Brian Dillon will be giving a talk at the Ling Lang Lunch at Brown University's Cognitive, Linguistic, and Psychological Sciences department this Wednesday, November 14th. He will be presenting joint work with Lyn Frazier and Charles Clifton on "Syntactic complexity across the at-issue / not-at-issue divide."
Much work in psycholinguistics has been dedicated to uncovering the source of complexity effects in syntactic processing (Chomsky & Miller 1963; Gibson, 1998; Levy, 2007; Lewis, 1996; Lewis & Vasishth, 2005; Yngve, 1960; i.a.). There are many theoretical accounts of syntactic complexity effects, starting from Chomsky and Miller's (1963) observations on the difficulty of self-embedding, to the introduction of new discourse referents while simultaneously maintaining syntactic predictions (Gibson, 1998), among many others. One recent and influential model attempts to reduce syntactic complexity to interference effects related to memory retrieval (Lewis & Vasishth, 2005), In the present talk I present joint work with Lyn Frazier and Chuck Clifton that investigates the source of syntactic complexity by looking how the at-issue / not-at-issue distinction relates to syntactic complexity effects. Not-at-issue content like appositives and parentheticals do not directly contribute to the truth conditions of a sentence, and so have been argued to form a separate 'dimension' of meaning (Potts, 2005). In a series of judgment experiments, it is seen that syntactic complexity in the not-at-issue dimension does not lead to complexity effects in offline judgments, while complexity in at-issue content does. I then present eye-tracking data that helps to locate the source of the complexity effects in online comprehension. The results provide initial evidence that i) the parser distinguishes at-issue and not-at-issue content, and ii) the complexity effects observed in the present data cannot be reduced to retrieval interference. I suggest that at-issue / not-at-issue distinction is used to structure parsing routines by maintaining distinct stacks for different types of linguistic content, thereby minimizing complexity for the sentence as a whole.