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Should psycholinguists (still) care about symbolic parsers?

Aniello De Santo

University of Utah

Dewey 1101 Auditorium

An open question in psycholinguistics is to what degree the detailed structural analyses posited by theoretical syntactitians are relevant to the cognitive processes underlying sentence processing (Bresnan 1978, a.o.). In this talk, I overview a line of research recasting such question in a computational framework, specifying a transparent (i.e., interpretable) linking hypothesis between structure building operations and processing complexity via symbolic parsers for expressive grammar formalisms. I present work exploring how a parser for Minimalist grammars (Stabler, 1996; MGs) can explain known contrasts in sentence processing in terms of subtle structural differences between syntactic derivations. This model is especially suited to probe the relation between syntactic and processing complexity, as it specifies: 1) a formalized theory of syntax in the form of a rich grammar formalism; 2) a sound and complete parser; 3) a linking theory between syntactic assumptions and processing behavior, in the form of metrics measuring memory usage. Through a few case studies, I will show that predictors linking structure building operations to memory usage improve our ability to capture both off-line and online processing patterns, beyond the contribution of expectation-based approaches. I will then discuss how transparent parsing models of this kind allow us to address broader questions about fundamental principles in linguistic theory (e.g. locality and economy) and the nature of syntactic representations (e.g. gradience). By investigating a growing array of processing phenomena and theoretical constructs, this project aims to add support to computational modeling work arguing for the relevance of structure-building operations in developing plausible cognitive models of human sentence comprehension. It thus paves the way for a modern, empirically grounded, theoretically insightful reframing of the Derivational Theory of Complexity.