Emotion Perception and Elicitation

Emotion Perception and Elicitation E Meaux and P Vuilleumier, University of Geneva, Geneva, Switzerland ã 2015 Elsevier Inc. All rights reserved.

Glossary Componential theories Consider that emotions result from a combination of multiple appraisal processes that evaluate the meaning of events in relation to both external and internal factors, including pleasantness but also novelty, current goals, personal values, social norms, and other cognitive processes. Such models thus make an explicit distinction between different components of emotions, including motor expression, peripheral autonomic changes, motivational drive, and cognitive, attentional, and memory effects, in addition to subjective feeling state (Ellsworth & Scherer, 2003; Sander, Grandjean, Kaiser, Wehrle, & Scherer, 2007; Sander, Grandjean, & Scherer, 2005; Scherer, Schorr, & Johnstone, 2001). This view also highlights the role of different aspects of cognition in the shaping of emotion, as well as a recursive temporal dynamics of interactions between emotion and cognition. This view provides an account for the malleability and high degree of qualitative differentiation of emotional experiences across different situations and different individuals. Dimensional theories Conceptualize emotions as arising from combinations of essential dimensions along continuous gradients, particularly emotional arousal (state of alertness) and emotional valence (degree of pleasantness

Introduction Although the study of emotions and their biological function has been a topic of interest since at least the nineteenth century (e.g., Darwin, James, and Freud’s work), neuroscience approaches have flourished much more recently. Because emotions were often defined in relation to their subjective and private experiential aspects, they were long considered intractable for scientific investigation. Functional neuroimaging techniques greatly contributed to invigorate research on the neurobiology of emotions and give new credence to it, by making covert brain processes and even unconscious activity visible, so that affective phenomena could now become a target of more objective inquiry. In the last 20 years, there has been an ever-growing amount of neuroimaging studies on emotion and related fields, such as motivation, reward, pain, social cognition, and economic decision making (to name a few), further amplified by increasing realization that emotional processes are intimately intertwined with many other mental functions, challenging a classic divide between cognitive and affective sectors of the mind (Damasio, 1994).

Brain Mapping: An Encyclopedic Reference

or unpleasantness) (Barrett, 1998; Gable & Harmon-Jones, 2010; Gerber et al., 2008; Lang & Bradley, 2010; Russell, 2003; Russell & Barrett, 1999). This view also underlies a “constructivist” view of emotion where basic psychological processes may combine in various ways with more abstract conceptual (e.g., verbal) representations to produce distinct emotional states. Discrete theories Contend that there is a limited set of separable, basic emotions that are the most elemental and adaptive, culturally universal, and that have an inherited, biological basis in the brain (Darwin, 1872; Ekman, 1972; Ekman & Cordaro, 2011; Izard, 1993). Multivariate pattern analyses (MVPA) A neuroimaging analysis method based on pattern-classification algorithms. Unlike standard univariate analysis, MVPA does not involve averaging over voxel intensities, but instead measures the distributed profile of relative activity evoked in different voxels within a given brain region. This technique is sensitive to fine-grained neural representations and can be used to test, for example, whether different conditions recruit similar or different neuronal populations with the same region, reflected by separable patterns of activity at the voxel by voxel level.

As a consequence, charting the functional neuroanatomy of emotional processing covers most of the brain to some degree. Traditional neuroanatomy has often linked emotions with a specific set of regions referred to as the ‘limbic system,’ somewhat uniquely connected to information from both the external world and internal milieu, but the delineation of this core system is vague and arbitrary (Ledoux, 2012). In addition to the so-called ‘limbic’ areas, emotional processing involves various brain regions implicated in perception, memory, action, attention, and autonomic functions, each recruited to different degrees according to the type of emotions and the modality by which emotions are elicited or expressed. Indeed, emotions are generally studied by exposing people to various stimuli, from different sensory channels and with different affective meanings, leading to various patterns of brain activity that are partly shared and partly specific to these different conditions. This article will therefore review current brain mapping approaches to emotion in humans by describing neural systems engaged by specific types of stimuli and specific types of emotions separately. We will focus on fMRI, although other methodologies provide highly valuable and complementary insights, such as EEG for tracking the time course of

http://dx.doi.org/10.1016/B978-0-12-397025-1.00159-7

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emotional responses (Junghofer, Peyk, Flaisch, & Schupp, 2006; Meaux, Roux, & Batty, 2013; Pourtois, Delplanque, Michel, & Vuilleumier, 2008) and positron emission tomography (PET) for examining the release of specific neurochemicals like opioids (Koepp et al., 2009) and dopamine (Kienast et al., 2008). For more detailed reviews, see Armony and Vuilleumier (2013).

