogel, 1993)” (p.623). De Jaegher et al. (2010) additionally points out that even social interaction itself cannot merely be reduced to cognitive processes in an individual’s head – rather, the interaction may in itself be a constitutive aspect of social cognition. Many, if not all, mechanisms that underlie cognition thus play a role in social interaction. For those interested in the study of human cognition, social interaction therefore provides an important setting. For those interested in artificial cognitive systems – which is the perspective we will take here – social interaction is equally important, not the least because real-world artificial cognitive systems are built to interact with humans. There is also an increasing trend towards making such systems explicitly “social”, see for example recent developments towards “companion” robots such as Jibo (https://www.jibo.com/) or Pepper (https://www.aldebaran.com/en/coolrobots/pepper). Other examples of robots designed explicitly for social interaction include those for use in therapies, for instance for children with autism spectrum disorder (see Scassellati et al., 2012; Thill et al., 2012, for comprehensive reviews). Here, we are primarily interested in the role of internal simulation in these social interactions for two reasons. First, internal simulations are a necessary component of social cognition. We will begin the review by making this case in the next section. Second, internal simulation has already received some at2

tention in robotics (see, for example, Vernon, 2014, section 7.5.3, and Svensson et al. 2013), but robotic efforts are mostly limited to predicting the outcome of immediate actions in relatively simple environments or tasks (see Svensson et al., 2013; Svensson, 2013, for examples). The second part of this review therefore contrasts such efforts with current work in social robotics (from which the inclusion of simulation mechanisms is still largely lacking). We conclude with a reflection what internal simulation can bring to social robotics. While presenting this argument, we also consider related concepts – primarily, we need to distinguish between embodied and episodic simulations, and discuss theory of mind and prospection. First, however, we clarify what precisely embodied simulation is, and how it relates to social cognition.

2. Embodied simulation Simulation (or emulation, the term preferred by e.g. Grush, 2004) has been implicated in nearly all cognitive phenomena (e.g. perception, mental imagery, long-term memory, short term memory, and language; see Svensson, 2013). While some of the arguments tie simulations to specific cognitive phenomena (e.g. language, see Zwaan, 2003), others see them as a general principle of cognition (e.g. Hesslow, 2002). The various simulation theories primarily differ somewhat with regard to their relation to certain epistemological or theoretical frameworks. For example, some accounts are representationalist (Barsalou, 1999; Grush, 2004) while others are purely associationist Hesslow (2002). That said, there are two general defining aspects of simulations: reactivation and prediction (Svensson, 2013). Here, we use embodied simulation to refer to this general view of simulation. The term simulation is easily confused as referring to explicit, conscious, and/or, deliberate mental simulations, such as the type of cognitive ability that is implied by prospection (the ability to envision oneself in future situations). We refer to such simulations as episodic, and return to them later. For now, it is therefore important to underline that embodied simulation refers to a particular

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cognitive mechanism, which can be described at different levels of analysis: the phenomenological, the functional, and the neural level (see Hurley, 2008, for a concrete example). Although embodied simulations do not always involve consciousness or awareness, some of the phenomenological aspects of cognition reported in mental imagery and dreams are thought to reflect the functioning of embodied simulations (Svensson et al., 2013). For example, first person motor imagery involves feeling “as if” actually performing the action (Decety, 1996; Jeannerod, 1994), and visual imagery can, at least under certain conditions, be similar to our perception of external objects to the point that they are hard to distinguish (Perky, 1910, cited by Cotterill 1998, pp. 19–20). At the functional level, as previously mentioned, embodied simulations function as reactivations and predictions: first, they reactivate modality-specific information, thereby providing access to the epistemic properties of previously experienced situations (Barsalou, 2005; Meyer & Damasio, 2009; Zwaan, 2003, for a more thorough discussion of the relation of simulation to representation see Chapter 2 of Svensson 2013). An example by Barsalou illustrates the basic concept: Consider a situated conceptualization for interacting with a purring house cat. This conceptualization is likely to simulate how the cat might appear perceptually. When cats are purring, their bodies take particular shapes, they execute certain actions, and they make distinctive sounds. All these perceptual aspects can be represented as modal simulations in the situated conceptualization. Rather than amodal redescriptions representing these perceptions, simulations represent them in the relevant modality-specific systems. (Barsalou, 2005, p. 626-627) Second, embodied simulations function as predictions by chaining simulated experiences into sequences (Hesslow, 2012). Most commonly, simulated perceptions are coupled to simulated actions – that is one is generated based on the 4

other without any overt movements or perception/interoception. Svensson et al. (2009) argue that embodied simulations can consist of at least three different anticipatory functions (implicit anticipation, bodily anticipation, and environmental anticipation), each likely implemented by several different neural systems. For example, implicit predictions of varying complexity are found in corticocerebellar loops (Downing, 2009; Wolpert et al., 1998), basal-gangliacortex loops (including amygdala influence) (Prescott et al., 1999; Downing, 2009), corticocerebellar loops (Downing, 2009; Wolpert et al., 1998), and neocortical loops (Wise & Murray, 2000).

3. Embodied simulations in social interaction Embodied simulations are involved in fundamental aspects of social interaction such as the self-other distinction. They are often elicited by present social stimuli and are closely tied to self-locomotion (Lindblom & Ziemke, 2006) and bodily aspects (Barsalou et al., 2003). In this section, we expand on this, basing ourselves on Hurley’s (2008) shared circuits model (see also Gallagher, 2015, for a critical discussion of the role of embodied simulation in social cognition). Hurley’s (2008) model consist of five levels (here described at the subpersonal functional level): Level 1 contains feedback-controlled behaviour: the organism is able to adapt its behaviour based on feedback reafference (afferent signals caused by the organisms actions) or exafference (afferent signals caused by environmental events). Level 2 adds the ability to predict the outcome of one’s own actions. Organisms have the ability to reliably learn associations between particular contexts and actions, and outcomes (in other words, level 2 adds what is known as forward models in the motor control literature, see Wolpert et al., 1995). Level 3 adds mirror mechanisms, which give an organism the ability to imitate behaviours of others. While we omit some of the intricacies of this layer 5

in the present discussion, the main point is that, although this implies (shared) circuits for the self and other, it is, by itself, not sufficient for distinguishing between self and other. Level 4 adds the ability to monitor the inhibition of outputs, which implies the ability to separate actual from possible actions. When coupled with level 2, it is possible to internally simulate different behavioural alternatives and their outcomes in a particular situation, and only then choose the most advantageous. When coupled with level 3, it is possible to distinguish self from other in that the mirroring mechanism is able to infer the “goal” of the observed actions through the (mirrored) activation of one’s own simulated response, and then retrodicting the causes of that action. Level 5 adds the ability to monitor the inhibition of inputs which enables to organism to operate in a fully off-line mode, and to generate information about both the causes and effects of the others actions. The difference from level 4 is that monitoring of simulated inputs enables a distinction between possible and actual actions in others, that is, the ability to simulate what others’ simulated inputs could be. This allows the agent to both simulate one’s own actions and the actions of others. It has been argued that level 2 predictions are involved, for example, when trying to tickle oneself with a feather: as it turns out, it is often necessary for someone else to do the tickling for it to be effective. The reason for this lies in the difference in predictability between one’s own actions and the action of others. In neural terms, it has been argued (Blakemore et al., 1999, 2001, 2000) that when an action is observed, efference copies (i.e. outputs of simulated actions) are fed to the cerebellum. The cerebellum then generates a “guess” of the likely sensory consequences of that action, which in turn is compared to the actual sensory feedback from touch sensors. If there is no discrepancy between the two, this then cancels out the activity in the somatosensory cortex. The actions of others are more difficult to predict, and therefore the somatosensory activity from the actions of others cannot be suppressed entirely. Hurley (2008) 6

argues that this type of anticipatory relationship allows organisms to attribute perceptual changes to either perception or self-generated actions. While the self-other distinction is not fully established at this level, it therefore does allow for the distinction between events caused by one’s own actions and those caused by external events. At level 4, simulated actions can be inhibited and monitored, thus making it possible for level 2 predictions to be used “off-line”, for example in mental motor imagery (Grush, 2004). For example, experiments have shown that imagery evokes autonomic responses that are beyond voluntary control (such as the adaptation of heart and respiratory rates), and that these are activated to an extent proportional to that of actually performing the action (Decety, 1996; Decety et al., 1991). Thus, these level 4 embodied simulations are often closely related to aspects of the sensorimotor systems. Level 3 involves the so-called mirror system, which can be seen as an extension of level 2 predictive mechanisms. Mirror neurons are neurons that are active both when an agent executes an action and when the same agent observes the action being carried out by someone else (Di Pellegrino et al., 1992; Rizzolatti et al., 1996). Their role (if any) in cognition continues to be a matter of debate (e.g. Hickok, 2009) and it goes beyond our scope to discuss them in detail (but we refer the interested reader to Thill et al., 2013, for a recent review). Mirroring can go beyond sensorimotor aspects to include, for example, emotion. Wood et al. (2016), for instance, argue that emotion recognition is partially dependent on embodied simulations that reactivate affective states which also results in behavioural changes. For the present purposes, mirror activity can be interpreted as reactivation, one of the defining aspects of embodied simulations: (pre)-motor structures of the brain are activated in a manner that resembles activity during normal action, but does not cause any overt movement. Mirror circuits might thus implement both forward and inverse models, that is, both predictions of the sensory consequences of actions, and implicit predictions of the proper actions to execute given a specific situation (Miall, 2003). 7

Levels 4 and 5 specify the need for inhibiting and monitoring the mirrored response and the predicted effects. If the observation of others’ actions is reflected by the covert activation of the motor system, it is in many situation advantageous to inhibit the action rather than to copy it. For example, automatically copying expressions of anger might lead to negative social and physical effects (Wood et al., 2016)1 . Monitoring the inhibited response provides information about the cause of the reactivation, originating from another agent. This monitoring enables the ability to distinguish the self from others. Monitoring the predicted input, meanwhile, may lead to more complex simulations that involve counterfactual thinking and other functions that resemble a theory of mind. In sum, the ability to distinguish between self and other, in Hurley’s model, derives from the ability to use embodied simulations to distinguish between actions of the self and actions of others. The use of own sensorimotor circuits in embodied simulations pertaining to actions of others brings us to so called social embodiment effects Barsalou et al. (2003): it follows that perceived social stimuli can produce bodily states and that bodily states can produce and affect emotional states, which suggests a critical role for immediate bodily signals in social interactions. An important aspect of these embodiment effects is that the state of the body (such as postures, arm movements and facial expressions) changes automatically without any conscious mediating of knowledge structures in specific instances of social interaction. As such, the link between mental and bodily states may provide a first basis for developing a Theory of Mind (ToM) – the ability for one agent to infer the mental state of another. For example, Lindblom & Ziemke (2006) linked the “nine-month revolution” in the social competence of infants to the emergence of crawling: suddenly, the infant is able to – through selfinduced locomotion – experience the world in a new way from which the ability to understand others as having “different perspective and intentions” can be 1 At

the same time, copying mechanisms does have benefits in certain situations, such as

when copying the behaviour of caretakers (Hurley, 2008).

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scaffolded. To truly understand another’s perspective, however, Lindblom & Ziemke (2006) further argue that it is necessary to develop embodied off-line simulations of what it would be like to be the other person. Simulations have in fact a long history as a hypothesis in in ToM research (Carruthers & Smith, 1996). Historically, research in this area has been dominated by two different approaches, “theory-theory” and “simulation theory”. Theory-theory argues that the mechanisms underlying ToM are based on a “folkpsychological theory” of the human mind in terms of structure and functionality (Carruthers & Smith, 1996). Simulation theory, on the other hand, disagrees with the use of a theory as the underlying mechanism, and instead argues that ToM is based on “an ability to project ourselves imaginatively into another persons perspective, simulating their mental activity with our own” (Carruthers & Smith, 1996, p.3). Simulation theory has received support from neuroscience studies on social cognition that emphasise the role of embodied simulations or bodily representations (Gallese & Goldman, 1998; Gallese & Sinigaglia, 2011; Goldman & de Vignemont, 2009). It has further been suggested that this type of embodied simulation mechanism may also underlie other cognitive abilities such as perception, mental imagery, memory and decision making Hesslow (2012); Svensson (2013). Gallese & Goldman (1998) point out that mind reading is not only about prediction but also about retrodiction in the sense of determining past mental states. They suggest that externally generated mirror neuron activity in humans can be a suitable mechanism for retrodicting the mental states of others, moving backwards from the observed action: when mirror neurons are activated by observing the other execute an action, the corresponding plans are inferred off-line (as postulated by simulation theory). The close correspondence to the other persons actions and the reuse (or reactivation) of the motor system enables the retrodiction (Gallese & Sinigaglia, 2011; Hurley, 2008), and this makes the connection to embodied simulation. Gallese & Goldman (1998), for example, argued that “[. . .] MN [mirror neuron] activity is not mere theoretical inference. It creates in the observer a state that matches that of the agent. This is how it 9

resembles the simulation heuristic.” (p. 498). Since then, the precise meaning of “matching” or simulation in these accounts has been subject to much discussion. Gallagher (2015) suggests that “[t]o get around problems with definitions that focus on either pretense or matching, the most recent shift in thinking about simulation has been to consider simulation as involving the re-use of either neural structures involved in our own bodily experiences, or motor-control mechanisms, where MNs are involved in this sort of reuse.” (p. 37). The focus on reuse (or reactivation, in terms of embodied simulation) in the context of mind-reading is used to emphasize that these processes are executed automatically without any higher level (personal/conscious level) control (Gallagher, 2015; Gallese & Sinigaglia, 2011). While retrodiction has been emphasized by some accounts, it is worth pointing out that other theories exist. Gallagher (2015), for example, argued that the basic mechanism for knowing about others’ actions might be the predictions themselves, rather than the reversal of predictions, that underlie the ability to understand others’ actions and intentions: rather than retrodicting through simulation, actions of others serve as a type of affordance that prime response actions of the self.

4. Embodied simulations in artificial cognitive systems Models of embodied simulations have been implemented in simple computational agents, robot simulations and robotic platforms. The mechanisms suggested by embodied simulation are often conceptualized as forward and inverse models in the robotic domain. Forward models predict the next state of the system based on the current state plus a motor command/signal. Inverse models take the current state of the system plus a goal (signal) as input and outputs the motor command(s) necessary to achieve the goal. By feeding the outputs of forward models to the inverse models a simulation loop is created where the predicted (sensory) state is used instead of actual states of the robot. In this sense, forward models relate to level 2 in combination with level 4 of Hurley’s (2008) model while inverse models also relate to level 3.

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It has been shown that level 2+4 simulations can be learned from the ”experienced” sensorimotor flow and consequently used to guide a robot’s behavour in the absence of sensory input (e.g. Baldassarre, 2003; Hoffmann & Mller, 2004; Jirenhed et al., 2001; Tani & Nolfi, 1999). For example, Gross et al. (1999) provide an example of how a simulating robot can use its predictions to provide better obstacle avoidance behaviour than possible with a reactive robot. Robotic work in the above sense focuses solely on interaction with the environment but not with other agents. Research that does typically uses other agents as a demonstrator for the robot to imitate, in line with the previously discussed level 3+4 simulations. A prominent example of this line of work is the HAMMER architecture (Demiris & Johnson, 2003): during observation of another agent, the motor commands generated by the inverse models in response to observed demonstrations are inhibited and instead fed to the forward models, which predict the next state of the observer. The error between the prediction and the actual state of the observer can be used to update the inverse models or even learn new models (Demiris & Johnson, 2003). Billing et al. (2016) develop a model based on human demonstration able construct internal simulations that both reproduce previous walks through an apartment environment but also constructs novel paths. Max the Mouse (Buchsbaum et al., 2005), an anthropomorphic animated character that uses its own motor system to interpret the behaviour of others, is another example of a level 3 model. Such mirroring activity can, according to Hurley (2008), occur at different parts of the behaviour generating systems which coincide with different levels of abstraction. This aspect of abstraction in embodied simulation has also been investigated in several robot experiments (Baldassarre, 2001; Holland & Goodman, 2003; Nolfi & Tani, 1999; Stening et al., 2005). In general terms, mirror systems have received much attention from modellers, even if these models were not always implemented in actual robots (for overviews of mirror neuron modelling, see Oztop et al., 2006; Thill et al., 2013). To move beyond imitative abilities, level 3-5 systems could provide the means for robots to both retrodict possible intentions and predict the consequences of 11

