Understanding human individuation of unfamiliar faces with oddball fast periodic visual stimulation and electroencephalography

Abstract

To investigate face individuation (FI), a critical brain function in the human species, an oddball fast periodic visual stimulation (FPVS) approach was recently introduced (Liu‐Shuang et al., Neuropsychologia, 2014, 52, 57). In this paradigm, an image of an unfamiliar “base” facial identity is repeated at a rapid rate F (e.g., 6 Hz) and different unfamiliar “oddball” facial identities are inserted every nth item, at a F/n rate (e.g., every 5th item, 1.2 Hz). This stimulation elicits FI responses at F/n and its harmonics (2F/n, 3F/n, etc.), reflecting neural discrimination between oddball versus base facial identities, which is quantified in the frequency domain of the electroencephalogram (EEG). This paradigm, used in 20 published studies, demonstrates substantial advantages for measuring FI in terms of validity, objectivity, reliability, and sensitivity. Human intracerebral recordings suggest that this FI response originates from neural populations in the lateral inferior occipital and fusiform gyri, with a right hemispheric dominance consistent with the localization of brain lesions specifically affecting facial identity recognition (prosopagnosia). Here, we summarize the contributions of the oddball FPVS framework toward understanding FI, including its (a)typical development, with early studies supporting the application of this technique to clinical testing (e.g., autism spectrum disorder). This review also includes an in‐depth analysis of the paradigm's methodology, with guidelines for designing future studies. A large‐scale group analysis compiling data across 130 observers provides insights into the oddball FPVS FI response properties. Overall, we recommend the oddball FPVS paradigm as an alternative approach to behavioral or traditional event‐related potential EEG measures of face individuation.

Harnessing fast periodic visual stimulation to study face cognition: Sub‐processes, brain‐behavior relationships, and objectivity

“…We appreciate the reasoning that "fluctuations of amplitude outside of the ROIs associated with the maximal response may be essentially due to signal leakage, which is problematic for multi-variate pattern analysis". However, we contest the view that limited reliability observed for "outside-ROIs" should be interpreted as "no evidence that they could account for significant modulations of the FI response" (Rossion et al, 2020). In our opinion, combining systematically varied task-and stimulus-related attributes with electrode-agnostic analyses could unveil important individual differences in the FI response's spatial distribution.…”

“…Objectivity in psychological research generally implies maximizing available df, while limiting controlled task-and stimulus-related attributes to those necessary for eliminating potential confounds. Rossion et al (2020) suggest reducing the df in FI response analysis by downsampling to occipital, temporal, and parietal electrodes. Yet, this practice can inflate false-positive statistical inferences, by homogenizing them based on potentially circular selection criteria (Kriegeskorte et al, 2009;Simmons et al, 2011).…”

Attention affects overall gain but not selective contrast at meter frequencies in the neural processing of rhythm

“…While faithful tracking of stimulus envelope is fundamental for auditory perception (Peelle et al, 2013;Di Liberto et al, 2018;Etard and Reichenbach, 2019;Ghinst et al, 2019), the brain must go beyond one-to-one representation of the sensory input to achieve adaptive behavior (Kuchibhotla and Bathellier, 2018). Thus, the sensory input is continuously transformed within the brain towards higher-level categories (Ley et al, 2014;Brodbeck et al, 2018;Rossion et al, 2020;Sankaran et al, 2020;Yin et al, 2020). Such transformations are critical for timing perception already at the level of single intervals (Desain and Honing, 2003;Jacoby and McDermott, 2017), but also patterns of intervals (Notter et al, 2018), and for meter perception, where a range of physically different acoustic inputs can be mapped onto the same set of periodic pulses (Nozaradan et al, 2017a).…”

“…Similarly, we do not claim that the contrast at meter frequencies measured in EEG responses is one-to-one with meter perception in a phenomenological sense. At the same time, it is important to note that all measures of perception are indirect (including behavioral measures), and critically depend on the definition of the perceptual phenomenon (see also Rossion et al, 2020). If meter is defined as the perception of pulses that are time-locked to the temporal structure of the stimulus, and if pulse is understood as something that consistently occurs at regularlyspaced time points and not otherwise (thus creating a temporal contrast), our EEG measure is directly relevant for meter processing.…”

“…From the EEG, we measured the difference in amplitude of the neural activity at meter-related frequencies vs. meterunrelated frequencies elicited by the rhythms, i.e., the contrast at perceptually-relevant timescales, using frequency tagging. This approach has proven to be a powerful tool for capturing the contrast in brain responses between periodically spaced time points with high signal-to-noise ratio and without assumptions about the latency or the shape of the response (Nozaradan, 2014;Rossion, 2014;Norcia et al, 2015;Nozaradan et al, 2017a;Rossion et al, 2020). Moreover, the approach also allows the overall gain of the response (i.e.…”