Feature saliency and feedback information interactively impact visual category learning

Frontiers in Psychology
Rubi HammerKalanit Grill-Spector

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 both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load...Continue Reading

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Citations

Jul 19, 2016·Topics in Cognitive Science·Philippe Vincent-LamarreStevan Harnad
Aug 16, 2017·Cognitive Processing·Alberto Greco, Stefania Moretti

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