Skip to main content

Processing of Individual Items during Ensemble Coding of Facial Expressions.

Liang, H., Ji, L., Tong, K., Ren, N., Chen, W., Liu, C. and Fu, X., 2016. Processing of Individual Items during Ensemble Coding of Facial Expressions. Frontiers in Psychology, 7 (September), 1332.

Full text available as:

[img]
Preview
PDF (OPEN ACCESS ARTICLE)
Li et al 2016 Processing of Individual Items during Ensemble Coding of Facial Expressions.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

DOI: 10.3389/fpsyg.2016.01332

Abstract

There is growing evidence that human observers are able to extract the mean emotion or other type of information from a set of faces. The most intriguing aspect of this phenomenon is that observers often fail to identify or form a representation for individual faces in a face set. However, most of these results were based on judgments under limited processing resource. We examined a wider range of exposure time and observed how the relationship between the extraction of a mean and representation of individual facial expressions would change. The results showed that with an exposure time of 50 ms for the faces, observers were more sensitive to mean representation over individual representation, replicating the typical findings in the literature. With longer exposure time, however, observers were able to extract both individual and mean representation more accurately. Furthermore, diffusion model analysis revealed that the mean representation is also more prone to suffer from the noise accumulated in redundant processing time and leads to a more conservative decision bias, whereas individual representations seem more resistant to this noise. Results suggest that the encoding of emotional information from multiple faces may take two forms: single face processing and crowd face processin

Item Type:Article
ISSN:1664-1078
Uncontrolled Keywords:facial expression, emotion, individual representation, ensemble representation, processing resource, diffusion model
Group:Faculty of Science & Technology
ID Code:24732
Deposited By: Symplectic RT2
Deposited On:19 Sep 2016 13:56
Last Modified:14 Mar 2022 13:58

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -