Task-specific codes for face recognition: how they shape the neural representation of features for detection and individuation.

ABSTRACT:

Background

The variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural processing.

Methodology/principal findings

Our study appeals to a computational framework characterizing the features representing object categories as sets of overlapping image fragments. Within this framework, we assess the extent to which task-relevant information differs across image fragments. Based on objective differences we find among task-specific representations, we test the s

SUBMITTER: Nestor A 

PROVIDER: S-EPMC2607027 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature