Feature saliency and feedback information interactively impact visual category learning.

ABSTRACT: Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object's features most relevant for categorization, while 'filtering out' irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied

SUBMITTER: Hammer R 

PROVIDER: S-EPMC4333777 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature