An Artificial Grammar Investigation into the Mental Encoding of Syntactic Structure

• Pyeong Whan Cho, Department of Psychology Cognitive Science Program University of Connecticut Haskins Laboratories
• Emily Szkudlarek, Department of Psychology University of Connecticut
• Anuenue Kukona, Department of Psychology Cognitive Science Program University of Connecticut Haskins Laboratories
• Whitney Tabor, Department of Psychology Cognitive Science Program University of Connecticut Haskins Laboratories

Abstract

We explore neural network learning and parallel human learning on an artificial language task. The task generates rich data on human interaction with syntactic systems, including recursive ones. Studying the network’s properties, we argue for a “Structured Manifold” view of syntactic representation. The “Structured Manifold” lies in the parameter space (weight space) of the network. It exhibits (1) loci of high order, corresponding to complex rule systems, (2) continuity, which explains how one rule system can morph into another one, and (3) “recursion approximation”, a concept related to symbolic recursion, which addresses some of the puzzles about embedding patterns in human behavior.

• The Paper: An Artificial Grammar Investigation into the Mental Encoding of Syntactic Structure

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