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Quantitative Biology > Neurons and Cognition

arXiv:2502.02831v2 (q-bio)
[Submitted on 5 Feb 2025 (v1) , revised 7 Feb 2025 (this version, v2) , latest version 11 May 2025 (v3) ]

Title: How the Stroop Effect Arises from Optimal Response Times in Laterally Connected Self-Organizing Maps

Title: 如何从横向连接的自组织映射中的最优反应时间产生斯特鲁普效应

Authors:Divya Prabhakaran, Uli Grasemann, Swathi Kiran, Risto Miikkulainen
Abstract: The Stroop effect refers to cognitive interference in a color-naming task: When the color and the word do not match, the response is slower and more likely to be incorrect. The Stroop task is used to assess cognitive flexibility, selective attention, and executive function. This paper implements the Stroop task with self-organizing maps (SOMs): Target color and the competing word are inputs for the semantic and lexical maps, associative connections bring color information to the lexical map, and lateral connections combine their effects over time. The model achieved an overall accuracy of 84.2%, with significantly fewer errors and faster responses in congruent compared to no-input and incongruent conditions. The model's effect is a side effect of optimizing response times, and can thus be seen as a cost associated with overall efficient performance. The model can further serve studying neurologically-inspired cognitive control and related phenomena.
Abstract: 斯特鲁普效应指的是在颜色命名任务中的认知干扰:当颜色和单词不匹配时,反应更慢且更容易出错。斯特鲁普任务用于评估认知灵活性、选择性注意和执行功能。本文使用自组织映射(SOMs)实现了斯特鲁普任务:目标颜色和竞争性单词是语义和词汇映射的输入,关联连接将颜色信息带到词汇映射,侧向连接随时间结合它们的影响。该模型总体准确率为84.2%,在一致条件下比无输入和不一致条件下的错误更少,反应更快。该模型的效果是优化反应时间的副作用,因此可以视为整体高效表现相关的成本。该模型还可进一步用于研究神经学启发的认知控制及相关现象。
Comments: 7 pages, 6 figures
Subjects: Neurons and Cognition (q-bio.NC) ; Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2502.02831 [q-bio.NC]
  (or arXiv:2502.02831v2 [q-bio.NC] for this version)
  https://doi.org/10.48550/arXiv.2502.02831
arXiv-issued DOI via DataCite

Submission history

From: Divya Prabhakaran [view email]
[v1] Wed, 5 Feb 2025 02:14:09 UTC (553 KB)
[v2] Fri, 7 Feb 2025 01:21:17 UTC (553 KB)
[v3] Sun, 11 May 2025 16:05:58 UTC (1,007 KB)
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