Real faces, real emotions: perceiving facial expressions in naturalistic contexts of voices, bodies and scenes. Beatrice de Gelder1,2 & Jan Van den Stock1,3 1 Laboratory of Cognitive and Affective Neuroscience, Tilburg University, The Netherlands
2 Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
3 Department of Neuroscience, KU Leuven, Leuven, Belgium
* Corresponding author: Beatrice de Gelder, Cognitive and Affective Neurosciences Laboratory, Department of Psychology, Tilburg University, P.O. 90153. 5000 LE TILBURG. Tel.: +31 13 466 24 95; Fax.: +31 13 466 2067; E-mail: B.deGelder@uvt.nl
For a while ‘’Headless Body in Topless Bar” counted as one of the funniest lines to have appeared in US newspapers. But headless bodies and bodiless heads figure only in crime catalogues and police reports and are not part of our daily experience, at the very least not part of the daily experience that constitutes the normal learning environment in which we acquire our face and body perception expertise. Yet, except for a few isolated studies, the literature on face recognition has not yet addressed the issue of context effects in face perception. By ‘context’ we mean here the whole naturalistic environment that is almost always present when we encounter a face.
Why has context received so little attention and what, if any, changes would we need to make to mainstream models of face and facial expression processing if indeed different kinds of context have an impact on how the brain deals with faces and facial expressions? Discussions on context influences and their consequences for how we read and react to an emotion from the face have a long history (Fernberger, 1928). But the kind of context effects that were investigated in the early days would nowadays qualify as so called late effects or post-perceptual effects, related as they are to the overall (verbal) appraisal of a stimulus rather that to its online processing. In contrast, the context effects we have specifically targeted in recent studies are those that are to be found at the perceptual stage of face processing.
In this chapter we review recent investigations of three familiar naturalistic contexts in which facial expressions are frequently encountered: whole bodies, natural scenes and emotional voices (See also Ambady and Weisbuch, this volume) . In the first section we briefly review recent evidence that shifts the emphasis from a categorical model of face processing, based on the assumption that faces are processed as a distinct object category with their dedicated perceptual and neurofunctional basis, towards more distributed models where different aspects of faces (like direction of gaze and emotional expression) are processed by different brain areas and different perceptual routines and show how these models are better suited to represent face perception and face-context effects. In the second section we look in detail at one kind of context effect, as found in investigations of interactions between facial and bodily expressions. We sketch a perspective in which context plays a crucial role, even for highly automated processes like the ones underlying recognition of facial expressions. Some recent evidence of context effects also has implications for current theories of face perception and its deficits.
Making space for context effects in models of face perception
Older theories on face perception have tended to restrict scientific investigations of face perception to issues of face vs. object categorization. The major sources of evidence for category specificity of face perception are findings about its temporal processing windows and neurofunctional basis. But this debate is not settled and recent evidence now indicates that the temporal and spatial neural markers of face categorization are also sensitive to some other non-face stimuli (for a review of such overlap between spatial and temporal markers of face and body specificity, see de Gelder et al., 2009). Furthermore, it is becoming increasingly clear that the presence of an emotional expression influences even those relatively early and relatively specific neural markers of category specificity like the N170 and the face area in fusiform gyrus. Finally, distributed models as opposed to categorical models of face processing seem more appropriate to represent the relation between face perception, facial expression perception and perceptual context effects as they represent the various functional aspects of facial information and allow for multiple entry points of context into ongoing face processing. Finally, models must also include the role of subcortical structures shown to be important components of face and facial expression processes.
a. Face perception and categorization
Much of the face recognition literature has been dominated by the view that face processing proceeds at its own pace, immune to the surrounding context in which the face is encountered. In line with this, one of the major questions in the field continues to be that of the perceptual and neurofunctional bases of faces. An important assumption has been and continues to be that faces occupy a neurofunctional niche on their own, such that face representations co-exists with but does not overlap with object representations, a view that in one sense or another is linked to the notion of modularity. Typical characteristics of modular processing as viewed in the eighties and brought to a broad audience by Fodor (1983) are mainly that processing is mandatory, automatic and insulated from context effects. What was originally a theoretical argument purporting to separate syntactic from the more intractable semantic aspects of mental processes became for a while the focus of studies using brain imaging (Kanwisher et al., 1997). A research program fully focused on category specificity is unlikely to pay attention to perceptual context effects on face processing. In contrast, more recent distributed models of face processing appear more suited to accommodate the novel context findings (de Gelder et al., 2003; Haxby et al., 2000).
