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

Divya Prabhakaran, Uli Grasemann, Swathi Kiran, Risto Miikkulainen

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In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

4 authors.

Divya PrabhakaranThe University of Texas at Austin, Austin, TX 78712 USA.

Uli GrasemannThe University of Texas at Austin, Austin, TX 78712 USA.

Swathi KiranBoston University, Boston, MA 02215 USA.

Risto MiikkulainenThe University of Texas at Austin, Austin, TX 78712 USA.

Funding

Computational modeling of language impairment and control in bilingual individuals with post-stroke aphasia and neurodegenerative disordersR01DC020653 · NIDCD · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Swathi Kiran, RISTO MIIKKULAINEN · 2023 to 2026

$2.6M

NIDCD NIH HHS R01 DC020653

6 · The paper itself

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 speed-accuracy tradeoffs, 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.

Indexed as

Cognitive controlComputational modelingNeural networksSelf-organizing mapsStroop task

Identifiers

PMID39975444

PMCPMC11838785

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.