acl acl2010 acl2010-13 acl2010-13-reference knowledge-graph by maker-knowledge-mining
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Author: Klinton Bicknell ; Roger Levy
Abstract: A number of results in the study of realtime sentence comprehension have been explained by computational models as resulting from the rational use of probabilistic linguistic information. Many times, these hypotheses have been tested in reading by linking predictions about relative word difficulty to word-aggregated eye tracking measures such as go-past time. In this paper, we extend these results by asking to what extent reading is well-modeled as rational behavior at a finer level of analysis, predicting not aggregate measures, but the duration and location of each fixation. We present a new rational model of eye movement control in reading, the central assumption of which is that eye move- ment decisions are made to obtain noisy visual information as the reader performs Bayesian inference on the identities of the words in the sentence. As a case study, we present two simulations demonstrating that the model gives a rational explanation for between-word regressions.
Allauzen, C., Riley, M., Schalkwyk, J., Skut, W., & Mohri, M. (2007). OpenFst: A general and efficient weighted finite-state transducer library. In Proceedings of the Ninth International Conference on Implementation and Application of Automata, (CIAA 2007) (Vol. 4783, p. 11-23). Springer. Bicknell, K., & Levy, R. (2010). Rational eye movements in reading combining uncertainty about previous words with contextual probability. In Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society. Boston, M. F., Hale, J. T., Kliegl, R., Patil, U., & Vasishth, S. (2008). Parsing costs as predictors of reading difficulty: An evaluation using the potsdam sentence corpus. Journal of Eye Movement Research, 2(1), 1–12. Connine, C. M., Blasko, D. G., & Hall, M. (1991). Effects of subsequent sentence context in audi- tory word recognition: Temporal and linguistic constraints. Journal of Memory and Language, 30, 234–250. Demberg, V., & Keller, F. (2008). Data from eyetracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109, 193–210. Ehrlich, S. F., & Rayner, K. (1981). Contextual effects on word perception and eye movements during reading. Journal of Verbal Learning and Verbal Behavior, 20, 641–655. Engbert, R., & Krügel, A. (2010). Readers use Bayesian estimation for eye movement control. Psychological Science, 21, 366–371 . Engbert, R., Longtin, A., & Kliegl, R. (2002). A dynamical model of saccade generation in reading based on spatially distributed lexical processing. Vision Research, 42, 621–636. Engbert, R., Nuthmann, A., Richter, E. M., & Kliegl, R. (2005). SWIFT: A dynamical model of saccade generation during reading. Psychological Review, 112, 777–813. Engel, G. R., Dougherty, W. G., & Jones, B. G. (1973). Correlation and letter recognition. Canadian Journal of Psychology, 27, 3 17–326. Genzel, D., & Charniak, E. (2002, July). Entropy rate constancy in text. In Proceedings ofthe 40th annual meeting of the Association for Computational Linguistics (pp. 199–206). Philadelphia: Association for Computational Linguistics. Genzel, D., & Charniak, E. (2003). Variation of entropy and parse trees of sentences as a function of the sentence number. In M. Collins & M. Steedman (Eds.), Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (pp. 65–72). Sapporo, Japan: Association for Computational Linguistics. Geyer, L. H. (1977). Recognition and confusion of the lowercase alphabet. Perception & Psychophysics, 22, 487–490. Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of the Second Meeting of the North American Chapter oftheAssociationfor Computational Linguistics (Vol. 2, pp. 159–166). New Brunswick, NJ: As- sociation for Computational Linguistics. Jaeger, T. F. (2010). Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology. doi: 10. 1016/j.cogpsych.2010.02.002. Jurafsky, D. (1996). A probabilistic model of lexical and syntactic access and disambiguation. Cognitive Science, 20, 137–194. Keller, F. (2004). The entropy rate principle as a predictor of processing effort: An evaluation against eye-tracking data. In D. Lin & D. Wu (Eds.), Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (pp. 3 17–324). Barcelona, Spain: Association for Computational Linguistics. Legge, G. E., Hooven, T. A., Klitz, T. S., Mansfield, J. S., & Tjan, B. S. (2002). Mr. Chips 2002: new insights from an ideal-observer model of reading. Vision Research, 42, 2219– 2234. Legge, G. E., Klitz, T. S., & Tjan, B. S. (1997). Mr. Chips: an Ideal-Observer model of reading. Psychological Review, 104, 524–553. Levy, R. (2008). A noisy-channel model of rational human sentence comprehension under uncertain input. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 234–243). Honolulu, Hawaii: Association for Computational Linguistics. 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, 21086–21090. 1177 Levy, R., & Jaeger, T. F. (2007). Speakers optimize information density through syntactic reduction. In B. Schölkopf, J. Platt, & T. Hoffman (Eds.), Advances in Neural Information Processing Systems 19 (pp. 849–856). Cambridge, MA: MIT Press. Levy, R., Reali, F., & Griffiths, T. L. (2009). Modeling the effects of memory on human online sentence processing with particle filters. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in Neural Information Processing Systems 21 (pp. 937–944). Mohri, M. (1997). Finite-state transducers in language and speech processing. Computational Linguistics, 23, 269–3 11. Narayanan, S., & Jurafsky, D. (2001). A Bayesian model predicts human parse preference and reading time in sentence processing. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 59–65). Cambridge, MA: MIT Press. Ng, A. Y., & Jordan, M. (2000). PEGASUS: A policy search method for large MDPs and POMDPs. In Uncertainty in Artificial Intelligence, Proceedings of the Sixteenth Conference (pp. 406–415). Norris, D. (2006). The Bayesian reader: Explaining word recognition as an optimal Bayesian decision process. Psychological Review, 113, 327– 357. Norris, D. (2009). Putting it all together: A unified account of word recognition and reaction-time distributions. Psychological Review, 116, 207– 219. Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124, 372–422. Reichle, E. D., & Laurent, P. A. (2006). Using reinforcement learning to understand the emergence of “intelligent” eye-movement behavior during reading. Psychological Review, 113, 390–408. Reichle, E. D., Pollatsek, A., Fisher, D. L., & Rayner, K. (1998). Toward a model of eye movement control in reading. Psychological Review, 105, 125–157. Reichle, E. D., Pollatsek, A., & Rayner, K. (2006). E-Z Reader: A cognitive-control, serialattention model of eye-movement behavior during reading. Cognitive Systems Research, 7, 4– 22. Reichle, E. D., Warren, T., & McConnell, K. (2009). Using E-Z Reader to model the effects of higher level language processing on eye movements during reading. Psychonomic Bulletin & Review, 16, 1–21 . Schilling, H. E. H., Rayner, K., & Chumbley, J. I. (1998). Comparing naming, lexical decision, and eye fixation times: Word frequency effects and individual differences. Memory & Cognition, 26, 1270–1281. Smith, N. J., & Levy, R. (2008). Optimal processing times in reading: a formal model and empirical investigation. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 595–600). Austin, TX: Cognitive Science Society. Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M., & Sedivy, J. C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science, 268, 1632– 1634. 1178