🤖 AI Summary
How do dynamic contexts modulate real-time semantic interpretation of polysemous words (e.g., English *have*)?
Method: We propose a neurodynamic model grounded in neural field theory, representing *have*’s polysemy as metastable activation patterns emerging from nonlinear population dynamics along continuous semantic dimensions—specifically, connectivity and control asymmetry. This framework formalizes lexical polysemy as a continuous, time-evolving, and inter-individually variable neural state trajectory. We test it using self-paced reading combined with acceptability judgment experiments.
Contribution/Results: The model successfully reproduces contextual modulation and individual differences in processing, and generates a novel, empirically confirmed prediction: the relationship between sentence reading time and acceptability is dynamically regulated by context. By unifying context representation, individual variability, and real-time language comprehension within a single computational architecture, this work provides a testable, neurally plausible account of the dynamic neurocognitive mechanisms underlying lexical semantics.
📝 Abstract
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.