Contextual modulation of language comprehension in a dynamic neural model of lexical meaning

TL;DR

This paper presents a dynamic neural model of lexical meaning that explains how context influences word interpretation, demonstrating its predictions through simulations and an experiment on the word 'have' in English.

Contribution

It introduces a novel dynamic neural model that captures contextual modulation of lexical semantics and predicts the relationship between reading time and acceptability, supported by empirical data.

Findings

Model captures contextual modulation of lexical interpretation

Simulations replicate empirical observations of individual variation

Experiment supports the model's novel prediction about reading time and acceptability

Abstract

We computationally implement and experimentally test the behavioral predictions of a dynamic neural model of lexical meaning in the framework of Dynamic Field Theory. 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 independently motivated continuous conceptual dimensions, connectedness and control asymmetry. 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 transiently stable 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 partially bears out the model's novel prediction. Altogether, results support a novel perspective on lexical polysemy: that the many related meanings of a word are not categorically distinct representations; rather, they are transiently stable neural activation states that arise from the nonlinear dynamics of neural populations governing interpretation on continuous semantic dimensions. Our model offers important advantages over related models in the dynamical systems framework, as well as models based on Bayesian inference.