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在不同的、复杂的单一和多感官环境中,明确知识、学习动态和时间期望之间的相互作用最小。

Minimal interplay between explicit knowledge, dynamics of learning and temporal expectations in different, complex uni- and multisensory contexts.

机构信息

Department of Biological Psychology, Faculty of Natural Science, Otto-von-Guericke-University Magdeburg, PO Box 4120, 39106, Magdeburg, Germany.

Center for Behavioral Brain Sciences, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.

出版信息

Atten Percept Psychophys. 2021 Aug;83(6):2551-2573. doi: 10.3758/s13414-021-02313-1. Epub 2021 May 11.

Abstract

While temporal expectations (TE) generally improve reactions to temporally predictable events, it remains unknown how the learning of temporal regularities (one time point more likely than another time point) and explicit knowledge about temporal regularities contribute to performance improvements; and whether any contributions generalise across modalities. Here, participants discriminated the frequency of diverging auditory, visual or audio-visual targets embedded in auditory, visual or audio-visual distractor sequences. Temporal regularities were manipulated run-wise (early vs. late target within sequence). Behavioural performance (accuracy, RT) plus measures from a computational learning model all suggest that learning of temporal regularities occurred but did not generalise across modalities, and that dynamics of learning (size of TE effect across runs) and explicit knowledge have little to no effect on the strength of TE. Remarkably, explicit knowledge affects performance-if at all-in a context-dependent manner: Only under complex task regimes (here, unknown target modality) might it partially help to resolve response conflict while it is lowering performance in less complex environments.

摘要

虽然时间预期(TE)通常会改善对时间可预测事件的反应,但目前尚不清楚时间规律(一个时间点比另一个时间点更有可能)的学习以及关于时间规律的明确知识如何有助于提高表现;以及任何贡献是否可以跨模态推广。在这里,参与者在听觉、视觉或视听分散序列中辨别了发散的听觉、视觉或视听目标的频率。时间规律在运行时(序列内的早期与晚期目标)进行了操纵。行为表现(准确性、反应时间)加上计算学习模型的度量都表明,时间规律的学习发生了,但没有跨模态推广,学习的动态(运行中 TE 效应的大小)和明确的知识对 TE 的强度几乎没有影响。值得注意的是,明确的知识会以依赖于上下文的方式影响表现:只有在复杂的任务环境中(这里是未知的目标模态),它可能会部分帮助解决响应冲突,同时在不那么复杂的环境中降低表现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc82/8302534/b0132f4e1155/13414_2021_2313_Fig1_HTML.jpg

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