It’s my great pleasure to announce to the world (i.e., all 4 subscribed readers to this blog) that Alex B. Fine successfully defended his thesis entitled “Prediction, Error, and Adaptation During Online Sentence Comprehension” jointly advised by Jeff Runner and me. Alex is the first HLP lab graduate (who started his graduate studies in the lab), so we gave him a very proper send-off and roasted the heck out of him. Alex will be starting his post-doc at the University of Illinois Psychology Department in June, working with Gary Dell, Sarah Brown-Schmidt, and Duane Watson.
Alex’s thesis investigates syntactic expectation adaptation. Work over the last two decades has firmly established that language comprehension is experience- or, more precisely- expectation-based: comprehenders draw on previous experience in order to robustly and efficiently predict (and thereby understand) the linguistic signal. Yet sociolinguistic and variationist work has documented that speakers and writers differ in their production preferences — the same message might be realized with different phonetic, lexical, and syntactic material. This raises a question, not previously acknowledged in the literature on sentence processing: how can it be that comprehension seems to make efficient use of probabilistic beliefs about linguistic distributions if the statistics of these distributions depend on the speaker, register, style, etc.? If the systems underlying language comprehension have evolved to efficiently deal with such (subjective) non-stationarity, we might expect comprehenders to a) store and use environment-specific (e.g., speaker-specific) syntactic expectations based previous experience, b) readily generalize based on these previous experience to novel environments, and c) integrate information about novel environments with previous experience. Alex’s thesis focuses on the third prediction. In series of self-paced reading experiments, he presents evidence i) that readers implicitly adapt their syntactic expectations to converge towards the statistics of the current linguistic environment, ii) that these effects cannot be reduced to task-based learning, floor-effects, or saturation effects, iii) that the time-course of such syntactic expectation adaptation depends both the on the prior statistics in a comprehender’s life-long experience and on its statistics in the current environment, iv) that syntactic expectation adaptation can be seen as cumulative syntactic priming (for those who prefer to think about phenomena rather than theories), and v) that the strength of syntactic priming in comprehension is sensitive to the prediction error experience while processing the prime structure.
If you’re interested in his work, his work on the role of prediction errors in syntactic priming has recently appeared in Cognitive Science (Fine, A. B. and Jaeger, T. F. 2013. Evidence for implicit learning in syntactic comprehension. Cognitive Science 37(3), 578–591).
Another paper, summarizing the first few studies of his thesis is currently under review. Preliminary reports of other studies can be found in various CogSci proceedings from 2010-2013, e.g.:
- Preliminary modeling work:
- Fine, A. B., Qian, T., Jaeger, T. F., and Jacobs, R. 2010. Is there syntactic adaptation in language comprehension. Proceedings of ACL: Workshop on Cognitive Modeling and Computational Linguistics, 18-26.Uppsala, Sweden.
- Kleinschmidt, D.F., Fine, A.B., and Jaeger, T.F. 2012. A belief-updating model of adaptation and cue combination in syntactic comprehension. The 34th Annual Meeting of the Cognitive Science Society (CogSci12). Sapporo, Japan. July, 2012.
- More evidence for the account:
- Fine, A. B. and Jaeger, T. F. 2013. Syntactic priming in language comprehension allows linguistic expectations to converge on the statistics of the input. In TBA (eds.) Proceedings of the 35th Annual Meeting of the Cognitive Science Society (CogSci13), XXX-XXX. Austin, TX: Cognitive Science Society.