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

Understanding the role of eye movement pattern and consistency during face recognition through EEG decoding

Eye movement patterns and consistency during face recognition are both associated with recognition performance. We examined whether they reflect different mechanisms through EEG decoding. Eighty-four participants performed an old-new face recognition task with eye movement pattern and consistency quantified using eye movement analysis with hidden Markov models (EMHMM). Temporal dynamics of neural representation quality for face recognition were assessed through decoding old vs new faces using a support vector machine classifier. Results showed that a more eye-focused pattern was associated with higher decoding accuracy in the high-alpha band, reflecting better neural representation quality. In contrast, higher eye movement consistency was associated with shorter latency of peak decoding accuracy in the high-alpha band, which suggested more efficient neural representation development, in addition to higher ERP decoding accuracy. Thus, eye movement patterns are associated with neural representation effectiveness, whereas eye movement consistency reflects neural representation development efficiency, unraveling different aspects of cognitive processes.

Breaking through suppression: Face expertise selectively modulates very early awareness of high level face properties

Neurotypical variability in face recognition abilities is known to be driven by differences present across multiple elements of an extended processing pathway, i.e., from early visual perception through to later explicit retrieval and recall. Here across two experiments, we utilised breaking Continuous Flash Suppression paradigms to explore the earliest stage of face encoding: the lead up to conscious detection. We investigated whether faces selectively receive preferential access to awareness among participants with relatively stronger (cf. weaker) face recognition abilities at the categorical level (contrasting detection of faces with another object category) and higher levels of face processing (exploring differences associated with orientation and attractiveness). Both experiments identified selectively faster access to awareness for faces over a non-face object control (houses) in better face recognisers at both the group and individual level. Experiment two further clarified that these expertise-related effects are selective to upright (cf. inverted) faces, indicating that this link is unlikely to be solely driven by sensitivity to low level visual cues. We also observed expertise-related modulation of attractiveness effects on CFS breakthrough, consistent with the possibility that individuals with higher levels of face processing ability have accelerated early access to even this high-level stimulus dimension. Taken together these experiments provide new insight into very early face perception, and the extent to which expertise modulates this processing stage at both the group and individual level.

Exploring the development of face recognition across childhood via logistic mixed-effects modelling of the standardised Cambridge Face Memory Test

Individual differences in face identity recognition abilities are present across the lifespan but require developmentally differentiated methods of assessment. Here, we examine the empirical validity of a widely used face identity recognition measure, the Cambridge Face Memory Test for Children (CFMT-C). Logistic mixed-effects modelling of a large data set (607 children, 5-12 years) replicates and extends the findings of the only previous normative study of the CFMT-C (Croydon et al., Neuropsychologia, 62, 60-67, 2014). This novel, analytical approach enables us to take into account sources of variability typically overlooked in a classical analysis. We consider variability introduced by the task, alongside variability across children, to provide the first comprehensive characterisation of the interactive effects of factors inherent to participants (e.g. age, gender, and ethnicity), and the test (stage: face learning, simple recognition, harder recognition) on face memory performance. In line with past findings, we clearly observed age-related improvement in the task. Additionally, and for the first time, we report that this developmental effect is significantly more pronounced in the later, harder stages of the task; that there is an effect of gender, with females having better performance; and that consideration of participant ethnicity or testing context did not alter the best fitting model of these data. These results highlight the value of applying multilevel statistical models to characterise the factors driving performance variability, providing evidence of the divergence in recognition abilities across genders and confirming the stability of the CFMT-C in assessing face recognition abilities across variable experimental contexts and with diverse participant groups.

The neural dynamics of familiar face recognition

Humans are highly efficient at recognising familiar faces. However, previous EEG/ERP research has given a partial and fragmented account of the neural basis of this remarkable ability. We argue that this is related to insufficient consideration of fundamental characteristics of familiar face recognition. These include image-independence (recognition across different pictures), levels of familiarity (familiar faces vary hugely in duration and intensity of our exposure to them), automaticity (we cannot voluntarily withhold from recognising a familiar face), and domain-selectivity (the degree to which face familiarity effects are selective). We review recent EEG/ERP work, combining uni- and multivariate methods, that has systematically targeted these shortcomings. We present a theoretical account of familiar face recognition, dividing it into early visual, domain-sensitive and domain-general phases, and integrating image-independence and levels of familiarity. Our account incorporates classic and more recent concepts, such as multi-dimensional face representation and course-to-fine processing. While several questions remain to be addressed, this new account represents a major step forward in our understanding of the neurophysiological basis of familiar face recognition.

The Timing of Gaze Direction Perception: ERP Decoding and Task Modulation

Distinguishing the direction of another person's eye gaze is extremely important in everyday social interaction, as it provides critical information about people's attention and, therefore, intentions. The temporal dynamics of gaze processing have been investigated using event-related potentials (ERPs) recorded with electroencephalography (EEG). However, the moment at which our brain distinguishes the gaze direction (GD), irrespectively of other facial cues, remains unclear. To solve this question, the present study aimed to investigate the time course of gaze direction processing, using an ERP decoding approach, based on the combination of a support vector machine and error-correcting output codes. We recorded EEG in young healthy subjects, 32 of them performing GD detection and 34 conducting face orientation tasks. Both tasks presented 3D realistic faces with five different head and gaze orientations each: 30°, 15° to the left or right, and 0°. While the classical ERP analyses did not show clear GD effects, ERP decoding analyses revealed that discrimination of GD, irrespective of head orientation, started at 140 ms in the GD task and at 120 ms in the face orientation task. GD decoding accuracy was higher in the GD task than in the face orientation task and was the highest for the direct gaze in both tasks. These findings suggest that the decoding of brain patterns is modified by task relevance, which changes the latency and the accuracy of GD decoding.

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.