Characterizing the neural signature of face processing in Williams syndrome via multivariate pattern analysis and...

Face recognition ability is often reported to be a relative strength in Williams syndrome (WS). Yet methodological issues associated with the supporting research, and evidence that atypical face processing mechanisms may drive outcomes 'in the typical range', challenge these simplistic characterisations of this important social ability. Detailed investigations of face processing abilities in WS both at a behavioural and neural level provide critical insights. Here, we behaviourally characterised face recognition ability in 18 individuals with WS comparatively to typically developing children and adult control groups. A subset of 11 participants with WS as well as chronologically age matched typical adults further took part in an EEG task where they were asked to attentively view a series of upright and inverted faces and houses. State-of-the-art multivariate pattern analysis (MVPA) was used alongside standard ERP analysis to obtain a detailed characterisation of the neural profile associated with 1) viewing faces as an overall category (by examining neural activity associated with upright faces and houses), and to 2) the canonical upright configuration of a face, critically associated with expertise in typical development and often linked with holistic processing (upright and inverted faces). Our results show that while face recognition ability is not on average at a chronological age-appropriate level in individuals with WS, it nonetheless appears to be a relative strength within their cognitive profile. Furthermore, all participants with WS revealed a differential pattern of neural activity to faces compared to objects, showing a distinct response to faces as a category, as well as a differential neural pattern for upright vs. inverted faces. Nonetheless, an atypical profile of face orientation classification was found in WS, suggesting that this group differs from typical individuals in their face processing mechanisms. Through this innovative application of MVPA, alongside the high temporal resolution of EEG, we provide important new insights into the neural processing of faces in WS.