Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements


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… (More)
DOI: 10.1371/journal.pone.0083302


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