Contextual modulation of language comprehension in a dynamic neural model of lexical meaning
<|reference_start|>Contextual modulation of language comprehension in a dynamic neural model of lexical meaning: We propose and computationally implement a dynamic neural model of lexical meaning, and experimentally test its behavioral predictions. We demonstrate the architecture and behavior of the model using as a test case the English lexical item 'have', focusing on its polysemous use. In the model, 'have' maps to a semantic space defined by two continuous conceptual dimensions, connectedness and control asymmetry, previously proposed to parameterize the conceptual system for language. The mapping is modeled as coupling between a neural node representing the lexical item and neural fields representing the conceptual dimensions. While lexical knowledge is modeled as a stable coupling pattern, real-time lexical meaning retrieval is modeled as the motion of neural activation patterns between metastable states corresponding to semantic interpretations or readings. Model simulations capture two previously reported empirical observations: (1) contextual modulation of lexical semantic interpretation, and (2) individual variation in the magnitude of this modulation. Simulations also generate a novel prediction that the by-trial relationship between sentence reading time and acceptability should be contextually modulated. An experiment combining self-paced reading and acceptability judgments replicates previous results and confirms the new model prediction. Altogether, results support a novel perspective on lexical polysemy: that the many related meanings of a word are metastable neural activation states that arise from the nonlinear dynamics of neural populations governing interpretation on continuous semantic dimensions.<|reference_end|>
@article{stern2024contextual,
    title={Contextual modulation of language comprehension in a dynamic neural
  model of lexical meaning},
    author={Michael C. Stern and Maria M. Pi~nango},
    journal={arXiv preprint arXiv:2407.14701},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14701},
    primaryClass={cs.CL}
}