Healthy aging delays the neural processing of face features relevant for behavior by 40 ms

Abstract

Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioral responses. Here, we addressed the critical challenge of understanding how aging changes neural information processing mechanisms to delay behavior. Young (20–36 years) and older (60–86 years) adults performed the basic social interaction task of detecting a face versus noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behavior, and also where, when and how EEG activity represents them. We found that occipital‐temporal pathway activity dynamically represents the eyes of the face images for behavior ~170 ms poststimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioral deficit of slower reaction times. Our results therefore demonstrate how aging can change neural information processing mechanisms that underlie behavioral slow down.

Different measures of holistic face processing tap into distinct but partially overlapping mechanisms

“…It might therefore be argued that while the first 'global' or holistic processing stage is elicited by all of these measures, individual differences in performance reflect the second 'local' stage. If this were true, then individual differences in measures of holistic processing may instead reflect variations in the ability to process the featural aspects of faces (see also Jaworska et al, 2019). This could explain the lack of relationship between measures found here and elsewhere.…”

Potentiated early neural responses to fearful faces are not driven by specific face parts

“…According to the registered data sampling plan, 87 participants were examined (19 male, 68 female). Similar decoding approaches to this study have been used in the context of group statistical analyses 71,72 , however, not related to emotional expressions. Therefore, power calculations were not possible.…”

Different computations over the same inputs produce selective behavior in algorithmic brain networks

“…As an alternative analysis to the linear modelling we use an information theoretic approach to quantify the interactions between the representations of the left and right lenses (Jaworska et al, 2020; Schyns, Zhan, Jack, & Ince, 2020). We use Gaussian-Copula Mutual Information (GCMI; Ince et al, 2017), which is a semi-parametric estimator of information theoretic quantities.…”