Face processing strategies in ASC

Other theorists attribute facial processing deficits to pervasive processing atypicalities. The ES/EMB theory would expect ASC individuals to adopt a systematic rather than intuitive approach to the analysis of facial stimuli; breaking the face down into its constituent parts and looking for causal relationships between facial features (Baron-Cohen, 2006). Likewise, the WCC theory would predict that faces may be attended to but when they are, face processing would be characterised by the less efficient detail-focussed processing bias (Happe & Frith, 2006). This domain-general bias when applied to visual stimuli that is both complex and perceptually homogenous, is argued to lead to less efficient processing since stimuli of this type is better suited to the sensitivity of holistic processing. Furthermore, the EDRG theory would posit that the hyper-sensitivity to differences and deficient categorical processing would lead to difficulties in applying the knowledge gained through experience to new instances of that face being viewed (different facial expressions, head positions etc.). Since this theory offers that this detail-focussed processing style is domain-general, any complex stimuli with a large number of commonalities (in addition to faces) would also suffer from deficient processing (Ewing, Pellicano & Rhodes, 2013).

The research findings by Gross (2004), that in ASC there are no performance gains of presenting whole faces as opposed to isolated areas, suggests that, in ASC, there is more of a reliance on individual facial features during face processing. Behavioural and neuropsychological research findings indicating slower processing times also point to faces

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being processed in a more deliberate and effortful manner, rather than intuitively. Even in face processing tasks where there are no observed accuracy differences between ASC and non- ASC participants, ASC groups have been found to be slower at reaching judgements pertaining to identity and gender (Behrmann, Thomas & Humphreys, 2006) and emotion labelling (Capps

et al., 1992). Evidence for slower face processing is also provided by an event related potential study which found that the N170 waveform, typically elicited when we view facial stimuli, has a longer latency in ASC individuals, indicative of slower information processing of faces (McPartland et al., 2004). Therefore, whereas typically, people appraise faces intuitively, ASC individuals may adopt rule based strategies to make identity or emotion judgements.

Walsh, Vida and Rutherford (2014) demonstrated that rule based strategies are adopted in ASC when judging facial expressions of emotion and argued that these strategies compensate for the lack of intuitive face appraisal processes seen in typically developing individuals. When we see an emotionally expressive face, we automatically and intuitively infer the emotional state of the person communicating the expression by matching the overall facial configuration to a prototype held for that emotion category. However, Walsh et al. (2014) argue that ASC individuals adopt a systematic rule based matching strategy; painstakingly matching each facial feature appearance (i.e. corners of the mouth raised for happiness) against a memorised list of characteristics which they hold as defining that expression. The more characteristics that are present in a viewed expression or the more intense those characteristics are, the easier it will be for the ASC individual to correctly label the emotion being communicated. To test this prediction, Walsh et al. (2014) presented ASC adults with images of facial expressions of varying intensities. Emotion expression exemplars of happy and sad were manipulated so that each face was presented at 100%, 150%, 200%, 250% and 300% intensity. Pairs of different intensities were then presented to participants who indicated which of the images provided a better representation of how a person would look when they were feeling that emotion. ASC participants indicated that the most exaggerated exemplar (300%) of both happy and sad faces were more representative significantly more than did matched non-ASC controls. The amplification of the emotion exemplar defining characteristics in the exaggerated images appeared to lead the ASC group to consider the exemplar as a better representation of happy and sad than the typically expressed examples. Such a tolerance to exaggeration suggests that ASC individuals use a rule-based tick list type strategy to process the expression. The more

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exaggerated and obvious the characteristics are, the more it is considered appropriate. If the participants were employing a more typical automatic prototypic matching strategy, exaggerated faces would be rejected as not being compatible with the prototype for that expression, as was the case for the non-ASC participants. This finding again supports the notion that a failure to attend preferentially to faces from very early on in development prevents the development of face specialised brain circuitry and therefore disrupts the ability to intuitively and automatically process emotive facial stimuli (Dawson et al., 1998). It is plausible therefore that ASC individuals systematically and explicitly learn the rules for facial expressions of emotions. The findings of this research are important for two reasons; firstly, from a clinical point of view, findings suggest that interventions aimed at improving FER abilities in ASC should teach the rules of each emotion exemplar in order to cater for the perceptual and cognitive strategies typically employed. Allowing ASC individuals to learn and then practice their cognitive based strategies may enhance subsequent FER abilities. Secondly, if ASC individuals adopt compensatory strategies to process faces, this has a bearing on the contradictory results in the domain of FER in ASC (to be discussed in Chapter Four). Studies using tasks which present expressions briefly may be more effective at highlighting deficiencies since they do not offer a sufficient amount of time to appraise the emotions using their rule-based strategy. When however, ASC individuals have no time restraints, strategies can be employed in order to answer questions accurately (Walsh et al., 2014).

