Learning-induced changes in attentional allocation during categorization: a sizable catalog of attention change as measured by eye movements.

ABSTRACT: Learning how to allocate attention properly is essential for success at many categorization tasks. Advances in our understanding of learned attention are stymied by a chicken-and-egg problem: there are no theoretical accounts of learned attention that predict patterns of eye movements, making data collection difficult to justify, and there are not enough datasets to support the development of a rich theory of learned attention. The present work addresses this by reporting five measures relating to the overt allocation of attention across 10 category learning experiments: accuracy, probability of fixating irrelevant information, number of fixations to category features, the amount of change in the allocation of attention (using a new measure called Time Proportion Shift - TIPS), and a measu

SUBMITTER: McColeman CM 

PROVIDER: S-EPMC3908863 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature