Semantics and Syntax Co-emerge in Adaptive Reservoir Network Dynamics
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It is well known that a word can carry different meanings in different contexts, but how exactly do lexical semantics interact with a broader syntactic context? On the one hand, meaning at the lexical level may influence how one syntactically parses the sentence within which it is embedded. Snedeker & Trueswell (2004) found behavioral evidence that the interpretation of syntactically ambiguous verb-argument structures is influenced by a verb’s bias towards one of these structures or another, and more recent evidence from Ryskin et al. (2017) indicates that these verb biases can be manipulated through experience. This constitutes behavioral evidence that semantics can influence syntax in language use. On the other hand, in neural network simulations, changes in lexico-syntactic context have been shown to subtly modulate the population encoding of lexical inputs, or, in other words, induce shifts in semantics. (Elman, 2009). These findings suggest that the distinction between semantics and syntax may be more conceptual than it is indicative of two natural kinds. Building on the findings of Falandays et al. (2021), we test if an unsupervised, adaptive reservoir computing network is able to learn long-distance dependencies in a simple linguistic environment of sentences containing verbs which have differential biases toward possible arguments. The future goal of this work is to use this model to explore if changes in the statistical patterns of verb bias result in semantic shifts. This work uses computational modeling to explore how semantics and syntax can co-emerge as linguistic properties from simple adaptive behaviors of a cognitive agent entraining with its environment.