As discussed in Chapter Two, human faces are typically and more effectively processed as a single configural stimulus rather than as a number of isolated features (Todorov, Loehr & Oosterhof, 2010). The ability to effectively process faces in a gestalt like manner however develops qualitatively during most of childhood (Taylor, Batty & Itier, 2004). Face categorisation in childhood involves a more piecemeal approach whereby children rely predominantly of prominent facial features, however, as the child ages, face specific neural circuitry develops and they become face experts. This face specific expertise allows them to process faces configurally with information taken primarily from the relationships that exist between the facial features rather than from the shapes of each isolated feature (Maurer, LeGrand & Mondloch, 2002). This pattern of development, from featurally based to configural is reflected by the qualitative improvements typically seen in FER. Four months old infants are able to distinguish basic emotional expressions (e.g. happy), whereas more complex and emotions are not usually

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enabled until later in development once configural processing strategies become the norm (Bornstein & Arterberry, 2003; Vicari et al., 2000). Once this stage is reached, FER becomes more accurate and faster (Rump et al., 2009). A configural processing style allows for more fine grained discriminations between faces and facially expressed affect, which are structurally similar. Configural processing allows us to pick up on very slight distortions of the facial feature spatial relationship information in order to make subtle discriminations between both faces (identity) and emotional expressions (Tanaka & Farah, 1993; Thomas et al., 2007).

As discussed in the review of the WCC theory, ASC individuals have a propensity for feature based processing (Happe, 1996) and it is offered that this processing bias when applied to face stimuli (which share more commonalities than differences), may explain deficient face and FER processing in ASC (Karatekin, 2007). If the ASC individual is fixating on isolated facial features separately, and in particular on features which may not effectively convey critical information (e.g. the mouth area), then it follows that face processing and FER will be less effective (Behrmann et al., 2006; Neumann et al., 2006), since they could miss critical communicatory information from the differential spatial relationships that characterise a person’s identity or an emotion’s physical manifestation.

Evidence for featural over configural face processing strategies in ASC come from the previously discussed face inversion effect (FIE) (Leder & Bruce, 2000; Yin, 1969). In ASC there is evidence of an attenuated FIE, or in other words, task performance (such as identity recognition) in ASC groups is found to be less disrupted by face inversion than it is in non-ASC groups (Boucher and Lewis, 1992; Davies et al., 1994; Dimitriou et al., 2015; Hauck et al., 1998; Langdell, 1978; Tantam et al., 1989; Teunisse & de Gelder, 2003). This has also been found to hold true when ASC children are required to attribute a basic emotional expression (happy, sad or angry) to an inverted face (Rosset et al., 2008). In fact, studies have found that ASC children are more accurate than non-ASC matched controls in sorting identity and emotional expressions when they are presented upside down (Hobson, Ouston & Lee, 1988). Similar findings of inverted face superiority have also been evidenced when ASC children complete an emotional expression labelling task (Tantam et al., 1989). Since the FIE is taken as a measure of configural processing, an attenuated FIE is taken as evidence for the adoption of a feature- based processing strategy in ASC. In ASC, facial recognition tasks are performed as well for upright faces as they are for inverted faces. However, results are conflicting. Typical FIEs in

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ASC have been reported in a number of studies (Riby et al., 2009; Scherf et al., 2008) and therefore the picture emerging from FIE studies remains unclear. What is clear however is the evidence from neuroimaging studies which have found atypical neural activation patterns in response to object and face stimuli in ASC, indicative of feature-based face processing. An fMRI study by Schultz et al. (2000) found that HF ASC individuals demonstrated significantly more activation in the right inferior temporal gyri (typically elicited in response to object perception) during a face discrimination task and significantly less activation in the right fusiform gyrus (typically associated with face processing) relative to matched non-ASC controls. This pattern of neural activity suggests that ASC individuals perceive and process faces as though they were objects using a segmented approach. Furthermore, whereas the N170 negative ERP component associated with face processing typically has a longer latency when people view inverted faces (indicative of a switch from configural to feature-based processing), McPartland

et al. (2004) found no evidence of increased latency in an ASC group. This finding suggests that inverting a face does not interfere with processing strategies in ASC as it does in non-ASC individuals and this is taken as evidence of feature-based processing in both upright and inverted faces in ASC.

Another paradigm offering support for deficient configural processing in ASC assesses the sensitivity to spatial differences between facial features. ASC children are found to struggle with eye-eye distance manipulations relative to non-ASC controls (Wolf et al., 2008). Similarly, Rutherford et al. (2007) found impaired discriminations of eye-to-eye spacing in ASC whereas mouth-to-nose spacing discriminations were found to be unaffected. In addition, evidence suggests a high spatial frequency bias (as opposed to the typical low frequency bias) in ASC during facial encoding, indicative of feature-based processing strategies (Deruelle et al., 2006)