b. Similarities between facial expressions and other affective signals in perceptual and neurofunctional processes
Seeing bodily expressions is an important part of everyday perception and scientific study of how we perceive whole body expressions has taken off in the last decade. Issues and questions that have been addressed in face research are also on the foreground in research on whole body expressions (see de Gelder et al., 2009 for a review). This is not surprising, considering the fact that faces and bodies appear together in daily experience. It may be not so surprising that perception of faces and bodies show several similarities at the behavioural and neuro-functional level. For example, both faces and bodies are processed configurally, meaning as a single perceptual entity, rather than as an assemblage of features. This is reflected in the perceptual processes triggered when face and body stimuli are presented upside-down (the inversion effect): recognition of faces and bodies presented upside-down is relatively more impaired than recognition of inverted objects, like houses (Reed et al., 2003). Also, a comparison of perception of upright and inverted faces reveals that the time course of the underlying brain mechanisms is similar for upright and inverted bodies (Stekelenburg and de Gelder, 2004). The presence of a bodily expression of fear in the neglected field also significantly reduces attention deficits in neurological populations (Tamietto et al., 2007), just as has been reported for faces (Vuilleumier and Schwartz, 2001). As will be shown in detail in the later sections, perception of bodily expressions activates some brain areas that are associated with the perception of faces (for reviews, see de Gelder, 2006; Peelen and Downing, 2007. See also section II).
c. From a face module to a face processing network
Categorical models of face processing (e.g. Kanwisher et al., 1997) tend to assume that the core of face processing consists of a dedicated brain area or module that is functionally identified by contrasting faces with a small number of other object categories mostly by using passive viewing conditions. All other dimensions of face processing corresponding to other dimensions of face information (emotion, age, attractiveness, gender…) are viewed as subsequent modulations of the basic face processing ability implemented in the brain’s face area(s). In contrast, distributed models for face perception also consider other aspects of faces besides person identity (Adolphs, 2002; Adolphs et al., 2000; de Gelder et al., 2003; de Gelder and Rouw, 2000; Haxby et al., 2000; Haxby et al., 1994; Haxby et al., 1996; Hoffman and Haxby, 2000; Puce et al., 1996). In distributed models, different areas of the brain process different attributes of the face, such as identity (FFA and the occipital face area (OFA)), gaze direction (superior temporal sulcus (STS)) and expression and/or emotion analysis (OFC, amygdala, anterior cingulate cortex, premotor cortex, somatosensory cortex).
Clinical cases constitute critical tests for theoretical models, and patients suffering from a deficit in face recognition or prosopagnosia (Bodamer, 1947) have long served as touchstone for models of face processing (see also chapters by Young, Calder, and Kanwisher and Barton). Available fMRI studies targeting face perception in prosopagnosics so far show inconsistent results (see Van den Stock et al., 2008b for an overview), but very few of those studies included facial expressions or compared emotional with neutral faces (see Calder, this volume). Configural processing as measured by the inversion effect is a hallmark of intact face processing skills and a few studies have reported that the normal pattern of the inversion effect does not obtain when a face perception disorder is present whether of acquired or of developmental origin (de Gelder and Rouw, 2000; but see McKone and Yovel, 2009). We investigated whether adding an emotional expression would normalize their face processing style with respect to the inversion effect. We presented neutral and emotional faces to patients with acquired prosopagnosia (face recognition deficits following brain damage) with lesions in FFA, inferior occipital gyrus (IOG) or both. Our study showed that emotional but not neutral faces elicited activity in other face related brain areas like STS and amygdala and, most importantly, that most of these patients showed a normal inversion effect for emotional faces as well as normal configural processing as measured by in a part-to-whole face identity matching task when the faces were not neutral but expressed an emotion (de Gelder et al., 2003). In a follow up fMRI study with patients suffering from developmental prosopagnosia (prosopagnosia without neurological history), we presented neutral and emotional (fearful and happy) faces and bodies and the results showed normal activation in FFA for emotional faces (fearful and happy) but lower activation for neutral faces, compared to controls (Van den Stock et al., 2008b) (see Figure 1). ---------------Figure 1
Increased activation for emotional faces compared to neutral faces in FFA has since been reported in an acquired prosopagnosia case by others also (Peelen et al., 2009). Electrophysiological studies are crucial for investigating distributed face models because the limited time resolution of fMRI does not allow one to conclude that all dimensions of facial information indeed necessarily depend on activity in the fusiform face area. Studies using electroencephalogram (EEG) or magnetoencephalogram (MEG) data initially provided support for face modularity, in the sense that there appeared to be a unique time window for a stimulus to enter the face processing system. EEG and MEG investigations into face perception have characterised two early markers in the temporal dynamics of face perception: a positive waveform around 100ms (P1) and a negative waveform around 170ms (N170) after stimulus onset indicating the time course of dedicated brain mechanisms sensitive to face perception. It is a matter of debate where in the brain these waveforms originate, whether in early extrastriate areas, STS or fusiform gyrus (FG) and what type of processing mechanism these waveforms reflect, whether global encoding, object categorization or configural processing (see de Gelder et al., 2006 for a review).
d. Face processing includes subcortical and cortical areas.
Finally, we have shown, as have other groups, that patients with striate cortex damage can process and recognize faces presented in their blind visual field (Andino et al., 2009; de Gelder and Tamietto, 2007; de Gelder et al., 1999b; Morris et al., 2001; Pegna et al., 2005) and for which they have no conscious perception. For this and other reasons not relevant here, the involvement of subcortical structures in face perception also needs to be represented in a distributed model of face processing as we sketched in de Gelder et al (2003) . Masking studies performed with neurologically intact observers, on residual visual abilities for faces and facial expressions in cortically blind patients and on face processing skills of infants with immature visual cortex converge to provide tentative evidence for the importance of subcortical structures. Research indicates that the distributed brain network for face perception encompasses two main processing streams: a subcortical pathway from superior colliculus and pulvinar to the amygdala that is involved in rudimentary and mostly nonconscious processing of salient stimuli like facial expressions (de Gelder et al., 2001; de Gelder et al., 2008; de Gelder et al., 1999b; Morris et al., 2001; Morris et al., 1998b; Pegna et al., 2005) and a more familiar cortical route from the lateral geniculate nucleus (LGN) via primary visual cortex to OFA, FFA and STS, sub serving fine grained analysis of conscious perception. Feed forward and feedback loops, especially between amygdala and striate cortex, OFA, FFA and STS (Amaral and Price, 1984; Carmichael and Price, 1995; Catani et al., 2003; Iidaka et al., 2001; Morris et al., 1998a; Vuilleumier et al., 2004) support the interaction between these routes to contribute ultimately to a unified and conscious percept (but see Cowey, 2004; Pessoa et al., 2002).
In summary, clinical phenomena like prosopagnosia and affective blindsight form an important contribution to the current understanding of face perception. Distributed face processing models that neuro-anatomically include subcortical structures and incorporate the many dimensions of faces like emotional expression appear to resonate best with the empirical data.
II. Body context effects on facial expressions
Of all the concurrent sources of affective signals that routinely accompany our sight of a facial expression, the body is by far the most obvious and immediate one. We review recent evidence for this perceptual effect and follow with a discussion of possible mechanisms underlying body context effects.
Perception of facial expression is influenced by the bodily expressions.
Research on the simultaneous perception of faces and bodies is still sparse. Two behavioural studies directly investigated how our recognition of facial expressions is influenced by accompanying whole body expressions (Meeren et al., 2005; Van den Stock et al., 2007). Meeren et al. (2005) combined angry and fearful facial expressions with angry and fearful whole body expressions to create both congruent (fearful face on fearful body and angry face on angry body) and incongruent (fearful face on angry body and angry face on fearful body) realistically looking compound stimuli (see Figure 2). These were briefly (200ms) presented one by one while the participants were instructed to categorize the emotion expressed by the face and ignore the body. The results showed that recognition of the facial expression was biased towards the emotion expressed by the body language, as reflected by both the accuracy and reaction time data. In a follow-up study, facial expressions that were morphed on a continuum between happy and fearful were once combined with a happy and once with a fearful whole body expression (Van den Stock et al., 2007). The resulting compound stimuli were presented one by one for 150ms, while the participants were instructed to categorize the emotion expressed by the face in a 2 alternative forced choice paradigm (fear or happiness). Again, the ratings of the facial expressions were influenced towards the emotion expressed by the body and this influence was highest for facial expressions that were most ambiguous (expressions that occupied an intermediate position on the morph continuum). Evidence from EEG-recordings during the experiment shows that the brain responds to the emotional face-body incongruency as early as 115ms post stimulus onset (Meeren et al., 2005). The reverse issue, whether perception of bodily expressions is influenced by facial expression has not been studied so far. However, natural synergies between facial and bodily expressions predict emotional spill over between the face and the body as exists between the facial expression and the voice (de Gelder and Bertelson, 2003).
---------- Figure 2
Possible mechanisms underlying body context effect
A few different explanations are suggested by body context effect. First, one may view these effects as providing support for a thesis that has a long history in research on facial expressions and states that facial expressions seen on their own are inherently ambiguous (Frijda, 1986). A different approach may be that emotions are intimately linked to action preparation and that action information is provided much more specifically by bodily than by facial expressions. A third consideration is that there may be considerable overlap between the neurofunctional basis of facial and bodily expressions such that showing either the face or the body also automatically triggers representation of the other.
i. Facial expressions may be inherently ambiguous. Does the strong impact of bodily expressions on judging facial expressions provide evidence for drawing the more radical conclusion that judgments of facial expressions are entirely context sensitive? Some recent studies have indeed suggested so. Adopting our methodology Aviezer et al. (2008) used disgust pictures with an average recognition of 65.6% in combination with contrasting upper body postures and contextual object cues like dirty underpants. Such low recognition rate does in fact provide a large margin for external influences on the face. Indeed, their results show that disgust faces are no longer viewed as expressing disgust when perceived with an incongruent body. This result is consistent with what has been known for a long time that the effect of the secondary information is the biggest where recognition rates of the primary stimulus are poorest (Massaro and Egan, 1996). This doesn’t seem that this study provides good evidence that judgments of facial expressions are entirely malleable, since the effects it shows are for facial expressions that are rather ambiguous when they are viewed on their own.
Aviezer et al. (2008) rightly remark that a crucial issue is whether the context effects are post-perceptual rather than truly perceptual (de Gelder and Bertelson, 2003). Their experiments unfortunately do not allow a conclusion one way or the other. They did not use rapid presentation or masking, the two classical means of exercising strategic control over the perceptual process. In all experiments they used untimed presentation with free exploration of the compound stimulus which allows the viewer to attend to the face and the body and ultimately to choose what information to base the response on, either on an ad hoc basis or also possibly depending on the particular emotion combination. The eye movement data they recorded do not settle the issue of rapid perceptual procedures in the observer. The eye movements effect they report cannot be deemed to reflect an underlying fast or rapid process, as the fixation latencies to enter either the upper or lower face area are on average around 1000 ms. In view of the fact that the latency to make a saccade is around 150-200 ms the reported latencies are very long indeed. Moreover, comparing their saccade latency values with RTs reported in Meeren et al. (2005) shows that on average RTs are about 200ms faster and even more for the congruent conditions. This is remarkable since RTs are by definition a slower measure than saccades (Bannerman et al., 2009). The findings indicate that the long eye gaze latencies reflect gaze fixation under voluntary-attentional control. Participants look at the compound stimulus and as we have shown, rapidly (in EEG time at the P1, which is in the window around 100ms) realizing the oddity of the compound stimulus and then explore and reassess the facial expression intentionally and apply a verbal label.
