6 - The Modular Statistical Hypothesis: Exploring Lexical Category Ambiguity

Summary

Introduction

A central topic of debate in the sentence processing community has been whether or not the Human Sentence-Processing Mechanism (HSPM) is composed of a number of articulated modules (Frazier, 1987) or a single homogenous processing unit (Trueswell & Tanenhaus, 1994). This debate is still current; new modular (Frazier & Clifton, 1996; Crocker, 1996; Stevenson, 1994) and interactive (MacDonald, Pearlmutter, & Seidenberg, 1994, Tanenhaus, Spivey-Knowlton & Hanna, this volume) models are being proposed that can explain a larger portion of the data than their predecessors. The modularity question has been inextricably intertwined with our theories of human parsing strategies, since such issues of architecture will determine what kinds of representations and knowledge the parser can bring to bear in making decisions. Rational motivation for a modular parsing subsystem also derives from the fact that current theories of syntax typically posit a restricted and specialised representational framework. A further argument for modularity concerns computational complexity: A module which need only consider restricted knowledge in making decisions can function more rapidly that one which does not.

In this chapter we develop the latter, computational arguments further by considering the role of probabilistic mechanisms within a modular language comprehension system. While the use of frequency information has standardly been associated with nonmodular, interactive architectures (Spivey-Knowlton & Eberhard, 1996), we argue that the use of frequency-based heuristics is in fact a more natural ally of modular systems.