Response time modeling reveals multiple contextual cuing mechanisms

被引:19
作者
Sewell, David K. [1 ]
Colagiuri, Ben [2 ]
Livesey, Evan J. [2 ]
机构
[1] Univ Queensland, Sch Psychol, St Lucia, Qld 4072, Australia
[2] Univ Sydney, Sch Psychol, Sydney, NSW 2006, Australia
基金
澳大利亚研究理事会;
关键词
Contextual cuing; Visual search; Diffusion model; Computational modeling; Response times; DIFFUSION DECISION-MODEL; VISUAL-SEARCH; SPATIAL CONTEXT; ATTENTIONAL GUIDANCE; WORKING-MEMORY; PERCEPTUAL DISCRIMINATION; DYNAMIC NOISE; TARGET; RECOGNITION; CONSTRAINTS;
D O I
10.3758/s13423-017-1364-y
中图分类号
B841 [心理学研究方法];
学科分类号
040201 ;
摘要
Contextual cuing refers to a response time (RT) benefit that occurs when observers search through displays that have been repeated over the course of an experiment. Although it is generally agreed that contextual cuing arises via an associative learning mechanism, there is uncertainty about the type(s) of process(es) that allow learning to influence RT. We contrast two leading accounts of the contextual cuing effect that differ in terms of the general process that is credited with producing the effect. The first, the expedited search account, attributes the cuing effect to an increase in the speed with which the target is acquired. The second, the decision threshold account, attributes the cuing effect to a reduction in the response threshold used by observers when making a subsequent decision about the target (e.g., judging its orientation). We use the diffusion model to contrast the quantitative predictions of these two accounts at the level of individual observers. Our use of the diffusion model allows us to also explore a novel decision-level locus of the cuing effect based on perceptual learning. This novel account attributes the RT benefit to a perceptual learning process that increases the quality of information used to drive the decision process. Our results reveal both individual differences in the process(es) involved in contextual cuing but also identify several striking regularities across observers. We find strong support for both the decision threshold account as well as the novel perceptual learning account. We find relatively weak support for the expedited search account.
引用
收藏
页码:1644 / 1665
页数:22
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