In fact, it is easy to imagine the opposite situation where the bodily expression completely loses its categorical expression identity in favor of the facial expression. In view of our limited understanding of what the critical components of bodily expressions are, it is currently still difficult to create stimuli where information from body and face is well balanced with respect to the informational content such that what each contributes can reliably be compared. More importantly, the relative predominance of the face vs. the body when both are present and are equally attended to may very well depend on the specific emotion. This is already suggested by data from eye movement studies indicating that observers’ fixation behavior during perception of bodily expressions is also a function of the emotion displayed. During perception of joy the observers tend to fixate on the head region, whereas during anger and fear most attention is devoted to the hands and arms. For sadness the subjects fixate on heads, arms, and hands and the legs almost never attract the subjects' attention. This fixation behavior is emotion-specific and remains stable under different conditions: whether the subjects were asked to recognize the body postures or were just watching; for both incorrectly and correctly recognized emotions; for pictures with different response times; and during the time progression of the experiment (perceptual learning) ( Ousov-Fridin, Barliy, Shectman, de Gelder, Flash, submitted).
One explantion may be provided by comparing the physical characteristic of different facial expressions. Components of different facial expressions may resemble each other, for example, upturned corners of the mouth characterizes both a smile and pain expression. An example of this strategy is provided by the study just discussed. The role of the context would then be to glue the components together in the configuration reflecting the information from the context. But such a view prima facie goes against notion that facial expressions are perceived configurally, and that ERP data indicate that they are rapidly processed.
ii. Emotional expressions involve the whole body in action. Bodyless heads are incomplete visual stimuli just as headless bodies are. To us the body to face context effects primarily suggest not that facial expressions are vague, imprecise or noisy, but that there is a very close link between both. An important aspect to consider when trying to explain that bodily postures influence the expression recognized on a face is provided by recent findings of overarching similarity in the perceptual (configural processing) (Reed et al., 2003) and neurofunctional (spatial and temporal overlap as shown in fMRI, EEG and MEG) signature of facial and bodily expressions (Meeren et al., 2008; Stekelenburg and de Gelder, 2004; van de Riet et al., 2009). This suggest that faces as well as bodies can rapidly convey the same message and do so in very similar ways. The brain mentally completes the headless body or the bodyless head. This can obviously not be based on missing physical information as would for example be the case when only part of the face was shown or one component was missing. What triggers the brain’s filling in may be, in the case of emotional body postures, the adaptive action the person is engaged in.
From a Darwinian evolutionary perspective, emotions are closely related to actions and therefore likely to involve the whole body rather than only the facial expressions. One view is that emotion provoking stimuli trigger affect programs (Darwin, 1872; Frijda, 1986; Panksepp, 1998; Russell and Feldman Barrett, 1999; Tomkins, 1963), which produce an ongoing stream of neurophysiologic change (or change in a person’s homeostatic state) and are associated with evolutionary-tuned behaviors for dealing with stimuli of significant value. Along with the orbitofrontal cortex (OFC) and amygdala, the insula and somatosensory cortex are involved in the modulation of emotional reactions involving the body via connections to brain stem structures (Damasio, 1994; Damasio, 1999; LeDoux, 1996) . This function of the insula and somatosensory cortex may contribute to their important role in emotion perception.