The Nature of Emotion: From Discrete and Dimensional to Componential Models Before characterizing their neural substrates, it is useful to ask how emotions are defined and distinguished from other mental phenomena. This question actually remains debated and no single unanimous answer exists (Russell & Barrett, 1999). A classic model postulates distinct circuits for a small number of basic and universal emotions, such as fear, anger, sadness, disgust, happiness, surprise (Ekman & Cordaro, 2011). (Note: Much longer ago, philosophical analysis already led to a distinction of six primitive emotions: admiration, love, hatred, desire, joy, and sadness (Descartes, 1649) (Art. 69).) Others propose even fewer elementary circuits essential for survival needs, such as fear, desire, aggression, and separation (Panksepp & Watt, 2011), upon which more complex ‘secondorder’ emotions are built through learning and culture (Izzard, 2011). Another class of theories (Lang & Bradley, 2010; Rolls, 2014; Russell & Barrett, 1999) conceptualize emotions as arising from the combination of two fundamental orthogonal dimensions, namely, arousal (from calm to excited) and valence (from pleasurable to aversive), sometimes with one or more additional factors such as control, predictability, social context, and temporal perspective (Fontaine, Scherer, Roesch, & Ellsworth, 2007; Kassam, Markey, Cherkassky, Loewenstein, & Just, 2013), whereas still others emphasize the role of action tendencies (Frijda, 2010), such as avoidance and approach. Finally, componential theories consider that emotions result from a combination of multiple appraisal processes that evaluate the meaning of events in relation to both external and internal factors, including pleasantness but also novelty, current goals, personal values and social norms, and other cognitive processes (Ellsworth & Scherer, 2003). Such models thus make an explicit distinction between different constituents of emotions, including motor expression, peripheral autonomic changes, motivational drive, and cognitive, attentional, and memory effects, in addition to subjective feeling state. While the latter component is the most difficult to approach with neuroscience methods (particularly in animals; cf. Ledoux, 2012), other components are amenable to objective measurements and can be related at least partly to well-defined brain systems (Hamann, 2012). These theoretical models have strongly influenced empirical approaches and interpretations of neuroimaging studies. Basic theories inspired early work trying to map particular emotions onto distinct brain modules, for example, fear in the amygdala, pleasure in the striatum, and disgust in the insula (Calder, Lawrence, & Young, 2001), whereas others linked arousal and valence dimensions to specific structures such as the amygdala and orbitofrontal cortex (OFC), respectively (Anderson et al., 2003). However, it is increasingly recognized that many of the same brain areas are recruited across

different categories or dimensions of emotions, such that their role must be understood in broader terms. Thus, current views may better accord with componential models, whereby each emotion constituent reflects the engagement of distributed circuits that mediate adaptive responses to environmental challenges and partly overlap with more general-purpose functions (Hamann, 2012; Ledoux, 2012; Sander, Grandjean, Kaiser, Wehrle, & Scherer, 2007). Yet, still little research has been conducted to systematically investigate emotions from a componential perspective, as opposed to the wealth of studies embracing discrete or dimensional approaches. Furthermore, in species like humans and other primates, strong motivational incentives and emotional experiences are associated with social interactions throughout life. The importance of social demands in humans may explain not only a considerable development and efficiency of social information processing systems but also a remarkable overlap between social and emotional brain networks (Lieberman, 2007). Thus, affective functions are intimately connected to neural mechanisms mediating both expression and perception of social signals of emotions, for example, through the face, voice, or gestures. Accordingly, emotional signals from social cues are powerful stimuli that have been widely used to study emotional processing in human neuroimaging studies, as described in the next sections.

Emotion Perception in Different Modalities Emotions from Vision As they provide a wealth of emotional information that is perceived swiftly and effortlessly, faces are extensively used to elicit emotional responses. Face processing recruits a highly organized, right-hemisphere-dominant network including visual areas in not only the fusiform gyrus and occipital cortex, primarily involved in extracting shape cues critical for identity recognition, but also the amygdala and higher-level cortices such as the superior temporal sulcus (STS), medial prefrontal cortex (mPFC), OFC, and inferior fusiform gyrus (IFG) (refer to Table 1 for abbreviations used throughout the text) that have a more general role in processing emotional and social information. The latter regions therefore also respond to emotion expressed by other social stimuli (e.g., bodies or voices; Peelen, Atkinson, & Vuilleumier, 2010) or nonemotional social signals (e.g., gaze directions; N’Diaye, Sander, & Vuilleumier, 2009). The right STS is particularly sensitive to dynamic face cues and has strong direct projections to the right IFG (Ethofer, Gschwind, & Vuilleumier, 2011; Gschwind, Pourtois, Schwartz, Van De Ville, & Vuilleumier, 2012). Emotional responses in face-responsive areas are generally observed irrespective of task demand, even when observers judge another aspect of faces (e.g., gender), but explicit attention to expression further recruits STS and prefrontal areas involved in emotion categorization (Critchley et al., 2000; Winston, O’Doherty, & Dolan, 2003), and task demands can modulate connectivity patterns between face processing regions (Fairhall & Ishai, 2007). Different areas may encode facial expressions as perceptual continua or discrete categories, as found for the STS and amygdala, respectively (Harris, Young, & Andrews, 2012). Fusiform activation is also enhanced by emotional relative to neutral

INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE | Emotion Perception and Elicitation

Table 1

Abbreviations

Brain regions Occipital IOG latOCC MTþ V1 V4 V8 Temporal IFG MTG STG STS TC TP TPJ Frontal dfrOp dmPFC frOP Inf.PFC MFG mPFC OFC pOFC Pre-SMA vmPFC vlPFC Limbic Amy ACC Hy HCMP PCC pgACC rdACC Thal sgACC Basal ganglia (BG) dPut vStr vGP Insular aINS vaINS midIns Midbrain PAG

Inferior occipital gyrus Lateral occipital cortex Visual association area V5 Primary visual cortex Visual association area 4 Visual association area 8 Inferior fusiform gyrus Middle temporal gyrus Superior temporal gyrus Superior temporal sulcus Temporal cortex Temporal pole Temporoparietal junction Dorsal frontal operculum Dorsomedial prefrontal cortex Frontal operculum Inferior prefrontal cortex Medial frontal gyrus Medial prefrontal cortex Orbitofrontal cortex Posterior orbitofrontal cortex Presupplementary motor area Ventromedial prefrontal cortex Ventrolateral prefrontal cortex Amygdala Anterior cingulate cortex Hypothalamus Hippocampus Posterior cingulate Pregenual ACC Rostrodorsal ACC Thalamus Subgenual ACC Dorsal putamen Ventral striatum Ventral globus pallidus Anterior insula Ventral anterior insula Middle insula Periaqueductal gray

Note. Abbreviations for brain regions are organized by anatomical structures and in alphabetical order.

expressions, an effect due to feedback signals from the amygdala boosting attention and perceptual encoding (Vuilleumier, Richardson, Armony, Driver, & Dolan, 2004), whereas repetition suppression occurs when the same emotion is presented repeatedly across different face identities (Fox, Moon, Iaria, & Barton, 2009), suggesting that this region may contain neuronal populations selective for visual features associated with specific expressions. It remains unresolved whether emotional responses to faces are driven by particular features (e.g., eyes for fear; Whalen et al., 2004), combination of features (Smith et al.,

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2008), or holistic configural information (Calder, Young, Keane, & Dean, 2000; Tanaka et al., 2012). However, faceresponsive areas show greater responses to all emotions (Winston et al., 2003), though fear often produces stronger effects than other expressions (Surguladze et al., 2003). Emotion-specific responses may arise outside the face processing network, such as the insula for disgust (Phillips et al., 1998) or the striatum and presupplementary motor area (pre-SMA) for smiling (Krolak-Salmon et al., 2006; Vrticka, Andersson, Grandjean, Sander, & Vuilleumier, 2008). Expression-specific patterns were found in the fusiform cortex using multivariate pattern analyses (MVPA) (see Glossary for definition) in some studies (Harry, Williams, Davis, & Kim, 2013) but not others (Peelen, Atkinson, Andersson, & Vuilleumier, 2007; Peelen et al., 2010). Emotion-specific patterns are also observed with MVPA in the posterior STS and mPFC (Said, Haxby, & Todorov, 2011), reflecting supramodal representations equally activated by vocal and bodily expressions (Peelen et al., 2007, 2010). These supramodal activations might relate to general mentalizing functions in the STS (i.e., theory of mind) and more specific affective representations in the mPFC (Corradi-Dell’acqua, Hofstetter, & Vuilleumier, 2013). Several meta-analyses show that both ventral PFC (Murphy, Nimmo-Smith, & Lawrence, 2003; Phan, Wager, Taylor, & Liberzon, 2002) and inferior PFC (Fusar-Poli et al., 2009; Montgomery & Haxby, 2008; Sabatinelli et al., 2011) respond not only to emotional faces across many different emotion categories and tasks but also to other emotional stimuli, suggesting a general role in appraisal and cognitive control processes integrating affective information with contextual or goalrelated information. Emotional gestures (hands and postures) recruit specific visual regions in the lateral occipital and fusiform cortex (the extrastriate body area and fusiform body area) that extract body-shape information (Peelen & Downing, 2005) and show selective increases to bodily but not facial expressions correlating with amygdala responses (Peelen et al., 2007). Face and body-responsive clusters in fusiform cortex appear closely juxtaposed but nonoverlapping (Weiner & Grill-Spector, 2010). In addition to social–emotional networks shared with face processing, bodily expressions of emotion often activate premotor areas presumably associated with covert mirror motor response or defensive reactions (Pichon, De Gelder, & Grezes, 2008). Visual scenes are also frequently used to evoke positive or negative emotions by depicting various events or objects (accidents, mutilations, spiders, erotica, cute babies, etc.). These produce widespread activations consistent with their complex content, including in the ventral and lateral occipitotemporal areas and the parietal areas linked to attention and eye movements, in addition to the amygdala, insula, anterior cingulate, and medial and lateral prefrontal areas associated with emotional processing (Britton et al., 2006; Sabatinelli et al., 2011). Subcortical regions such as the pulvinar and mediodorsal thalami are also implicated (Sabatinelli et al., 2011). Thus, the nature of stimuli has a considerable impact on emotion responses, even within a single (e.g., visual) modality and after removing basic sensory effects through subtractive contrasts (Sabatinelli et al., 2011), because distinctive perceptual, motor, or cognitive systems are engaged by different emotional stimuli. While this makes emotion-related and

