Mechanisms of face specificity - Differentiating speed and accuracy in face cognition by event-related potentials of central processing

Effects of expectation on face perception and its association with expertise

Perceptual decisions are derived from the combination of priors and sensorial input. While priors are broadly understood to reflect experience/expertise developed over one's lifetime, the role of perceptual expertise at the individual level has seldom been directly explored. Here, we manipulate probabilistic information associated with a high and low expertise category (faces and cars respectively), while assessing individual level of expertise with each category. 67 participants learned the probabilistic association between a color cue and each target category (face/car) in a behavioural categorization task. Neural activity (EEG) was then recorded in a similar paradigm in the same participants featuring the previously learned contingencies without the explicit task. Behaviourally, perception of the higher expertise category (faces) was modulated by expectation. Specifically, we observed facilitatory and interference effects when targets were correctly or incorrectly expected, which were also associated with independently measured individual levels of face expertise. Multivariate pattern analysis of the EEG signal revealed clear effects of expectation from 100 ms post stimulus, with significant decoding of the neural response to expected vs. not stimuli, when viewing identical images. Latency of peak decoding when participants saw faces was directly associated with individual level facilitation effects in the behavioural task. The current results not only provide time sensitive evidence of expectation effects on early perception but highlight the role of higher-level expertise on forming priors.

Explicit face memory abilities are positively related to the non-intentional encoding of faces: Behavioral and ERP evidence

Individual differences in face memory abilities have been shown to be related to individual differences in brain activity. The present study investigated brain-behavior relationships for the N250 component in event-related brain potentials, which is taken as a neural sign of face familiarity. We used a task in which a designated, typical target face and several (high- and low-distinctive) nontarget faces had to be distinguished during multiple presentations across a session. Separately, face memory/recognition abilities were measured with easy versus difficult tasks. We replicated an increase of the N250 amplitude to the target face across the session and observed a similar increase for the non-target faces, indicating the build-up of memory representations also for these faces. On the interindividual level, larger across-session N250 amplitude increases to low-distinctive non-target faces were related to faster face recognition as measured in an easy task. These findings indicate that non-intentional encoding of non-target faces into memory is associated with the swift recognition of explicitly learned faces; that is, there is shared variance of incidental and intentional face memory.

Face recognition ability is manifest in early dynamic decoding of face-orientation selectivity-Evidence from multi-variate pattern analysis of the neural response

Although humans are considered to be face experts, there is a well-established reliable variation in the degree to which neurotypical individuals are able to learn and recognise faces. While many behavioural studies have characterised these differences, studies that seek to relate the neuronal response to standardised behavioural measures of ability remain relatively scarce, particularly so for the time-resolved approaches and the early response to face stimuli. In the present study we make use of a relatively recent methodological advance, multi-variate pattern analysis (MVPA), to decode the time course of the neural response to faces compared to other object categories (inverted faces, objects). Importantly, for the first time, we directly relate metrics of this decoding assessed at the individual level to gold-standard measures of behavioural face processing ability assessed in an independent task. Thirty-nine participants completed the behavioural Cambridge Face Memory Test (CFMT), then viewed images of faces and houses (presented upright and inverted) while their neural activity was measured via electroencephalography. Significant decoding of both face orientation and face category were observed in all individual participants. Decoding of face orientation, a marker of more advanced face processing, was earlier and stronger in participants with higher levels of face expertise, while decoding of face category information was earlier but not stronger for individuals with greater face expertise. Taken together these results provide a marker of significant differences in the early neuronal response to faces from around 100 ms post stimulus as a function of behavioural expertise with faces.

Reflections and New Perspectives on Face Cognition as a Specific Socio-Cognitive Ability

The study of socio-cognitive abilities emerged from intelligence research, and their specificity remains controversial until today. In recent years, the psychometric structure of face cognition (FC)-a basic facet of socio-cognitive abilities-was extensively studied. In this review, we summarize and discuss the divergent psychometric structures of FC in easy and difficult tasks. While accuracy in difficult tasks was consistently shown to be face-specific, the evidence for easy tasks was inconsistent. The structure of response speed in easy tasks was mostly-but not always-unitary across object categories, including faces. Here, we compare studies to identify characteristics leading to face specificity in easy tasks. The following pattern emerges: in easy tasks, face specificity is found when modeling speed in a single task; however, when modeling speed across multiple, different easy tasks, only a unitary factor structure is reported. In difficult tasks, however, face specificity occurs in both single task approaches and task batteries. This suggests different cognitive mechanisms behind face specificity in easy and difficult tasks. In easy tasks, face specificity relies on isolated cognitive sub-processes such as face identity recognition. In difficult tasks, face-specific and task-independent cognitive processes are employed. We propose a descriptive model and argue for FC to be integrated into common taxonomies of intelligence.