iii. Facial and bodily expressions share largely overlapping neurofunctional basis. Do the results just mentioned indicate that activation to facial expressions and to bodily expressions will almost always show complete overlap? As a matter of fact there is hardly any evidence in the literature to answer this question. For this reason we designed an fMRI study to investigate whether the brain shows distinctive activation patterns for perception of faces and bodies. We presented pictures of faces and faceless bodies that either showed a neutral, fearful or happy expression and asked participants to categorize the emotion expressed by the stimulus. To untangle brain activation related to faces and bodies, we compared how the brain responds to both categories (irrespective of emotional expression). Surprisingly, the results showed that the middle part of the fusiform gyrus (FG) that is typically associated with the perception of facial identity, is more activated for bodies than for faces (van de Riet et al., 2009). Previous studies have shown that there was at least partial overlap between the face-selective and body selective region within the FG (Hadjikhani and de Gelder, 2003; Peelen and Downing, 2005), and van de Riet et al. (2009) were the first to directly compare face and body related activation. In fact, perception of whole body expressions elicited a wider network of brain areas compared to faces, including other areas previously associated with perception of facial expressions, like STS. Other brain regions that were more active for bodies than for faces included the middle temporal/middle occipital gyrus (the so called extra-striate body area, EBA (Downing et al., 2001), the superior occipital gyrus and the parieto-occipital sulcus. When we consider more specifically the emotional information conveyed by the bodies and faces, again we observed a wider activation pattern specific for emotional bodies than for emotional faces. Interestingly, emotional body expressions activate cortical and subcortical motor areas like caudate nucleus, putamen and inferior frontal gyrus (IFG). This motor related activation may reflect the adaptive action component implied in the body expression, which is less pronounced in facial expressions (de Gelder et al., 2004a).
Since we used static images in this study, one may argue that the activity in areas associated with movement is related to the fact that there is more implied motion in the body expressions, compared to facial expressions. We therefore performed a follow up study in which we presented video clips of dynamic facial and bodily expressions that conveyed a neutral, fearful or angry expression instead of static picture stimuli. The results were nonetheless in the same line: bodies compared to faces activated more areas than vice versa, including the FG. Again, motor related areas were more activated by emotional body expressions (Kret et al. submitted).
Taken together these findings support the conclusion that while separating perception of faces and bodies may be somewhat artificial, bodily expressions activate a wider network of brain areas, including motor and action related regions.
III. Facial and bodily expressions in the context of scenes. When observing a stimulus that consists of a face and body with congruent expression (for example a fearful face on a fearful body) one might expect that recognition will be 100% correct. But this is not necessarily the case. In fact, perception and recognition of an emotional action is also influenced by the particular setting or scene in which it occurs. For example, viewed in isolation the sprint to the finish of a man shaking off a competitor looks quite similar to the flight of a man running away from a robber holding a knife. Without the context information, the emotional valence is ambiguous. Faces and bodies routinely appear as part of natural scenes and our perceptual system seems to be wired to make the link between the expression and the environment in which it appears to us. But little is known about the mechanism underlying this. Older appraisal theories of emotion (e.g. Scherer et al., 2001) acknowledge the importance of a visual event for our interpretation and evaluation of it and propose explanations for how we (emotionally) react to it. However, the primary focus in appraisal theories regards the emotional response of the observer to a stimulus, rather than the mere perception of the stimulus.
Hierarchical perception models tend to investigate the possible effects of a scene context as semantic effects which occur relatively late and take place in relatively middle to higher cognitive levels of processing (Bar, 2004). However, there is evidence that supports an early perceptual and neuro-anatomical analysis of a scene. Tachitoscopic presentation of a scene contributes to subsequent processing of the spatial relations across the scene (Sanocki, 2003), and the rapid extraction of the gist of a scene may be based on low spatial frequency coding (Oliva and Schyns, 1997). The more semantic effects of scene processing occur at a later stage, around 400ms after stimulus onset. For example, objects presented in their usual context are identified better (Davenport and Potter, 2004) and faster (Ganis and Kutas, 2003) and EEG data show the interaction occurs at about 390 ms after stimulus-onset. The functional neuro-anatomy of contextual associations of objects comprises a network including parahippocampal cortex (PHC), retrosplenial cortex, and superior orbital sulcus (Bar, 2004).
