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L2 processing as noisy channel language comprehension

Published online by Cambridge University Press:  22 September 2016

RICHARD FUTRELL  and
EDWARD GIBSON
RICHARD FUTRELL*
Affiliation:
Department of Brain & Cognitive Sciences, MIT, Cambridge
EDWARD GIBSON
Affiliation:
Department of Brain & Cognitive Sciences, MIT, Cambridge
*
Address for correspondence: Richard Futrell, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 43 Vassar Street, Room 46-3037, Cambridge Massachusetts, United States02139[email protected]
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Extract

The thesis in this paper is that L2 speakers differ from L1 speakers in their ability to do memory storage and retrieval about linguistic structure. We would like to suggest it is possible to go farther than this thesis and develop a computational-level theory which explains why this mechanistic difference between L2 and L1 speakers exists. For this purpose, we believe a noisy channel model (Shannon, 1948; Levy, 2008; Levy, Bicknell, Slattery & Rayner, 2009; Gibson, Bergen & Piantadosi, 2013) could be a good start. Under the reasonable assumption that L2 speakers have a less precise probabilistic representation of the syntax of their L2 language than L1 speakers do, the noisy channel model straightforwardly predicts that L2 comprehenders will depend more on world knowledge and discourse factors when interpreting and recalling utterances (cf. Gibson, Sandberg, Fedorenko, Bergen & Kiran, 2015, for this assumption applied to language processing for persons with aphasia). Also, under the assumption that L2 speakers assume a higher error rate than L1 speakers do, the noisy channel model predicts that they will be more affected by alternative parses which are not directly compatible with the form of an utterance.

Type
Peer Commentaries
Information
Bilingualism: Language and Cognition , Volume 20 , Issue 4 , August 2017 , pp. 683 - 684
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Copyright
Copyright © Cambridge University Press 2016 

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References

Christianson, K., Hollingworth, A., Halliwell, J. & Ferreira, F. (2001). Thematic roles assigned along the garden path linger. Cognitive Psychology, 42, 368–407.Google Scholar
Gibson, E., Bergen, L., & Piantadosi, S. T. (2013). Rational integration of noisy evidence and prior semantic expectations in sentence interpretation. Proceedings of the National Academy of Sciences, 110 (20), 8051–8056.Google Scholar
Gibson, E., Sandberg, C., Fedorenko, E., Bergen, L., & Kiran, S. (2015). A rational inference approach to aphasic language comprehension. Aphasiology, DOI: 10.1080/02687038.2015.1111994 Google Scholar
Jacob, G. & Felser, C. (2016). Reanalysis and semantic persistence in native and nonnative garden-path recovery. Quarterly Journal of Experimental Psychology 69 (5), 907–925.Google Scholar
Levy, R. (2008). A noisy-channel model of rational human sentence comprehension under uncertain input. Proceedings of the 13th Conference on Empirical Methods in Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), pp. 234–243.Google Scholar
Levy, R., Bicknell, K., Slattery, T., & Rayner, K. (2009). Eye movement evidence that readers maintain and act on uncertainty about past linguistic input. Proceedings of the National Academy of Sciences, 106 (50), 21086–21090.Google Scholar
Levy, R. (2011). Integrating surprisal and uncertain-input models in online sentence comprehension: formal techniques and empirical results. In Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 1055–1065).Google Scholar
Shannon, C. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27, 623–656.Google Scholar