  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.