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INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE | Emotion Perception and Elicitation

category-related effects difficult to fully disentangle, it illustrates the intimate reciprocal links of affective processes with other cognitive domains.

Emotions from Audition Similar in many ways to faces, human voices convey powerful affective signals through the prosody of speech (even without semantic content) or simple vocalizations (Ethofer et al., 2012; Fruhholz & Grandjean, 2013; Kotz, Kalberlah, Bahlmann, Friederici, & Haynes, 2012). Not only are auditory regions in the STG specialized for processing human voices relative to other sounds (with right-hemisphere dominance) (Belin, Zatorre, Lafaille, Ahad, & Pike, 2000), but also their activity is enhanced by emotional content such as laughing, crying, or speech prosody (Fecteau, Belin, Joanette, & Armony, 2007; Grandjean et al., 2005), a boosting again attributed to amygdala feedback. MVPA has identified activation patterns in the auditory cortex corresponding to specific emotion types across different voice identities (Ethofer, Van De Ville, Scherer & Vuilleumier, 2009), probably reflecting distributed representations of distinctive acoustic features associated with each emotion. Like for faces, the exact acoustic cues of vocal emotions and the nature of their representation in different brain areas remain unknown. The STG appears more responsive to affective and communicative signals than other vocal sounds such as coughing and sneezing (Shultz, Vouloumanos, & Pelphrey, 2012). Also like faces, vocal emotions activate the amygdala as well as the right IFG, mPFC, and OFC, most often equally across various emotion categories (Ethofer et al., 2012; Peelen et al., 2010). The right IFG/OFC (Ethofer, Kreifelts, et al., 2009; Mitchell, Elliott, Barry, Cruttenden, & Woodruff, 2003) is particularly implicated in explicit evaluation of affective prosody, rather than speech content. Emotion-specific activations to fear, disgust, or laughter are generally not found in other brain regions (Phillips et al., 1998; Sander & Scheich, 2001; Szameitat et al., 2010). Unlike faces, however, emotional voices often activate the basal ganglia (BG), which might relate to the perceived rhythmicity of speech or vocalizations (Grahn & Brett, 2007). Distributed networks are similarly engaged by environmental sounds evoking emotionally salient events (e.g., gunshots). Music is another auditory stimulus that expresses and/or elicits strong emotions. Beyond basic categories such as fear, sadness, and joy, it can produce more complex affective responses including nostalgia, wonder, or tenderness (Trost, Ethofer, Zentner, & Vuilleumier, 2012). Pleasant and arousing music accompanied with chills activates the striatum and the ventral tegmental area linked to reward and dopamine release, as well as the insula (Salimpoor, Benovoy, Larcher, Dagher, & Zatorre, 2011). Negative and low-arousing music activates the perihippocampal and ventromedial prefrontal regions (Koelsch, 2010; Trost et al., 2012), including a response to sad melodies in a subgenual cingulate area implicated in depression (Mayberg et al., 1999). The amygdala responds to various musical conditions including scary (Gosselin, Peretz, Hasboun, Baulac, & Samson, 2011), sad (Lerner, Papo, Zhdanov, Belozersky, & Hendler, 2009), and joyful music (Koelsch, 2010); unpleasant chords with harmonic violations (James, Britz, Vuilleumier, Hauert, & Michel, 2008); or tension (Lehne, Rohrmeier, &

Koelsch, 2013). Cortical motor areas and the BG are engaged by music with strong beats that elicit positive feelings of power (e.g., military march) or negative but arousing feelings of tension (Grahn & Brett, 2007; Trost et al., 2012). This conjoint recruitment of brain networks involved in memory, self-reflective, and sensorimotor processes, combined with activity in emotional and reward systems, may account for the richness of musical emotions.

