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Quantitative Biology > Neurons and Cognition

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

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

Title: 侧向连接的自组织映射中最佳反应时间导致 stroop 效应的产生

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: Stroop效应是指在颜色命名任务中的认知干扰: 当颜色和词语不匹配时,反应时间更长,并且更容易出错。 Stroop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(SOM)实现Stroop任务:目标颜色和竞争性词语是语义和词汇映射的输入,关联连接将颜色信息传递到词汇映射,侧向连接则随着时间结合它们的影响。 该模型的整体准确率为84.2%,与无输入和不一致条件相比,在一致条件下错误明显更少且反应更快。 模型的效果是优化反应时间的副产物,因此可以被视为高效整体性能的成本。 该模型还可以进一步用于研究神经启发的认知控制及相关现象。
Comments: Accepted to CogSci 2025 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:2502.02831v3 [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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