However, the effects of the emotional gist of a scene may occur at an earlier level, in line with the evolutionary significance of the information. Few experimental studies currently exist on the influence of emotional scenes on the perception of faces and bodies. In the first explorations of this issue, we presented fearful, disgusted and happy faces embedded in a natural scene (see Figure 3 for an example). The affective valence of the scene was either fearful, disgusted or happy, and the face-scene combinations were emotionally congruent (e.g. fearful face in fearful scene) or incongruent (e.g. fearful face in happy scene). Participants were required to categorize the emotion expressed by the face. The results revealed faster response times and higher accuracies for the congruent stimulus pairs, showing that the emotional expression of a face is recognized better when it is embedded in a congruent scene (Righart and de Gelder, 2008b). The context effects hold up under different attentional conditions: it can be observed when participants are explicitly decoding the emotional expression of the face (Righart and de Gelder, 2008a) but also when they are primarily focussed on the orientation of the face (Righart and de Gelder, 2006).
--- Figure 3
This indicates that it reflects an early and mandatory process and suggests a perceptual basis. Our EEG studies support this view: when fearful faces are presented in a fearful scene, EEG recordings show a higher N170 amplitude compared to when the same faces are presented in a neutral scene (Righart and de Gelder, 2006).
To investigate how the emotion conveyed by scenes influences brain activity associated with perception of faces we used fMRI while subjects were shown neutral and fearful faces in both neutral and emotional scenes. We ran a parallel version of the experiment with neutral and fearful bodies instead of faces. The results showed that the activation level in FFA is modulated by the kind of scene in which it is presented. In particular, fearful faces elicit more activity than neutral faces, but more interestingly, fearful faces in threatening scenes trigger more activity than fearful faces in neutral scenes. Also, activity in body areas, like the extrastriate body area (EBA) (Downing et al., 2001) is influenced by the scene in which it is embedded: overall, fearful bodies trigger more activity than neutral bodies, but interestingly, neutral bodies in threatening scenes trigger more activity than in neutral scenes. On the other hand, the presence of a face or a body influences brain activity in areas that are associated with the processing of scenes, like the retrosplenial complex (RSC) and the parahippocampal cortex (PHC) (Sinke & de Gelder, submitted; Van den Stock & de Gelder, submitted). In general, neutral scenes trigger higher activation in the PHC and RSC, but the presence of a neutral body boosts activity in these areas. In a behavioural experiment we presented participants with stimuli depicting an emotional body seen in the foreground against an emotionally congruent or incongruent background. Participants were instructed to categorize the emotion expressed by the foreground body and the results showed that especially negative emotions (fear and anger) were recognized faster in a congruent background, whereas this was not the case for happy expressions (Kret & de Gelder, submitted).
These findings suggest that the emotion conveyed by the scene ‘spills over’ to the embedded face or body, and vice versa. Stated simply, a fearful face makes a neutral scene appear threatening, while a threatening scene makes a neutral face fearful.
IV. Facial expressions in the context of the affective prosody of voices. Research focussing on human face and emotion perception has primarily targeted how visual stimuli are perceived, although in daily life facial expressions are typically accompanied by vocal expressions.
Human emotion recognition can be based on isolated facial or vocal cues (Banse and Scherer, 1996; Scherer et al., 1991) but combining both modalities results in a performance increase as shown by both increased accuracy rates and shorter response latencies (de Gelder et al., 1999a; de Gelder and Vroomen, 2000; de Gelder et al., 1995; Dolan et al., 2001; Massaro and Egan, 1996). Detailed behavioural investigations into crossmodal influences between vocal and facial cues requires a paradigm in which both modalities are combined to create audiovisual pairs. The manipulation ideally consists of altering both the emotional congruency between the two modalities and a task that consists of emotion categorization based on only one of both information streams. For example, de Gelder and Vroomen (2000) presented facial expressions that were morphed on a continuum between happy and sad while at the same time a short spoken sentence was presented. This sentence had a neutral semantic meaning, but was spoken in either a happy or sad emotional tone of voice. Participants were instructed to attend to and categorize the face and ignore the voice in a 2 alternative forced choice task. The results showed a clear influence of the task irrelevant auditory modality on the target visual modality. For example, sad faces were less frequently categorized as sad when they were accompanied by a happy voice. In a follow up experiment, vocal expressions were morphed on a fear-happy continuum and presented with either a fearful or happy face, while participants were instructed to categorize the vocal expression. Again, the task irrelevant modality (facial expressions) influenced the emotional categorization of the target modality (vocal expressions). Furthermore, this experiment was repeated under different attentional demands, but the facial expression influenced the categorization of vocal expression in every attentional condition (Vroomen et al., 2001).
