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Seeking predictions from a predictive framework

Published online by Cambridge University Press:  24 June 2013

T. Florian Jaeger  and
Victor Ferreira
T. Florian Jaeger
Affiliation:
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627-0268. [email protected]://www.hlp.rochester.edu/ Department of Computer Science, University of Rochester, Rochester, NY 14627.
Victor Ferreira
Affiliation:
Department of Psychology 0109, University of California, San Diego, La Jolla, CA 92093-0109. [email protected]://lpl.ucsd.edu/
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Abstract

We welcome the proposal to use forward models to understand predictive processes in language processing. However, Pickering & Garrod (P&G) miss the opportunity to provide a strong framework for future work. Forward models need to be pursued in the context of learning. This naturally leads to questions about what prediction error these models aim to minimize.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 4 , August 2013 , pp. 359 - 360
Copyright
Copyright © Cambridge University Press 2013 

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References

Aylett, M. & Turk, A. (2004) The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Language and Speech 47(1):31–56.Google Scholar
Baayen, R. H., Milin, P., Filipović Durdević, D., Hendrix, P. & Marelli, M. (2011) An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review 118:438–82.CrossRefGoogle ScholarPubMed
Brennan, S. E. & Clark, H. H. (1996) Conceptual pacts and lexical choice in conversation. Learning, Memory 22(6):1482–93.Google Scholar
Chang, F., Dell, G. S. & Bock, K. (2006) Becoming syntactic. Psychological Review 113(2):234–272.Google Scholar
Clark, A. (2013) Whatever next? Predictive brains, situated agents, and the future of cognitive science. Brain and Behavioral Sciences 36(3):181–253.Google Scholar
Clayards, M., Tanenhaus, M. K., Aslin, R. N. & Jacobs, R. A. (2008) Perception of speech reflects optimal use of probabilistic speech cues. Cognition 108(3):804–809.Google Scholar
Farmer, T. A., Brown, M. & Tanenhaus, M. K. (2013) Prediction, explanation, and the role of generative models in language processing. Brain and Behavioral Sciences 36(3):211–12.Google Scholar
Fedzechkina, M., Jaeger, T. F. & Newport, E. (2012) Language learners restructure their input to facilitate efficient communication. Proceedings of the National Academy of Sciences of the United States of America 109(44):17897–902.Google Scholar
Ferreira, V. S. (2008) Ambiguity, accessibility, and a division of labor for communicative success. Psychology of Learning and Motivation 49:209–46.Google Scholar
Fine, A. B. & Jaeger, T. F. (2013) Evidence for implicit learning in syntactic comprehension. Cognitive Science 37(3):578–91.Google Scholar
Fine, A. B., Jaeger, T. F., Farmer, T. & Qian, T. (submitted) Rapid linguistic adaptation during syntactic comprehension.Google Scholar
Frank, A. (2011) Integrating linguistic, motor, and perceptual information in language production. University of Rochester.Google Scholar
Guenther, F. H., Hampson, M. & Johnson, D. (1998) A theoretical investigation of reference frames for the planning of speech movements. Psychological Review 105(4):611.Google Scholar
Hale, J. (2001) A probabilistic early parser as a psycholinguistic model. In: North American Chapter of the Association for Computational Linguistics (NAACL), Vol. 2, pp. 1–8. Association for Computational Linguistics.Google Scholar
Jaeger, T. F. (2010) Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology 61:23–62.CrossRefGoogle ScholarPubMed
Jaeger, T. F. & Snider, N. (2013) Alignment as a consequence of expectation adaptation: Syntactic priming is affected by the prime's prediction error given both prior and recent experience. Cognition 127(1):57–83.CrossRefGoogle ScholarPubMed
Kleinschmidt, D., Fine, A. B. & Jaeger, T. F. (2012) A belief-updating model of adaptation and cue combination in syntactic comprehension. Proceedings of the 34rd Annual Meeting of the Cognitive Science Society (CogSci12), 605–10.Google Scholar
Kleinschmidt, D. & Jaeger, T. F. (2011) A Bayesian belief updating model of phonetic recalibration and selective adaptation. Proceedings of the Cognitive Modeling and Computational Linguistics Workshop at ACL, Portland, OR, June 23rd , 10–19.Google Scholar
Levy, R. (2008) Expectation-based syntactic comprehension. Cognition 106(3):1126–77.Google Scholar
Lindblom, B. (1990) Explaining phonetic variation: A sketch of the H&H theory. Speech production and speech modelling 55:40339.CrossRefGoogle Scholar
Perkell, J. S., Guenther, F. H., Lane, H., Matthies, M. L., Stockmann, E., Tiede, M. and Zandipour, M. (2004) The distinctness of speakers' productions of vowel contrasts is related to their discrimination of the contrasts. The Journal of the Acoustical Society of America 116:2338.Google Scholar
Ramscar, M., Yarlett, D., Dye, M., Denny, K. & Thorpe, K. (2010) The effects of feature-label-order and their implications for symbolic learning. Cognitive Science 34(6):909–57.Google Scholar
Roche, J., Dale, R., Kreuz, R. J. & Jaeger, T. F. (2013) Learning to avoid syntactic ambiguity. Ms., University of Rochester.Google Scholar
Sonderegger, M. & Yu, A. (2010) A rational account of perceptual compensation for coarticulation. Paper presented at the Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (CogSci10).Google Scholar
Warker, J. A. & Dell, G. S. (2006) Speech errors reflect newly learned phonotactic constraints. Journal of Experimental Psychology: Learning, Memory, and Cognition 32(2):387.Google Scholar
Wei, K. & Körding, K. (2009) Relevance of error: What drives motor adaptation? Journal of Neurophysiology 101(2):655–64.Google Scholar