Temporal characteristics of overt attentional behavior during category learning
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- 作者：Lihan Chen (1)
Kimberly M. Meier (3)
Mark R. Blair (1) (2)
Marcus R. Watson (3)
Michael J. Wood (2) (4) - 关键词：Attention ; Eye tracking ; Categorization ; Eye movements ; Optimization ; Learning ; Error ; Modeling ; Temporal regularity ; Visual cognition
- 刊名：Attention, Perception, & Psychophysics
- 出版年：2013
- 出版时间：February 2013
- 年：2013
- 卷：75
- 期：2
- 页码：244-256
- 全文大小：573KB
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Kimberly M. Meier (3)
Mark R. Blair (1) (2)
Marcus R. Watson (3)
Michael J. Wood (2) (4)
1. Cognitive Science Program, Simon Fraser University, Burnaby, Canada
3. Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada
2. Department of Psychology, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
4. School of Psychology, University of Kent, Canterbury, UK
文摘
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.