particular responses in social interaction. Thill & Vernon (2016), for example, discuss a framework for specifying the desired behaviour of social robots that uses forward and inverse models to predict the behaviours of other agents in response to the robot’s own actions and to retrodict the robot’s own actions given observed behaviours. Such forward models encapsulate ToM mechanisms while the inverse models allow the designer of the system to specify desired interactions (see Thill & Vernon, 2016, for details). This framework, however, has yet to be implemented in practice. More generally, Butz & Pezzulo (2008) concluded that there were no computational models that match the complex social abilities of our everyday life in 2008. Models have instead focused on simpler types of collective behaviours such as those of social insects. Butz & Pezzulo (2008) further conclude that to scale up current models of social interaction will likely require some type of anticipatory system. To the best of our knowledge, it still remains that anticipatory mechanisms – in particular in the form of embodied simulations – have rarely been used for the purpose of creating social artificial cognitive systems. Instead, a substantial share of research in social HRI is not concerned with the development of autonomous, socially interactive systems per se, but rather with principles of how such system could be designed and evaluated (see for instance the extensive survey in Goodrich & Schultz, 2007). An immediate realisation in such efforts is that there is no “one size fits all” solution; robots can interact with humans in a number of ways that then define and shape what one expects from such interaction. This then leads to a number of proposals for dimensions along which to rate the precise nature of the interaction at hand. The ubiquitous example is that of autonomy: Sheridan & Verplank (1978) proposed a 10-step scale describing degrees of automation, ranging from machines that are entirely remote-controlled to machines that ignore human beings altogether. Since then, there have been numerous discussions of the scale in particular and the concept of autonomy in HRI in general (e.g. Goodrich & Schultz, 2007; Yanco & Drury, 2004; Steinfeld et al., 2006; Thrun, 2004). Other metrics evaluate HRI performance in terms of task performance (e.g. robot efficiency and 12

effectiveness in the task and human situation awareness Steinfeld et al., 2006). Steinfeld et al. (2006) suggests that, at a minimum, metrics for evaluating HRI need to include interaction characteristics, persuasiveness, trust, engagement and compliance, but the exact methodologies for that remain unclear. How robots are perceived in the first place is another line of research in social HRI. For example, if, one expects, as in Hurley’s (2008) model, that social agents may activate mirror mechanisms, then it is an interesting degree to which robots do so. Research has thus shown, that, for some robots and actions at least, the human mirror system is activated when observing robot actions (Gazzola et al., 2007), and that these actions that can then be interpreted as being goal-directed (Sciutti et al., 2013). An implicit assumption in this is that humans would expect to interact with social robots as they do with other humans, but some researchers (e.g. Thrun, 2004) argue that this may not be the case and there have been several attempts at characterising the different ways one might characterise a social robot depending on its precise function. Dautenhahn (2003), for example, suggests “robot role” (ranging from tool/machine to companion/partner) as one of the dimensions for determining the requirements on a robot’s social skills, while Breazeal (2004) identified four relevant perspectives on the role of the robot (tool, cyborg extension, avatar, and sociable partner) in social HRI. Similarly, Thill & Ziemke (2015) argue that robots can be seen to varying degrees as tools and intelligent agents. Earlier work has shown argued that this is applicable to other types of artificial systems too: today’s increasingly automated vehicles can, for example, be perceived as both a tool used in navigation tasks, and as an intelligent agent with whom the driver collaborates in solving the task (Thill et al., 2014). The application domains for social robots also receive much attention, for instance in education (Kennedy et al., 2015) or robot-assisted therapy for children with autism spectrum disorder (Scassellati et al., 2012). In such cases, the focus is typically on what robot behaviours are and are not desired, how do these affect their human counterparts, and so on: it is therefore about mapping out the application domains for a relatively new technology. The robot controller itself 13

is however seldom the focus, and often operates on relatively simple scripted behaviours or even in a tele-operated manner (the so-called wizard-of-Oz technique often used in, for example, RAT, see Scassellati et al., 2012, for a review). While there are papers that discuss the need for an increased autonomy of such robots in interactive settings (Thill et al., 2012; Scassellati et al., 2012), including the need for, for example, ToM mechanisms, actual implementations are still largely missing and are either relatively simple proof-of-concept implementations (Scassellati, 2002) or make use of wearable sensors to gain access to indicators of affective states such as heart rate, blood flow, or galvanic skin response (e.g. Liu et al., 2008). To conclude this section, it is therefore apparent that work in social robotics and work on robotic implementations of embodied simulation still lead a largely separate existence. We therefore repeat the conclusion of Butz & Pezzulo (2008) that to scale up current models of social interaction will likely require some type of anticipatory system, but add that such systems could be built using embodied simulations, thereby allowing for a symbiotic effort with existing robotic work on such simulations that provide many pieces of the puzzle. In this paper, we have made that case by highlighting the foundational roles that embodied simulations play in human social cognition and social interaction. Overall, the case we make is therefore for the robots to build more complex social skills bottom-up, acquiring information from their embodied experiences, and learning from them (Gallagher, 2013), and specifically, that embodied simulations are a crucial aspect in this. At the same time, however, it is clear that social cognition and social interaction cannot be reduced entirely to embodied simulations and that while these may be necessary, they might not be sufficient. We have previously alluded to both prospection and episodic simulations as concepts that are related, but not the same, to the topics we have covered so far. The remainder of this paper is therefore dedicated to clarifying these concepts, in particular their connection to social cognition and relation with embodied simulations.

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5. Beyond embodied simulations: prospection and embodied simulations 5.1. Prospection in social interactions Humans spend a significant amount of time thinking about social interactions, for example, thoughts about one’s standing in the group or how one can navigate the social landscape at the workplace to advance the career ladder (Dunbar, 2004; Schilbach et al., 2008). However, without the ability to mentally travel back and forth in time, social conversations would be rather limited (cf. Sj¨ olander, 1995). To individually reason about social interactions or conversations, an agent must be able to remember episodic events (e.g. who did what to whom) as well as to predict possible reactions to actions, both from the agent itself and from other agents. This ability to think ahead and predict the behaviour of others has been extensively investigated – at least in lab situations – in psychological experiments on ToM (Carruthers & Smith, 1996) and, more recently, also in relation to prospection. In particular, it has recently been suggested that ToM (which we have discussed previously) and prospection share a common neural substrate (Buckner & Carroll, 2007). Prospection refers to the ability to think about the future and has been described as “the ability to represent what might happen in the future” (Szpunar et al., 2014), “[i]magining ourselves in the future” (Spreng et al., 2009), or “envisioning the future” (Buckner & Carroll, 2007). Consequently, prospection is when we mentally think about ourselves in a future situation. The episodic nature of prospection is critical: prospective thoughts are about particular episodes in the future, potentially signified by a what, a when and a how (like an episodic memory, see Buckner & Carroll, 2007). As such, prospection is distinct from a simple prediction of action outcomes. Prospection also does not have to be accurate – indeed it very often is not, due to the temporal scales involved, the inclusion of inherently unpredictable events, and cognitive limits. Humans frequently engage in prospection, sometimes more than one hundred times a day (D’Argembeau et al., 2011). An interesting developmental proposition is

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that the propensity to use prospective thought is influenced by parents talking to their children about future events (Hudson, 2006; Suddendorf, 2010), and that cultural artefacts related to time, such as clocks or calenders, may further scaffold such thinking (Suddendorf & Redshaw, 2013). Importantly, prospection involves not only one’s own behaviour, but also what other people in that situation are thinking, feeling and doing, possibly even to a greater extent than when it comes to planning how to act to achieve a certain goal together or in competition with other social agents. Prospection therefore also plays a crucial role in social cognition. Schilbach et al. (2008), for example, argue that “social cognition can be thought to be at the core of abilities which are most often studied as distinct: thinking about the future, remembering the past and understanding other minds can be conceptualized as related to forms of ‘self-projection’ during which self-referential information from past experiences is used to imagine events and perspectives beyond those that are immediately available (Buckner & Carroll, 2007)” (p.463). Similarly, Suddendorf & Corballis (2007) and others have argued that the need for cooperation and competition within the human race might have been the driving force behind the evolution of human-level abilities for prospection, theory of mind, and language (see also Dunbar, 2004; Whiten & Byrne, 1997). Prospective thoughts are episodic – as noted early on, they are explicit, conscious, and/or, deliberate mental simulations that should not be confused with embodied simulations. It is therefore important to clarify what episodic simulations are in this context, and how they related to embodied simulations. 5.2. Episodic memories and episodic simulations in the context of prospection Szpunar et al. (2014) present a two-dimensional taxonomy2 of different modes of thinking about the future and types of knowledge. The latter represents the well-known distinction between episodic and semantic memories while 2 for

a similar taxonomy in the form of a theatre metaphor, see Suddendorf & Corballis

(2007).

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the first dimension covers four different modes of thinking: simulation, prediction, intention, and planning. We next discuss these in more detail, but restrict ourselves to their episodic modes. • Episodic simulations are defined by Szpunar et al. (2014), following Ingvar (1979), as “an inner anticipatory programming of several alternative behavioral modes prepared to be used depending upon what will happen”. This is similar to the idea of a “virtual space in our minds able to entertain offline representations” (Suddendorf & Corballis, 2007; Suddendorf & Redshaw, 2013). Episodic simulation, in this sense, refers to the phenomenological sense of being able to imagine yourself in the future, including both the ability to generate sensory percepts in different modalities and connect these into a coherent and complex situation description (Hassabis & Maguire, 2007), and the ability to project yourself into the future (Buckner & Carroll, 2007). At the center of prospection is the ability to fully decouple the sensory organs from the current situation and create an alternative (virtual) world. • Episodic Prediction is “the estimation of the likelihood of and/or one’s reaction to a specific autobiographical future event.” (Szpunar et al., 2014, p. 18417). An important aspect of such predictions is that they do not start from the present; rather, the agent instantly mentally travels forward to a particular situation. Further these predictions can concern both behaviour and affective response. Gilbert & Wilson (2009) term the latter a premotion: the ability to predict affective states from simulations (previews, in their terms) of future events. Importantly, Gilbert & Wilson (2009) note that prospections and premotions are fallible because: (1) they are not representative but often based on simplifications such as relying on the availability heuristic, where representativeness is influenced by the ease of recollection; (2) they are essentialized, that is, reduced to the central gist, but without other details (in fact, Gilbert & Wilson (2009) point to studies that show how inducing more detail in prospections in 17

turn produces better premotions). (3) They are truncated, meaning that prospections appear centred in time around the first occurrence of the emotional event, leading to an impact bias – the tendency to overestimate the emotional impact of a negative event (premotion) and failing to adjust the prediction of the emotional reaction to one’s adaptation to the new situation. • Episodic intentions refer to the ability to set a goal in relation to a simulation of a situation in the future. Szpunar et al. (2014) note that prospective memories are a form of episodic intentions: they are the ability to remember to do something in the future. However, it is interesting to note that while prospective memories are often scaffolded by cues in the environment (such as seeing an unopened envelope on the desk, a post it note on the wall, smelling the burned butter in the frying pan Dix et al. (see e.g. 2004)), this is not true of episodic intentions, which function by means of internal cues only. • Episodic Planning is the ability to determine the steps needed to achieve a goal in a future situation (Szpunar et al., 2014). Such planning has been studied both in well defined laboratory tasks such as the Tower of Hanoi, and more realistic tasks such as planning a meal (Szpunar et al., 2014). As with prospective memory, it has been shown that the activity of planning and preparing a meal makes use of cues in the environment (Kirsh, 1995). It may be the case that prospections and episodic memory encodes these cues explicitly. Episodic memories seem to encode additional aspects when memorizing an object such as sensorimotor patterns as shown by the enactment effect (Nyberg et al., 2001) and it could be extended to other aspects of the physical environment, even though they are not consciously remembered. Overall, there is therefore a clear link between prospection and episodic memory, and this has been extensively explored (see, for example, Schacter & Addis,

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2009; Suddendorf & Corballis, 2007; Suddendorf, 2010). It has been argued that episodic memories share a common neural substrate with prospection, including functional and phenomenological similarities. Episodic memories merely operate in the “opposite” direction, involving mental travel backwards in time to (re)play a version of previously encountered situations that may or may not be an accurate recall of what actually happened. Equally, an agent can travel back in time to actively reconstruct a scene according to a particular goal, such as counterfactually reasoning about how a situation might had played out under different conditions (Van Boven et al., 2008). Additionally, episodic memory and prospection have also been implicated in navigation, theory of mind, and, on a neural level, related to the default mode network (Hassabis & Maguire, 2007). There are two defining aspects of episodic memory and prospection that are worth discussing further: imagery3 and the decoupling of the self from the present. Both aspects highlight the constructive aspects of episodic memory and prospection; to project oneself to the future or past is not a replay of sensory impressions, but it is the creation of a new, imagined, situations. Additionally, prospection involves the ability to differentiate between possible and actual actions, as well as between self and other. These might be evident and necessary for prospection to emerge but, as argued in the previous section, they are also the defining aspect of other types of (embodied) simulation grounded in evolutionarily older areas of the human brain. 5.3. Episodic and embodied simulations The exact relationship between embodied simulations and abilities such as prospection remains an open research question. In the above, we have highlighted in particular that, phenomenologically, the construction of a coherent 3 It

is worth noting that this type of mental imagery is somewhat special because the

imagery is formed into a coherent story that makes sense to the person (Hassabis & Maguire, 2007).

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visual scene is a defining aspect of prospection while embodied simulations are not necessarily accompanied by conscious imagery but are automatically elicited subconscious processes, aiding, for example, perception (M¨oller, 1999). In terms of the different levels of analysis used early on to introduce embodied simulation, at least some of the embodied simulations are dependent, in neural terms, on supervised learning mechanisms in subcortical structures (Svensson, 2013; Svensson et al., 2009). Episodic simulations, on the other hand, require the use of hippocampal or other neocortical systems capable of flexibly associating different kinds of neural patterns (Hassabis & Maguire, 2009). Functionally, episodic simulations involve predictions that often occur at time frames and complexities, which restrict the precision of the predictions (Gilbert & Wilson, 2009). In contrast, embodied simulations may often involve predictions of a higher level of accuracy. These include forward models closely tied to bodily aspects (Svensson et al., 2009), where the learning mechanisms involved ”yields procedurally predictive controllers that are adapted to the inherent sensory and motor delays of the organism.” (Downing, 2009, p. 54). The close correspondences observed between action planning and execution and mental motor imagery in terms of neural, physiological, behavioural and psychological effects (Grush, 2004; Svensson, 2013) suggest that the high degree of accuracy of the forward model predictions are reflected in some cognitive functions as well. Episodic simulations thus add flexibility to embodied simulations, enabling humans to incorporate diverse aspects of their autobiography into thoughts about the future. Episodic simulations also add the ability to arbitrarily situate simulations to a particular time and place (whether in prospective thought or episodic memory). Although episodic simulations and resulting abilities like prospection are likely to involve embodied simulations at some level, it therefore seems likely that they add a qualitatively different set of characteristics – embodied simulations may be necessary, but are not sufficient for episodic thought.

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6. Conclusions We have presented a review of embodied simulations from the perspective of social artificial cognitive systems. We have first shown that embodied simulations are foundational to human social cognition and interaction. We have then highlighted that most current work on robotic implementations of embodied simulations focusses on their predictive mechanisms and typically only involves interactions with other agents in scenarios of learning by demonstration. There are currently no social robots that derive their abilities from embodied simulation mechanisms. We have argued, by drawing parallels with Hurley’s (2008) model, that this might be a fruitful role to go. We have then discussed mechanisms that explicitly go beyond embodied simulations but are nonetheless relevant for social interaction, namely episodic simulations that underlie prospective thought and episodic memory. We have noted, in particular, that prospection appears to share a neural substrate with theory of mind. Although these mechanisms clearly go functionally beyond those of embodied simulations, they may nonetheless rely in part on them; again highlighting the potential benefit of using models of embodied simulation in scenarios that go beyond mere bodily anticipation. As argued earlier, existing models already provide many pieces of this puzzle; the present challenge is to understand how to integrate them for the benefit of socially interactive artificial cognitive systems. Characterisations such as Hurley’s (2008) model might provide a good starting point. References Baldassarre, G. (2001). Coarse planning for landmark navigation in a neuralnetwork reinforcement-learning robot. In Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on (pp. 2398– 2403). IEEE volume 4. Baldassarre, G. (2003). Forward and bidirectional planning based on reinforcement learning and neural networks in a simulated robot. In M. Butz, V., 21

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