Covert-attention shifting superseded: Simple visual search explained by a computational cognitive architecture with early vision limitations, eye movements, and task strategies

隐蔽性注意力转移已被取代:简单的视觉搜索可以用计算认知架构来解释,该架构考虑了早期视觉限制、眼动和任务策略。

阅读:1

Abstract

This article concerns simple visual-search tasks that require people to respond "yes" or "no" about whether a specified target object is present in stimulus displays containing relatively small numbers of typically simple objects. The currently most popular cognitive theories regarding human performance in these tasks claim that a person's response time depends on the number of shifts of covert visual attention required to choose the response. Such theories provide no significant roles for cognitive task strategies, eye movements, and early-vision limitations (e.g., lower visual resolution and increased crowding effects for displayed objects with greater retinal eccentricity). In contrast, the present research used the EPIC computational cognitive architecture to construct precise simulation models that rely on these more basic mechanisms without assuming any role for covert attention. Results from the simulations show that models systematically incorporating early-vision limitations, eye movements, and parsimonious cognitive task strategies may suffice to account precisely for both the speed and accuracy of human performance during simple visual search. These models succeed at fitting not only empirical data aggregated across participants but also data from different subsets of individual participants who had similar visual parameter values and task strategies. Thus, it appears that covert-attention shifting is not necessary to explain simple visual search. Future models of visual search can be made more veridical and complete by avoiding ill-defined concepts of attention and instead further developing theories of visual mechanisms, task strategies, and motor mechanisms to explain empirical phenomena.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。