Emotion from Other Sensory Modalities Emotional experience often originates from multisensory channels, and combining different modalities alters the processing of unimodal information. Bimodal presentation of facial and vocal expressions enhances their perceived emotion intensity relative to unimodal presentation, in parallel with greater responses in the amygdala, rostral anterior cingulate cortex (ACC), and OFC, as well as visual and auditory areas (Dolan, Morris, & De Gelder, 2001; Park et al., 2010; Pourtois, De Gelder, Bol, & Crommelinck, 2005), especially when expressions in each modality are congruent (Klasen, Kenworthy, Mathiak, Kircher, & Mathiak, 2011). Audiovisual integration may arise in an intermediate sector of the STS, between unimodal face- and voice-responsive sectors (Kreifelts, Ethofer, Shiozawa, Grodd, & Wildgruber, 2009). Incongruent emotion pairing also increases sensory analysis in the STS/MTG (Watson et al., 2013), together with a recruitment of attentional and conflict monitoring processes in the dorsal ACC and frontoparietal networks (Klasen et al., 2011; Watson et al., 2013). Multimodal information from video clips (Eryilmaz, Van De Ville, Schwartz, & Vuilleumier, 2011) or pairing visual scenes with music (Baumgartner, Lutz, Schmidt, & Jancke, 2006; Eldar, Ganor, Admon, Bleich, & Hendler, 2007) also produces vivid emotional experiences where the content in one modality can be boosted or modified by the other modality (Pehrs et al., 2013). Different emotions recruit partly separate networks integrating multimodal information, such as the amygdala or insula, in addition to areas implicated in nonemotional multisensory integration (Klasen et al., 2011; Park et al., 2010). Among other sensory modalities, taste and smell are powerful emotion-eliciting stimuli through their direct access to the amygdala and OFC (Rolls, 2014). While distinct parts in the OFC may code for valence (pleasant vs. unpleasant) of both odor and taste (Anderson et al., 2003; Small et al., 2003), amygdala and insula responses predominantly reflect the stimulus intensity or arousal effects interacting with valence (Winston, Gottfried, Kilner, & Dolan, 2005). It is still debated to what extent pleasantness is determined by the molecular structure of odorants, rather than by learned associations (Khan et al., 2007). Verbal labels (De Araujo, Rolls, Velazco, Margot, & Cayeux, 2005) or audiovisual information (Gottfried & Dolan, 2003) associated with an odor can modify emotional responses through top-down effects, such that the same stimuli are perceived as either appetizing (e.g., cheddar cheese) or disgusting (e.g., body sweat), in parallel with modulations of the OFC, amygdala, and hippocampus. In the somatosensory domain, noxious tactile stimuli produce pain whose unpleasantness is associated with insula and

INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE | Emotion Perception and Elicitation

anterior cingulate activation, although pain is classically not considered as an emotion in itself (Craig, 2003). In contrast, soft touch with slow strokes over hairy skin evokes pleasant caress-like sensations, conveyed through slow unmyelinated fibers (Loken, Wessberg, Morrison, Mcglone, & Olausson, 2009), which produce distinct activation patterns in the posterior insula and S1 (Gazzola et al., 2012; Mcglone et al., 2012) as well as the OFC and striatum (Rolls, 2014) relative to normal touch. Contextual factors (e.g., belief that pleasant touch is delivered by an opposite-sex person) can also modulate these responses (Gazzola et al., 2012).

Emotion from Memory and Imagination Besides external sensory stimuli, imagination of personal memories (Damasio et al., 2000) or fictive scenarios (Kassam et al., 2013) associated with emotional events recruits distributed brain areas that are implicated in processing interoceptive and somatic information as well as motivational signals, such as the insula, somatosensory cortices, OFC and ventromedial prefrontal cortex (vmPFC), posterior and anterior cingulate, striatum, and other subcortical structures in brain stem or hypothalamus. These activations demonstrate an important role in the experiential component of emotions (i.e., feeling states), which can be generated even without external inputs. Reliving different emotions produces partly segregated activation patterns, for example, increases in subgenual cingulate for happiness and sadness but decreases in the mPFC and increases in the dorsal ACC for fear and anger (Damasio et al., 2000), while there is substantial overlap in the anterior insula (aINS) across many emotion types. This overlap accords with a central role for the aINS in both current and predicted feeling states (Haddad et al., 2009; but see Damasio, Damasio, & Tranel, 2013). Scenario-based elicitation allows studying more complex emotions beyond basic categories, particularly related to social and moral values such as pride, guilt, shame, embarrassment, and indignation (moral disgust). Such emotions consistently activate the dorsomedial prefrontal cortex, temporoparietal junction (TPJ), STS, and temporal pole regions, thought to subtend theory of mind and social knowledge. Accordingly, social emotions require the anticipation of thoughts and intentions of people, whenever they are the cause or target of positive or negative actions from others (Kedia, Berthoz, Wessa, Hilton, & Martinot, 2008; Moll et al., 2005; Takahashi et al., 2004, 2008; Wagner, N’Diaye, Ethofer, & Vuilleumier, 2011). In addition, selective increases occur in the lateral OFC for guilt (Wagner et al., 2011) or indignation (Moll et al., 2005) relative to other negative emotions such as sadness or physical disgust, respectively. The lateral OFC also activates in self-reflective emotions associated with social rejection (Eisenberger, Lieberman, & Williams, 2003) and regret (Coricelli et al., 2005), suggesting a more general involvement in negative feeling states that guide social behavior and decision making (Rilling, King-Casas, & Sanfey, 2008). These data converge with neuropsychological observations that damage or dysfunction affecting the OFC/ vmPFC may cause severe disturbances in moral behavior and psychopathy (Damasio, 1994).

