An Architecturally-based Theory of Human Sentence Comprehension

This thesis presents NL-Soar, a detailed computational model of human sentence comprehension that accounts for a broad range of psycholinguistic phenomena. NL-Soar provides in-depth accounts of structural ambiguity resolution, garden path effects, unproblematic ambiguities, parsing breakdown on difficult embeddings, acceptable embeddings, immediacy of interpretation, and the time course of comprehension. The model explains a variety of both modular and interactive effects, and shows how learning can affect ambiguity resolution behavior. In addition to accounting for the qualitative phenomena surrounding parsing breakdown and garden path effects, NL-Soar explains a wide range of contrasts between garden paths and unproblematic ambiguities, and difficult and acceptable embeddings: the theory has been applied in detail to over 100 types of structures representing these contrasts, with a success rate of about 90%. The account of real-time immediacy includes predictions about the time course of comprehension and a zero-parameter prediction about the average rate of skilled comprehension. Finally, the theory has been successfully applied to a suggestive range of cross-linguistic examples, including constructions from head-final languages such as Japanese. NL-Soar is based on the Soar theory of cognitive architecture, which provides the underlying control structure, memory structures, and learning mechanism. The basic principles of NLSoar are a result of applying these architectural mechanisms to the task of efficiently comprehending language in real-time. Soar is more than an implementation language for the system: it plays a central theoretical role and accounts for many of the model’s novel empirical predictions.