These findings suggest that affective multisensory integration is a mandatory and automatic process. However, based on these behavioral data, no direct claims can be made about the nature of this crossmodal bias effect. The findings could either reflect an early perceptual or later more cognitive or decisional effect. Neuro-imaging methods with high temporal resolution are needed to provide information on the time course of when this bimodal crosstalk occurs. Studies addressing neural substrates of vocal expressions are few (de Gelder et al., 2004b; George et al., 1996; Ross, 2000) and primarily point to involvement of the right hemisphere. Electroencephalogram (EEG) investigations show that recognition of emotional prosody occurs already within the first 100-150 ms of stimulus presentation (Bostanov and Kotchoubey, 2004; de Gelder et al., 1999a; Goydke et al., 2004). The possibility that ecologically relevant audiovisual expressions may rely on specialized neural mechanisms has long been recognized in animal research and several studies have explored the relation between auditory and visual processing streams in non-human primate communication (Ghazanfar and Santos, 2004; Parr, 2004).
EEG studies addressing the time course of audiovisual integration point to an early integration of both modalities (around 110 ms after stimulus presentation) (de Gelder et al., 1999a; Pourtois et al., 2000), which is compatible with a perceptual effect. Supporting evidence for a mandatory nature of this integration is provided by studies with blindsight patients, who are unable, due to cortical damage, to consciously perceive visual stimuli presented in a segment of the visual field. When they are presented with auditory vocal expressions and at the same time visual facial expressions in their blind field, fMRI and EEG recordings are influenced by the facial expression of which they are unaware. This shows that the unconscious emotional information displayed by the face is processed by alternative brain pathways through which it influenced the brain responses to the consciously perceived vocal expressions.
Another question concerns where in the brain the integration of perceived vocal and facial expressions takes place. Heteromodal cortex is a logical candidate for multisensory integration (Mesulam, 1998). Superior temporal sulcus (STS) (Barraclough et al., 2005) and ventral premotor cortex (Kohler et al., 2002) have been shown to be involved in multisensory integration of biological stimuli. Functional imaging studies addressing the combined perception of emotional face-voice pairs (Dolan et al., 2001; Ethofer et al., 2006) show that fearful faces simultaneously presented with fearful voices activate the left amygdala. The role of the amygdala in emotional and face processing is well established (Zald, 2003) and connectivity data show that it receives inputs from both auditory and visual cortices (McDonald, 1998). These findings make this brain structure an important location for integration of affective bimodal inputs.
Recent studies have shown that next to facial expressions, bodily expressions are also prone to crossmodal affective influences. For example, recognition of dynamic whole body expressions of emotion are influenced not only by both human and animal vocalizations (Van den Stock et al., 2008a), but also by instrumental music (Van den Stock et al., 2009), suggesting the brain is well organized to combine affective information from different sensory channels.
Summary and conclusions
Real faces are part and parcel of their context and this consideration must play an important role in future models of face processing. Recent data show that bodily expressions, affective prosody, as well as the emotional gist of a natural scene all influence the recognition of facial expression. When a face is accompanied by a body or voice expressing the same emotion, or when it is presented in a congruent emotional scene, the recognition of facial expression typically improves, i.e. both the judgment accuracy and speed increase. Hence, both the immediate visual and auditory contexts function to disambiguate the signals of facial expression. Our behavioral and electrophysiological data suggest that this perceptual integration of information does not require high-level semantic analysis occurring relatively late at higher cognitive centers. Instead, the integration appears to be an automatic and mandatory process, which takes place very early in the processing stream, before full structural encoding of the stimulus and conscious awareness of the emotional expression are fully elaborated.
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