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

TL;DR

This paper models the Stroop effect using self-organizing maps, demonstrating how optimal response times can lead to cognitive interference phenomena similar to those observed in humans.

Contribution

It introduces a novel neural network model that explains the Stroop effect as a consequence of optimizing response times in a self-organizing map framework.

Findings

Achieved 84.2% accuracy in the model

Fewer errors and faster responses in congruent conditions

Model explains Stroop effect as a side effect of efficiency

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.