cs.CLOct 16, 2026

Modeling bilingual naming performance through laterally-connected SOMs

Authors: Divya Prabhakaran, Uli Grasemann, Erie Shivers, Marissa Russell-Meill, Swathi Kiran, Risto Miikkulainen

Organizations: Department of Computer Science, The University of Texas at Austin · Center for Brain Recovery, Boston University

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Abstract

Computational models have explanatory power to understand cognition in individuals, but there is usually not enough training data to build such models with traditional machine learning. However, alternative architectures that model neural and cognitive processes in more detail can be more easily fit to individuals and can lead to mechanistic insights into human behavior. BiLex is such a computational model of the bilingual lexicon, containing a shared semantic system and phonetic language systems represented through self-organizing maps. In this study, BiLex was extended further into biological realism with excitatory and inhibitory lateral connections. This extension makes it possible to account for response times as well as common errors such as those based on semantic concept familiarity, typicality, and specificity. The Boston Naming Test was simulated in Spanish and English, and model performance approximated naming accuracy and error types in Spanish-English bilinguals with varying primary and secondary language histories. The results indicated that the approach captures semantic-level processes in bilingual lexical access, thus modeling the semantic cognition of individual subjects. In the future, such models may serve as a foundation for modeling impairments and treatment options.

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Figure 1: Laterally-connected BiLex model. The model simulated naming by receiving input to the semantic map, propagating activation through lateral connections and associative connections across all maps, and producing a response from the target language map. This architecture is motivated by the lexical-semantic structures in the brain, and can therefore serve as a foundation for modeling individualized performance, its breakdown in e.g. stroke and dementia, and identifying effective treatments ( Kiran et al., 2026 ) . Figure adapted from ( Peñaloza et al., 2019 )
Figure 2: Distributions of responses in the BNT naming task in humans vs. the model. One-sample Wilcoxon tests were conducted on the L2-English early-AoA group ( 2(e) , 2(f) ), with ”*” indicating statistically significant and ”ns” non-significant differences. The model approximated human accuracy and error distributions well qualitatively, and also quantitatively when sufficient data were available.
Figure 3: Regression analysis comparing BNT word frequency to naming accuracy rates in humans vs. the model. Frequency slopes indicate L1 vs. L2 naming accuracy gaps. Positive word frequency vs. accuracy trends were present in humans and the model (significant in only L2-English-early) across bilingual groups, suggesting the model simulated human behavior of greater accuracy when naming higher-frequency words.
Figure 4: Adjusting settling to fit individual performance. As tmaxt_{\mathrm{max}} increased in the L2-Spanish-early simulation, naming accuracy increased in both English and Spanish from 75 to 400 steps. At tmax=600t_{\mathrm{max}}=600 to tmax=1000t_{max}=1000 , accuracy plateaued. Thus, tmaxt_{\mathrm{max}} can be used to fit model performance to individual subjects.
Figure 5: Distribution of naming error types in the L2-Spanish-early model. Decreasing the model’s settling time ( tmaxt_{\mathrm{max}} ) increased the percentage of no response errors. Increasing tmaxt_{\mathrm{max}} enabled the model to produce a wider variety of human-like errors. For all groups, tmax=400t_{\mathrm{max}}=400 was chosen to balance error matching and overall naming accuracy.
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