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Emotion-Specific Systems Anatomical Segregation of Emotional Responses In line with basic emotion theory (Ekman & Cordaro, 2011), many studies attempted to identify specific brain substrates for particular emotions (e.g., fear and happiness). However, few studies compared all basic emotions in a single experiment. Furthermore, most used face stimuli and did not test for emotion responses generalizing across sensory modalities. A classic notion is that perceiving fear consistently activates the amygdala, in agreement with its key role in fear conditioning and anxiety-related behaviors (Adolphs et al., 2005; Mineka & Ohman, 2002). However, the amygdala also activates to positive stimuli, reward, or funny jokes (Derntl et al., 2009; Schwartz et al., 2008). It was therefore proposed that it might code for arousal rather than valence (Anderson et al., 2003; Zald, 2003), in accord with dimensional models of emotions. Yet, faces expressing emotion with equal arousal levels (e.g., fear and anger) produce differential amygdala response (Whalen et al., 2001); and low-arousal emotions like sadness (Blumberg et al., 2005; Levesque et al., 2003; Posse et al., 2003) and even neutral faces (Iidaka et al., 2002) also recruit this region. Thus, although the amygdala may be preferentially or differentially sensitive to threat-related cues, there is compelling evidence that it has a broader function, presumably related to affective relevance detection and learning (Sander, Grafman, & Zalla, 2003). Accordingly, pictures with subjective ‘impact’ produce stronger amygdala responses than mundane scenes regardless of valence or arousal (Ewbank, Barnard, Croucher, Ramponi, & Calder, 2009). Disgust is another basic emotion repeatedly associated with distinctive activations in the aINS (Jabbi, Bastiaansen, & Keysers, 2008; Jehna et al., 2011) and BG (putamen and caudate nuclei) (Gorno-Tempini et al., 2001; Sprengelmeyer, Rausch, Eysel, & Przuntek, 1998). The aINS is activated by seeing disgusted faces as well as disgusting scenes or self-experience of disgust (Wicker et al., 2003). However, like the amygdala, it activates to a variety of other conditions including anger, fear, pain, and even happiness (Schienle et al., 2002) but also error detection and rhythm perception (Craig, 2009). This region may have a more general role in representing bodily states and internal visceral sensation (interoception) and/or generating predictive feeling states that guide adaptive responses to behaviorally salient events (Singer, Critchley, & Preuschoff, 2009). High interconnections of aINS with BG might subserve motor avoidance evoked by disgust and/or participate to other emotions linked to disgust such as contempt. Other basic emotions have been less studied. Happiness, smiles, or laughs often activate the rostral ACC (Murphy et al., 2003; Vytal & Hamann, 2010), pre-SMA (Fried, Wilson, MacDonald, & Behnke, 1998; Krolak-Salmon et al., 2006), and striatal regions associated with dopaminergic reward pathways (Delgado, 2007; O’Doherty, 2012). However, ventral striatum is also involved in processing negative information (Calder, Keane, Lawrence, & Manes, 2004; Klucken et al., 2009), reflecting a more general role in prediction error and contingency learning. Occipital and posterior parietal areas and the STG are also frequently activated by happy faces (Kesler-West et al., 2001; Phillips et al., 1998; Vytal & Hamann, 2010), possibly

