A Functional Coupling of Brain and Behavior During Social Categorization of Faces

Trial-level ERPs predicted behavioral responses during self-referential processing in late childhood

Self-referential information is uniquely salient and preferentially processed even in children. The literature has used the self-referent encoding task (SRET) combined with event-related potentials (ERPs) to study self-referential processing and its associations with youth psychopathology. However, it is unclear how the ERP and behavioral indices of SRET are associated with each other, although this knowledge can promote our mechanistic understanding of this construct and its role in psychopathology. We examined this question in 115 9- to 12-year-old children, a critical period for the development of self-related concepts. By applying a multilevel modeling approach to the trial-level data of SRET, we disaggregated the between- and within-person variability and observed within-person, but not between-person, effects of the P2 and late positive potential (LPP) on behavioral responses: a larger P2 on a given trial predicted a faster response in this trial; a larger LPP on a given trial predicted a higher likelihood of endorsing the word of this trial. We provided novel evidence on how the within-person variability of the ERPs predicted the overt responses of the SRET in children. These findings inform our mechanistic knowledge of self-referential processing and shed light on a better understanding of the role of self-referential processing in the development of psychopathology.

Default categorization of outgroup faces and the other race effect: Commentary on the special issue

This commentary addresses how studies examining the neurophysiological correlates of racial categorization can provide insight into the neurocognitive mechanisms of the other-race effect in recognition memory. Several articles in the special issue describe how event-related potentials (ERPs) have been used to examine processing of faces that vary according to race, some of which have concluded that larger ERP amplitudes elicited by other-race (relative to own-race) faces indicates less efficient visual processing of other-race faces. I describe findings from ERP studies of race categorization that suggest an alternative interpretation-that other-race faces elicit stronger categorization, which impedes individuation of other-race faces. Suggestions for future research are offered.

A novel dual-modal emotion recognition algorithm with fusing hybrid features of audio signal and speech context

With regard to human–machine interaction, accurate emotion recognition is a challenging problem. In this paper, efforts were taken to explore the possibility to complete the feature abstraction and fusion by the homogeneous network component, and propose a dual-modal emotion recognition framework that is composed of a parallel convolution (Pconv) module and attention-based bidirectional long short-term memory (BLSTM) module. The Pconv module employs parallel methods to extract multidimensional social features and provides more effective representation capacity. Attention-based BLSTM module is utilized to strengthen key information extraction and maintain the relevance between information. Experiments conducted on the CH-SIMS dataset indicate that the recognition accuracy reaches 74.70% on audio data and 77.13% on text, while the accuracy of the dual-modal fusion model reaches 90.02%. Through experiments it proves the feasibility to process heterogeneous information within homogeneous network component, and demonstrates that attention-based BLSTM module would achieve best coordination with the feature fusion realized by Pconv module. This can give great flexibility for the modality expansion and architecture design.

Brain System for Social Categorization by Narrative Roles

The cognitive system applies categorical thinking to facilitate perception of the rich environment around us. In person cognition, research has focused on the roles of gender, race, age, or appearance in social categorical thinking. Here we investigated how narrative roles, as portrayed by different cinematic characters, are categorized in the neurocognitive system. Under functional MRI, 17 human participants (7 females) were asked to make different judgments regarding personality traits of 16 renowned cinematic characters representing four roles: hero, sidekick, mentor, and villain. Classification analysis showed a brain network, comprising the dorsal medial prefrontal cortex, the precuneus and the temporoparietal junction bilaterally, and the left occipital face area (OFA), to discriminate among the four roles. No such classification was found between other individual attributes including age or the associated film. Moreover, regions overlapping the default mode network (DMN) were found to better discriminate between roles, rather than the individual characters, while the OFA was found to better discriminate between individuals. These results demonstrate the inherent role of roles in person cognition, and suggest an intimate relation between roles categorization and self-referential activity.SIGNIFICANCE STATEMENT Social categorization, the assignment of different people in our social network to subgroups, is a powerful strategy in social cognition. How is this managed by the brain? We provide evidence that different characters from different stories, representing similar roles in their corresponding narrative, elicit similar brain activation patterns, as revealed by functional MRI. Unlike previous studies of social categorization, these brain activations were similar to those elicited by social cognition rather than face processing, and included regions at the prefrontal cortex, the precuneus, and the temporoparietal junction. The identified brain network significantly overlapped the default mode network. We suggest that social categorization by roles is fundamental to the cognitive system, relying on brain regions related to social cognition.

Open Science in human electrophysiology

Open science practices are gaining momentum in psychophysiological research, but at the nascent stage of this special issue in the International Journal of Psychophysiology there was no systematic collection of resources to support the adoption of open science practices specific to studies of human electrophysiology (EEG). The purpose of this special issue was to gather and provide resources that identify the idiosyncratic considerations and implications of open science practices specifically for studies of human EEG and event-related potentials (ERPs). Papers also show the importance of promoting good scientific practices in the application of open science principles to EEG and ERPs. This introduction to the special issue provides a roadmap for identifying the resources necessary to begin and improve the application of open science principles to EEG and ERP research. We are optimistic that open science practices will help increase the robustness, rigor, and replicability of EEG research and ultimately become the norm in studies of EEG and ERPs.

Using multilevel models for the analysis of event-related potentials

Multilevel modeling (MLM) is becoming increasingly accessible and popular in the analysis of event-related potentials (ERPs). In this article, we review the benefits of MLM for analyzing psychophysiological data, which often contains repeated observations within participants, and introduce some of the decision-making points in the analytic process, including how to set up the data set, specify the model, conduct hypothesis tests, and visualize the model estimates. We highlight how the use of MLM can extend the types of theoretical questions that can be answered using ERPs, including investigations of how ERPs vary meaningfully across trials within a testing session. We also address reporting practices and provide tools to calculate effect sizes and simulate power curves. Ultimately, we hope this review contributes to emerging best practices for the use of MLM with psychophysiological data.