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类别相关性可减弱人类预测性学习中的掩蔽效应。

Category relevance attenuates overshadowing in human predictive learning.

机构信息

School of Psychology, University of Nottingham.

School of Psychology, University of Derby.

出版信息

J Exp Psychol Anim Learn Cogn. 2023 Jul;49(3):162-178. doi: 10.1037/xan0000357.

DOI:10.1037/xan0000357
PMID:37439744
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10339660/
Abstract

In situations in which multiple predictors anticipate the presence or absence of an outcome, cues compete to anticipate the outcome, resulting in a loss of associative strength compared to control conditions without additional cues. Critically, there are multiple factors modulating the magnitude and direction of such competition, although in some scenarios the effect of these factors remains unexplored. We sought to assess whether the relative salience of the elements in a compound of cues modulates the magnitude of the overshadowing effect in human predictive learning. Two separable categories (i.e., colors and symbols) were used in a predictive learning task. In Experiment 1, different groups of participants were granted with different time of exposure to a compound of cues belonging to different categories (color and symbol) to evaluate potential differences in the magnitude of overshadowing. Furthermore, we used posttest questionnaires to assess whether participants used either only one or both categories during training, and assessed if this impacted the magnitude of overshadowing. In general, overshadowing was not modulated by the time of exposition, except in the case of very short time of exposition with prominent learning about the most salient category. In Experiment 2, the relative salience of a category was biased via prior experience either with a biconditional discrimination or attending only the relevant category (either color or symbol). The previously relevant category was less prone to overshadowing, but not the alternative one. Results are discussed in light of attentional and configural theories of associative learning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

摘要

在多个预测因子预测结果的存在或不存在的情况下,线索会相互竞争来预测结果,与没有额外线索的对照条件相比,导致关联强度的损失。关键是,有多种因素调节这种竞争的幅度和方向,尽管在某些情况下,这些因素的影响仍未得到探索。我们试图评估在复合线索中元素的相对显著性是否会调节人类预测性学习中的遮蔽效应的幅度。在预测性学习任务中使用了两个可分离的类别(即颜色和符号)。在实验 1 中,不同组的参与者被给予不同的时间来暴露于属于不同类别的复合线索(颜色和符号),以评估遮蔽效应幅度的潜在差异。此外,我们使用后测试问卷来评估参与者在训练期间是否仅使用了一个或两个类别,以及这是否会影响遮蔽效应的幅度。总的来说,遮蔽效应不受暴露时间的调节,除非在非常短的暴露时间和对最显著类别的突出学习的情况下。在实验 2 中,通过双条件辨别或仅关注相关类别(颜色或符号)的先前经验,对类别相对显著性进行了偏向。先前相关的类别不太容易受到遮蔽效应的影响,但另一个类别则不会。结果在关联学习的注意和组态理论的背景下进行了讨论。(PsycInfo 数据库记录(c)2023 APA,保留所有权利)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/400b83791c12/xan_49_3_162_fig6a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/0e05a39f74bb/xan_49_3_162_fig1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/e604715098fc/xan_49_3_162_fig2a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/1622715a825b/xan_49_3_162_fig3a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/87fb1ce0d220/xan_49_3_162_fig4a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/e4927ae6f741/xan_49_3_162_fig5a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/400b83791c12/xan_49_3_162_fig6a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/0e05a39f74bb/xan_49_3_162_fig1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/e604715098fc/xan_49_3_162_fig2a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/1622715a825b/xan_49_3_162_fig3a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/87fb1ce0d220/xan_49_3_162_fig4a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/e4927ae6f741/xan_49_3_162_fig5a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4199/10339660/400b83791c12/xan_49_3_162_fig6a.jpg

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Psychon Bull Rev. 2023 Feb;30(1):350-361. doi: 10.3758/s13423-022-02155-4. Epub 2022 Aug 11.
3
Temporal and spatial contiguity are necessary for competition between events.时间和空间的接近是事件竞争的必要条件。
J Exp Psychol Learn Mem Cogn. 2022 Mar;48(3):321-347. doi: 10.1037/xlm0001108.
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The retrosplenial cortex as a possible "sensory integration" area: A neural network modeling approach of the differential outcomes effect in negative patterning.后扣带皮层作为一个可能的“感觉整合”区域:负面模式化中差异结果效应的神经网络建模方法。
Neurobiol Learn Mem. 2021 Nov;185:107527. doi: 10.1016/j.nlm.2021.107527. Epub 2021 Sep 27.
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Mitigating cue competition effects in human category learning.减轻人类类别学习中的线索竞争效应。
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