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INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE | Emotion Perception and Elicitation

reflecting an effect on attention and multimodal integration that may be enhanced by positive affect (Fredrickson & Branigan, 2005). The more ventral ACC is associated with sadness and depression (Mayberg et al., 1999) but inconstantly responds to sad facial expressions. It also activates to fearful or angry faces (Blair, Morris, Frith, Perrett, & Dolan, 1999) and even to happy faces presented subliminally (Killgore & Yurgelun-Todd, 2004). The exact function of this region is unclear, possibly subserving a supramodal integration of emotional, cognitive, autonomic, and visceral information, which may contribute to affect monitoring and regulation (George, 2013; Rushworth, Behrens, Rudebeck, & Walton, 2007). Finally, the vlPFC and OFC (Blair et al., 1999; Kesler-West et al., 2001; Morris, Ohman, & Dolan, 1999; Murphy et al., 2003; Sprengelmeyer et al., 1998) are recruited when processing signals of aggression, especially anger expression in faces or voices. Both the amygdala and the striatum are also engaged by anger-provoking scenarios (Kimbrell et al., 1999; Schaefer et al., 2003) and facial expressions of aggression (Beaver, Lawrence, Passamonti, & Calder, 2008; Phillips et al., 1998), together with

Types of emotional stimuli used

the midbrain, thalamus, and hypothalamus. These responses are modulated by dopaminergic and serotonergic activity (Passamonti et al., 2012), in line with evidence that aggression and violence are promoted by genetic anomalies in monoamine metabolic pathways. Such a role of the ventral striatal dopamine system in anger, besides its classic implication in reward and pleasure, might be linked to motivational drives for obtaining or protecting valued resources in social contexts (Beaver et al., 2008), whereas the vlPFC and OFC presumably mediate topdown regulation and decision-making processes, respectively, integrating information about the affective value and predicted outcomes of social events (George, 2013; Roy, Shohamy, & Wager, 2012). Overall, these neuroimaging results suggest that neither discrete nor dimensional theories of emotion account for the functional neural architecture of emotional processing. Some brain regions may preferentially respond to one or a few specific emotions, but most activate across many different emotions and stimulus types, without this being reducible to valence or arousal. This conclusion accords with recent metaanalyses, whether they focussed on faces (Fusar-Poli et al.,

Brain mapping of discrete emotions Thal

Insula

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Figure 1 Anatomical segregation of emotional responses. Summary of meta-analyses showing brain activation peaks evoked by happy, sad, angry, fearful, and disgusted emotional stimuli (faces, visual scenes, movie clips, voices, memory recall, and music) relative to neutral ones. Peaks were determined by combining the meta-analyses of Fusar-Poli et al. (2009), Vytal and Hamann (2010), Phan et al. (2002), and Murphy et al. (2003). See Table 1 for abbreviations.

INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE | Emotion Perception and Elicitation

2009; Sabatinelli et al., 2011) or included various stimulus modalities (Kober et al., 2008; Murphy et al., 2003; Phan et al., 2002; Tettamanti et al., 2012; Vytal & Hamann, 2010). Although some studies reported anatomical specificity in support of discrete (Fusar-Poli et al., 2009; Tettamanti et al., 2012; Vytal & Hamann, 2010) or dimensional (Anderson et al., 2003; Zald, 2003) accounts (see Figure 1), distributed and overlapping activations are generally found (Kober et al., 2008; Murphy et al., 2003; Phan et al., 2002), with part of the apparent specificity perhaps reflecting stimulus properties rather than emotional processes (Sabatinelli et al., 2011). While these data argue against a simple one-to-one mapping between specific emotions and individual brain regions, it remains possible that particular emotions or dimensions are implemented in separate neuronal circuits at a finer level of organization. For example, different emotions may be encoded in distinct subregions of the PAG (a brain stem region critically implicated in autonomic regulation and defensive motor responses) (Satpute et al., 2013) or in distinct neuronal populations within a given region (the amygdala or vmPFC; cf.

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Peelen et al., 2010), consistent with electrophysiological recordings in humans and animals (Laxton et al., 2013; Salzman, Paton, Belova, & Morrison, 2007). These shared activations across different affective states are likely to reflect broader functions that we are only beginning to unveil. These include salience detection, relevance appraisal, novelty and learning signals, prediction states, and prediction errors, all accompanied by adaptive changes in cognitive, sensorimotor, and autonomic systems, as well as concomitant regulation processes. In addition, mentalizing and mirroring mechanisms are engaged when processing emotions in other people based on sensory cues (e.g., faces) or social interactions (e.g., moral problems). Thus, emotion understanding and empathy recruit brain regions partly overlapping with those activated by one’s own experience. Taken together, these observations fit best with componential models according to which emotions emerge from the coordinated activation of multiple constituents, serving to evaluate the significance of events and promote adaptive responses according to both internal and external needs (Sander et al., 2007; Scherer, Schorr, & Johnstone, 2001).

