학술논문

The angry versus happy recognition advantage: the role of emotional and physical properties.
Document Type
Article
Source
Psychological Research. Feb2023, Vol. 87 Issue 1, p108-123. 16p. 1 Color Photograph, 1 Diagram, 3 Charts, 3 Graphs.
Subject
*FACIAL expression & emotions (Psychology)
*STIMULUS & response (Psychology)
*EMOTION recognition
*EMOTIONAL conditioning
*FACIAL expression
*RECOGNITION (Psychology)
Language
ISSN
0340-0727
Abstract
Facial emotional expressions are pivotal for social communication. Their fast and accurate recognition is crucial to promote adaptive responses to social demands, for the development of functional relationships, and for well-being. However, the literature has been inconsistent in showing differentiated recognition patterns for positive vs. negative facial expressions (e.g., happy and angry expressions, respectively), likely due to affective and perceptual factors. Accordingly, the present study explored differences in recognition performance between angry and happy faces, while specifically assessing the role of emotional intensity and global/regional low-level visual features. 98 participants categorized angry and happy faces morphed between neutral and emotional across 9 levels of expression intensity (10–90%). We observed a significantly higher recognition efficiency (higher accuracy and shorter response latencies) for angry compared to happy faces in lower levels of expression intensity, suggesting that our cognitive resources are biased to prioritize the recognition of potentially harmful stimuli, especially when briefly presented at an ambiguous stage of expression. Conversely, an advantage for happy faces was observed from the midpoint of expression intensity, regarding response speed. However, when compensating for the contribution of regional low-level properties of distinct facial key regions, the effect of emotion was maintained only for response accuracy. Altogether, these results shed new light on the processing of facial emotional stimuli, emphasizing the need to consider emotional intensity and regional low-level image properties in emotion recognition analysis. [ABSTRACT FROM AUTHOR]