Syntactic priming in language comprehension allows linguistic expectations to converge on the statistics of the input

Syntactic priming in language comprehension allows linguistic expectations to converge on the statistics of the input Alex B. Fine (afine@bcs.rochester.edu) Department of Brain & Cognitive Sciences and Department of Linguistics, University of Rochester Rochester, NY 14627 USA T. Florian Jaeger (fjaeger@bcs.rochester.edu) Department of Brain & Cognitive Sciences and Department of Computer Science, University of Rochester Rochester, NY 14627 USA Abstract Human language is characterized by variability in that the way in which language is used varies depending, for example, on facts about the identity of the speaker or author, the social context, and surrounding linguistic material. Variability poses formidable challenges to the systems underlying language comprehension, which are known to exploit statistical contingencies in the input to overcome the inherent noisiness of perception; nevertheless, we seem to comprehend language with apparent ease. How is this possible? Here we argue that we are able to comprehend language efficiently in part by continuously adapting to the statistics of novel linguistic situations. We argue further that adaptation specifically allows comprehenders’ expectations to converge towards the actual statistics of the linguistic input. Concretely, we show that readers can adjust their linguistic expectations in light of recent experience such that (a) previously difficult structures become easier to process, and, even more strikingly, (b) previously easy to process structures come to incur a processing cost. Keywords: Sentence processing; experience-based language processing; parsing; reading; learning; adaptation; priming Introduction Human language is variable in the sense that the way in which language is used varies across situations according to, for example, the social context, the surrounding linguistic material, and various facts about the identity of the speaker or author. Variability in this sense pervades our linguistic experience, and has been observed at virtually every level of linguistic representation. Despite the extent to which language use varies, communication is typically successful. That is, even when faced with novel speakers or accents, we seem to be able to quickly and accurately infer the messages intended by our interlocutors. Our apparent facility with language is particularly remarkable considering the extent to which linguistic experience has been demonstrated to play a role in language processing. Experience-based accounts of language processing hold that comprehenders generate expectations— about the probability of observing particular sounds, words, sentence structures, etc.—during online language processing, and that these expectations are informed by and reflect the statistics of previous linguistic experience. By generating expectations that reflect the actual distribution of events in the environment, comprehenders should, in principle, be able to reduce the average prediction error experienced during online processing, and thus process language efficiently. But if the distribution of words or sentence structures varies according to individual speakers, dialects, etc., then, at first blush, it is no longer clear that generating online linguistic expectations that reflect aggregate statistics over previous experience would be advantageous to the comprehender. How do we comprehend language as well as we do despite variability in the linguistic signal? Here we present evidence that comprehenders are able to rapidly adapt to or implicitly learn the statistics of novel linguistic situations, focusing specifically on sentence comprehension (“parsing”). We argue that syntactic adaptation allows comprehenders’ expectations about the statistics of the input to converge towards the actual statistics, providing an explanation for why experience- based processing is advantageous despite the variability present in the statistics of the signal. Our experiments build on and attempt to synthesize insights from three lines of research that have till now proceeded largely in parallel: (1) experience-based language processing (e.g., MacDonald, Pearlmutter, & Seidenberg, 1994), (2) syntactic priming (e.g., Traxler, 2008), and (3) research exploring the link between online processing and implicit learning (e.g., Misyak & Christiansen, 2012; Wells, Christiansen, Race, Acheson, & MacDonald, 2009). To test our hypothesis, we exploit a well-known temporary syntactic ambiguity that provides a window onto comprehenders’ expectations, illustrated in (1). 1. The experienced soldiers… a. …warned about the dangers before the midnight raid. b. …spoke about the dangers before the midnight raid. c. …warned about the dangers conducted the midnight raid. d. …who were warned about the dangers conducted the midnight raid. Verbs like warned give rise to temporary ambiguities since they may occur both as the main verb (MV) of a