Cognitive/motor group

(c)

(b) (d)

Medial posterior group

(g)

(a)

Lateral paralimbic group

Medial PFC group Occipital/visual association group

(f)

(e)

Core limbic group

Figure 2 Toward defining functional network of emotions. (a–f) Six functional groups of regions revealed by multivariate analysis, depicted on a 3-D brain template. Each group is rendered in a unique color. (g) Functional relationships among regions in each group are shown by coactivation lines on a ‘flattened’ map of connectivity space. Colors correspond to those in panels (a)–(f) and identify each network. Points closer together on the graph have stronger positive coactivation. This connectivity map was ‘pruned’ such that the depicted relationships were not mediated by any other single intervening region. See Table 1 for abbreviations. (R) ¼ right, (L) ¼ left, and (Bi) ¼ bilateral. Reprinted from Kober, H., Barrett, L. F., Joseph, J., Bliss-Moreau, E., Lindquist, K., & Wager, T. D. (2008). Functional grouping and cortical-subcortical interactions in emotion: A meta-analysis of neuroimaging studies. Neuroimage, 42(2), 998–1031. doi: 10.1016/j.neuroimage.2008.03.059, with permission from Elsevier.

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Toward Defining Functional Networks Beyond mapping emotions onto individual brain regions, recent work using novel analysis techniques has highlighted the importance of distributed networks whose configuration may be dynamically shaped by reciprocal interactions and differentially modulated across emotion states. For example, during movie watching, negative valence correlates with coherent fluctuations in the default mode network (the precuneus, mPFC, and TPJ) and emotion/salience processing networks (the amygdala, insula, and striatum), while arousal level follows fluctuations in attentional and somatosensory networks (frontoparietal cortices) (Nummenmaa et al., 2012). Moreover, the amygdala shows distinct functional coupling with distant cortical networks (e.g., the PPC during fear and the mPFC during joy) as a function of emotion content in movies (Tettamanti et al., 2012). Changes in large-scale functional connectivity between the anterior and posterior cingulate, insula, precuneus, and thalamus also persist for several minutes during rest periods following positive or negative emotional events (Eryilmaz et al., 2011) and can be characterized by network graph analyses and pattern-classification statistics (Richiardi, Eryilmaz, Schwartz, Vuilleumier, & Van De Ville, 2011). Likewise, six groups of frequently coactivated regions were identified by a meta-analysis of 162 studies using multivariate parcellation and clustering techniques (Kober et al., 2008). Each group was tentatively linked to a distinct emotion component (see Figure 2). The ‘core limbic’ and ‘lateral paralimbic’ groups were attributed to ‘core’ affective dimensions of hedonic value and arousal (Russell, 2003), whereas the ‘medial anterior’ and ‘posterior medial’ groups were linked to conceptual categorization and meaning attribution processes (Barrett, 2006). Finally, occipital/visual and cognitive/motor control groups were related to the engagement of perceptual, executive attention, and action selection processes (Kober et al., 2008). Although this meta-analysis is faced with several limitations (see Sabatinelli et al., 2011), it underscores a view of brain organization where networks constitute the key functional units, rather than isolated brain areas (Bullmore & Sporns, 2009; Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012), and accords with the notion that emotion systems are not strictly dissociable from perceptual and cognitive domains (Pessoa, 2008; Scherer et al., 2001; Vuilleumier & Pourtois, 2007). In this framework, the same brain region can sustain different functions at different times according to its recruitment in distinct networks, playing an emotion-specific role at a particular time but playing other roles at other times (Scarantino & Griffiths, 2011). Future studies should further explore the functional nature and distributed architecture of emotion representations by exploiting multivariate classifiers and machine learning algorithms (Mitchell et al., 2008; Richiardi et al., 2011) that can unveil both local (Ethofer, Van De Ville, et al., 2009; Peelen et al., 2010) and large-scale patterns of neural activity (Kassam et al., 2013; Pantazatos, Talati, Pavlidis, & Hirsch, 2012) corresponding to distinct emotions.

Conclusion Brain mapping work in the past decade has provided many new insights on the neural substrates of emotional processing,

not only allowing a refinement of psychological theories but also fostering translational research by defining affective processes in terms of neuroanatomical systems, rather than subjective or behavioral manifestations only. Future research should help better uncover the origin of individual differences in emotion reactivity and develop new approaches to understand and evaluate neuropsychiatric diseases such as anxiety and depression.

See also: INTRODUCTION TO CLINICAL BRAIN MAPPING: Depression; Emotion and Stress; Imaging Studies of Anxiety Disorders; INTRODUCTION TO SOCIAL COGNITIVE NEUROSCIENCE: A Neural Network for Moral Decision Making; Body Perception; Compassion; Emotion Regulation; Empathy; Face Perception: Extracting Social Information from Faces: The Role of Static and Dynamic Face Information; The Use of Brain Imaging to Investigate the Human Mirror Neuron System; INTRODUCTION TO SYSTEMS: Emotion; Face Perception; Functional Brain Imaging of Human Olfaction; Pain: Acute and Chronic; Taste, Flavor, and Appetite.

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