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

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.

Keywords: Cognitive control; Computational modeling; Neural networks; Self-organizing maps; Stroop task.

Figure 1:

The BiLex model of the Stroop effect. The semantic color input and concurrent lexical input are combined in the lexical map, and a lexical output is generated naming the color (maroon in this case). When the inputs are congruent, the response is fast and reliable; when they are incongruent, slower and error-prone. The errors can be seen as a cost of optimization of overall response times.

Figure 2:

The organization and activation in the BiLex model of the Stroop Effect. The model implements SOMs with lateral connections, with map units representing 16 colors and words. The semantic map (top) was trained with RGB values of each color, and the lexical map (bottom) with Spanish phonetic representations of words for each color. Initial map activation for the color maroon/granate is depicted in the right column. Similar colors are nearby in the semantic map, and similar words in the lexical map. As a result, the map activations are smooth and continuous.

Figure 3:

Finding the best values for the $r_{\text{lex}}$ routing parameter. The normalized $e_i$ (red), $t_i$ (purple), and $t_c$ (green) components of the $q$ metric are depicted for each possible $r_{\text{lex}}$ value. The optimal routing parameter was found to be an $r_{\text{lex}}$ of 0.45, marked by the dashed black line, balancing color naming speed with minimized naming errors.

Figure 4:

The mean and standard error (SE) of RTs across the three conditions of the Stroop effect: congruent (red), no-input (blue), and incongruent (green). RTs improve significantly for congruent vs. no-input vs. incongruent conditions, indicating that the model exhibits a strong Stroop effect.

Figure 5:

The mean and SE of RTs in the model compared with human performance. Left: n = 98, ages: 37–55 years (Forte et al., 2024); right: n = 77, ages: 17–45 years (Wright, 2017). The vertical scales were adjusted to line up the averages; the SEs are mostly similar and the differences between the conditions are significant, suggesting that the model replicates the human data.

Figure 6:

The response of the model over time. Lexical map activation (right) is shown over time for incongruent inputs. Initially (at t = 0), map activation (left) only reflects the lexical input. However, as time progresses, the correct input (maroon) is transferred from the semantic to the lexical map and gains more weight (t = 30), eventually causing the lexical map to converge on the correct output (t = 127). It is this competition over time, mediated by the lateral connections, that results in the Stroop effect.

Figure 7:

Comparing the model response to the congruent and incongruent inputs. When the lexical map is given incongruent input (lime), the map takes significantly longer to focus on the specified color and cross the output threshold for uncertainty below 1.0 bit than when the inputs are congruent (maroon).