From categorization to quantification: Stage-dependent neural and behavioral mechanisms of facial emotion recognition

NeuroImage, vol. 336, pp. 122011

Abstract

• FER follows a multi-stage processing architecture where categorical and intensity-related processing are temporally organized yet functionally dissociable; • Emotion categorization and intensity estimation show dissociable behavioral patterns across negative emotions; • Early components encode emotion category, while intensity sensitivity emerges exclusively at the late evaluative stage; • Early components increase processing costs on recognition, while late component facilitates behavioral performance. Facial emotion recognition (FER) is a core process in social communication, involving the identification of emotion categories and the estimation of intensity. Although both are essential for adaptive social functioning, whether they rely on shared or partially differentiated neural processes remains unresolved. To address this issue, the present study combined event-related potential (ERP) measures with trial-by-trial brain-behavior modeling using (generalized) linear mixed-effects models, i.e., (G)LMMs, in 44 healthy adults performing sequential emotion categorization and intensity rating on morphed faces varying in category (happiness, anger, fear, and sadness) and intensity (25%, 50%, 75%, and 100%). Behaviorally, categorization reflected categorical distinctions, whereas intensity estimation revealed emotion-specific patterns that did not align with categorization. At the neural level, ERP results revealed a temporally ordered pattern. Early (P1, N170) and mid-latency (EPN) components were modulated by category, while sensitivity to intensity emerged only at the late stage (LPP), indicating a temporal difference between structural encoding and evaluative integration. Brain-behavior modeling further revealed stage-specific predictive associations. In categorization, early and mid-latency components were negatively associated with performance, while the late components (LPP) indicated positive predictive associations; in intensity estimation, these associations varied with emotional category and intensity. Together, these findings suggested that within a fixed sequential judgment paradigm, intensity-related evaluation showed behavioral and brain-behavior association profiles that differed from those observed for categorization, without implying fully independent processes. By leveraging trial-by-trial brain-behavior modeling, this study uncovers stage-dependent predictive associations that traditional condition-average analyses cannot capture.

Authors 6

1. Wenbin Pan Aachen

   Leiden University · South China Normal University

   Affiliation as printed

   School of Psychology, South China Normal University, Guangzhou 510631, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; Department of Cognitive Psychology, Institute of Psychology, Leiden University, the Netherlands

2. Ran Zhou

   South China Normal University

   Affiliation as printed

   School of Psychology, South China Normal University, Guangzhou 510631, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China

3. Siying Li

   Shenzhen University

   Affiliation as printed

   School of Psychology, Shenzhen University, Shenzhen 518060, China

4. Qi Chen

   Shenzhen University

   Affiliation as printed

   School of Psychology, Shenzhen University, Shenzhen 518060, China

5. Junhua Dang corresponding

   Xi'an Jiaotong University

   Affiliation as printed

   Institute of Social Psychology, School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: dangjunhua@gmail.com

6. Hong Li corresponding

   South China Normal University · Sichuan Normal University

   Affiliation as printed

   School of Psychology, South China Normal University, Guangzhou 510631, China; Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China. Electronic address: lihong_psych@m.scnu.edu.cn