Temporal characteristics of overt attentional behavior during category learning

被引:131
作者
Chen, Lihan [1 ]
Meier, Kimberly M. [3 ]
Blair, Mark R. [1 ,2 ]
Watson, Marcus R. [3 ]
Wood, Michael J. [2 ]
机构
[1] Simon Fraser Univ, Cognit Sci Program, Burnaby, BC V5A 1S6, Canada
[2] Simon Fraser Univ, Dept Psychol, Burnaby, BC V5A 1S6, Canada
[3] Univ British Columbia, Dept Psychol, Vancouver, BC, Canada
基金
加拿大创新基金会; 加拿大自然科学与工程研究理事会;
关键词
Attention; Eye tracking; Categorization; Eye movements; Optimization; Learning; Error; Modeling; Temporal regularity; Visual cognition; SELECTIVE ATTENTION; PRESENTATION ORDER; RESPONSE-TIMES; EYE-MOVEMENTS; MODEL; EYETRACKING; ALLOCATION; WORKING; MEMORY;
D O I
10.3758/s13414-012-0395-8
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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.
引用
收藏
页码:244 / 256
页数:13
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