Literature DB >> 23151960

Temporal characteristics of overt attentional behavior during category learning.

Lihan Chen1, Kimberly M Meier, Mark R Blair, Marcus R Watson, Michael J Wood.   

Abstract

Many theories of category learning incorporate mechanisms for selective attention, typically implemented as attention weights that change on a trial-by-trial basis. This is because there is relatively little data on within-trial changes in attention. We used eye tracking and mouse tracking as fine-grained measures of attention in three complex visual categorization tasks to investigate temporal patterns in overt attentional behavior within individual categorization decisions. In Experiments 1 and 2, we recorded participants' eye movements while they performed three different categorization tasks. We extended previous research by demonstrating that not only are participants less likely to fixate irrelevant features, but also, when they do, these fixations are shorter than fixations to relevant features. We also found that participants' fixation patterns show increasingly consistent temporal patterns. Participants were faster, although no more accurate, when their fixation sequences followed a consistent temporal structure. In Experiment 3, we replicated these findings in a task where participants used mouse movements to uncover features. Overall, we showed that there are important temporal regularities in information sampling during category learning that cannot be accounted for by existing models. These can be used to supplement extant models for richer predictions of how information is attended to during the buildup to a categorization decision.

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Year:  2013        PMID: 23151960     DOI: 10.3758/s13414-012-0395-8

Source DB:  PubMed          Journal:  Atten Percept Psychophys        ISSN: 1943-3921            Impact factor:   2.199


  6 in total

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2.  An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features.

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Journal:  PLoS One       Date:  2015-08-14       Impact factor: 3.240

3.  Feature saliency and feedback information interactively impact visual category learning.

Authors:  Rubi Hammer; Vladimir Sloutsky; Kalanit Grill-Spector
Journal:  Front Psychol       Date:  2015-02-19

4.  The ubiquity of selective attention in the processing of feedback during category learning.

Authors:  Katerina Dolguikh; Tyrus Tracey; Mark R Blair
Journal:  PLoS One       Date:  2021-12-16       Impact factor: 3.240

5.  LAG-1: A dynamic, integrative model of learning, attention, and gaze.

Authors:  Jordan Barnes; Mark R Blair; R Calen Walshe; Paul F Tupper
Journal:  PLoS One       Date:  2022-03-17       Impact factor: 3.240

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

Authors:  Caitlyn M McColeman; Jordan I Barnes; Lihan Chen; Kimberly M Meier; R Calen Walshe; Mark R Blair
Journal:  PLoS One       Date:  2014-01-31       Impact factor: 3.240

  6 in total

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