Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-26T20:17:32.437Z Has data issue: false hasContentIssue false

27 - Computational Psycholinguistics

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

Computational psycholinguistics seeks to develop computational theories and implemented models of the cognitive systems that map an unfolding linguistic signal onto a mental representation of its meaning. Focusing primarily on language comprehension, this chapter begins with early theories of sentence processing, before reviewing several prominent implemented computational models. These accounts are largely informed by reading-time studies that seek to establish the strategies and constraints that determine how people resolve ambiguity. This review concludes with a more in-depth discussion of rational probabilistic accounts, for which there has been considerable consensus in recent years, and surprisal theory, which formally links these models with measures of human comprehension effort, such as reading times and brain potentials. Finally, an implemented neurobehavioral model of language comprehension is presented in greater detail, illustrating the benefit of linking computational models with several behavioral and neurophysiological indices of human comprehension, as well as the importance of looking beyond syntactic processing alone to the modeling of semantic comprehension and the role of world knowledge.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alishahi, A. (2010). Computational Modeling of Human Language Acquisition. San Rafael, CA: Morgan & Claypool.Google Scholar
Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98 (3), 409429. https://doi.org/10.1037/0033-295X.98.3.409Google Scholar
Aurnhammer, C., & Frank, S. L. (2019). Comparing gated and simple recurrent neural network architectures as models of human sentence processing. In Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 112118). Austin, TX: Cognitive Science Society.Google Scholar
Baggio, G., & Hagoort, P. (2011). The balance between memory and unification in semantics: a dynamic account of the N400. Language and Cognitive Processes, 26, 13381367.CrossRefGoogle Scholar
Bever, T. G. (1970). The cognitive basis for linguistic structures. In Hayes, J. R. (Ed.), Cognition and the Development of Language (pp. 279352). New York, NY: Wiley.Google Scholar
Boston, M. F., Hale, J., 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, 112.Google Scholar
Bowman, S. R., Rastogi, A., Gupta, R., Manning, C. D., & Potts, C. (2016). A fast unified model for parsing and sentence understanding. In Proceedings of the Association for Computational Linguistics (pp. 1466–1477).Google Scholar
Brennan, J. R., & Hale, J. T. (2019). Hierarchical structure guides rapid linguistic predictions during naturalistic listening. PLoS One, 14 (1), e0207741. https://doi.org/10.1371/journal.pone.0207741Google Scholar
Brennan, J. R., Kuncoro, A., Dyer, C., & Hale, J. T. (2020). Localizing syntactic predictions using recurrent neural network grammars. Neuropsychologia 146, 10741079. https://doi.org/10.1016/j.neuropsychologia.2020.107479Google Scholar
Brouwer, H., Crocker, M. W., Venhuizen, N. J., & Hoeks, J. C. J. (2017). A neurocomputational model of the N400 and the P600 in language processing. Cognitive Science, 41 (S6), 13181352. https://doi.org/10.1111/cogs.12461CrossRefGoogle ScholarPubMed
Brouwer, H., Delogu, F., Venhuizen, N. J., & Crocker, M. W. (2021). Neurobehavioral correlates of surprisal in language comprehension: a neurocomputational model. Frontiers in Psychology, 12, 110. https://doi.org/10.3389/fpsyg.2021.615538Google Scholar
Chater, N., Crocker, M. W., & Pickering, M. J. (1998). The rational analysis of inquiry: the case for parsing. In Chater, N. & Oaksford, M. (Eds.), Rational Analysis of Cognition (pp. 441468). Oxford: Oxford University Press.Google Scholar
Crocker, M. W. (1996). Computational Psycholinguistics: An Interdisciplinary Approach to the Study of Language. Dordrecht: Kluwer.Google Scholar
Crocker, M. W. (1999). Mechanisms for sentence processing. In Garrod, S. & Pickering, M. J. (Eds.), Language Processing (pp. 191232). London: Psychology Press.Google Scholar
Crocker, M. W. (2005). Rational models of comprehension: addressing the performance paradox. In Cutler, A. (Ed.), Twenty-First Century Psycholinguistics: Four Cornerstones (pp. 363380). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Crocker, M. W., & Brants, T. (2000). Wide coverage probabilistic sentence processing. Journal of Psycholinguistic Research, 29 (6), 647669.CrossRefGoogle ScholarPubMed
Crocker, M. W., Knoeferle, P., & Mayberry, M. R. (2010). Situated sentence processing: the coordinated interplay account and a neurobehavioral model. Brain and Language, 112, 189201. https://doi.org/10.1016/j.bandl.2009.03.004Google Scholar
Dell, G. S., & Cholin, J. (2012). Language production: computational models. In Spivey, M. J., McRae, K., & Joanisse, M. F. (Eds.), The Cambridge Handbook of Psycholinguistics (pp. 426442). Cambridge: Cambridge University Press.Google Scholar
Delogu, F., Brouwer, H., & Crocker, M. W. (2019). Event-related potentials index lexical retrieval (N400) and integration (P600) during language comprehension. Brain and Cognition (online), 135. https://doi.org/10.1016/j.bandc.2019.05.007Google Scholar
Delogu, F., Brouwer, H., & Crocker, M. W. (2021). When components collide: spatiotemporal overlap of the N400 and P600 in language comprehension. Brain Research (online), 1766. https://doi.org/10.1016/j.brainres.2021.147514CrossRefGoogle ScholarPubMed
Delogu, F., Crocker, M. W., & Drenhaus, H. (2017). Teasing apart coercion and surprisal: evidence from ERPs and eye-movements. Cognition, 161, 4659.Google Scholar
Demberg, V., & Keller, F. (2008). Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 109 (2), 193210.Google Scholar
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14 (2), 179211. https://doi.org/10.1207/s15516709cog1402_1Google Scholar
Ferreira, F. (2003). The misinterpretation of noncanonical sentences. Cognitive Psychology, 47, 164203.Google Scholar
Ferreira, F., Ferraro, V., & Bailey, K. G. D. (2002). Good-enough representations in language comprehension. Current Directions in Psychological Science, 11, 1115.CrossRefGoogle Scholar
Ferreira, F., & Patson, N. (2007). The ‘good enough’ approach to language comprehension. Language and Linguistics Compass, 1 (1–2), 7183.CrossRefGoogle Scholar
Fitz, H., & Chang, F. (2019). Language ERPs reflect learning through prediction error propagation. Cognitive Psychology, 111, 1552. https://doi.org/10.1016/j.cogpsych.2019.03.002CrossRefGoogle ScholarPubMed
Fodor, J. A. (1983). The Modularity of Mind: An Essay on Faculty Psychology. Cambridge, MA: MIT Press.Google Scholar
Frank, S. L., Haselager, W. F., & van Rooij, I. (2009). Connectionist semantic systematicity. Cognition, 110 (3), 358379. https://doi.org/10.1016/j.cognition.2008.11.013Google Scholar
Frank, S. L., Koppen, M., Noordman, L. G., & Vonk, W. (2003). Modeling knowledge-based inferences in story comprehension. Cognitive Science, 27 (6), 875910. https://doi.org/10.1207/s15516709cog2706_3Google Scholar
Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G. (2015). The ERP response to the amount of information conveyed by words in sentences. Brain and Language, 140, 111.Google Scholar
Frazier, L. (1979). On comprehending sentences: syntactic parsing strategies. Ph.D. thesis, University of Connecticut, Connecticut.Google Scholar
Gibson, E. A. (1998). Linguistic complexity: locality of syntactic dependencies. Cognition, 68, 176.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), 80518056.CrossRefGoogle ScholarPubMed
Gibson, E., Tan, C., Futrell, R., et al. (2017). Don’t underestimate the benefits of being misunderstood. Psychological Science, 28 (6), 703712. https://doi.org/10.1177/0956797617690277Google Scholar
Gouvea, A. C., Phillips, C., Kazanina, N., & Poeppel, D. (2010). The linguistic processes underlying the P600. Language and Cognitive Processes, 25, 149188.Google Scholar
Hale, J. (2001). A probabilistic Earley parser as a psycholinguistic model. In Proceedings of North American Association for Computational Linguistics (Vol. 2, pp. 159–166).Google Scholar
Hoeks, J. C. J., Stowe, L. A., & Doedens, G. (2004). Seeing words in context: the interaction of lexical and sentence level information during reading. Cognitive Brain Research, 19 (1), 5973.CrossRefGoogle ScholarPubMed
Johnson-Laird, P. N. (1983). Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness. Cambridge, MA: Harvard University Press.Google Scholar
Jurafsky, D. (1996). A probabilistic model of lexical and syntactic access and disambiguation. Cognitive Science, 20, 137194.Google Scholar
Kim, A., & Osterhout, L. (2005). The independence of combinatory semantic processing: evidence from event-related potentials. Journal of Memory and Language, 52 (2), 205225.CrossRefGoogle Scholar
Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621647.Google Scholar
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: brain potentials reflect semantic incongruity. Science, 207 (4427), 203205.CrossRefGoogle ScholarPubMed
Laszlo, S., & Plaut, D. C. (2012). A neurally plausible Parallel Distributed Processing model of event-related potential word reading data. Brain and Language, 120, 271281. https://doi.org/10.1016/j.bandl.2011.09.001Google Scholar
Lenci, A. (2018). Distributional models of word meaning. Annual Review of Linguistics, 4 (1), 151171.Google Scholar
Levy, R. (2008). Expectation-based syntactic comprehension. Cognition, 106 (3), 11261177. https://doi.org/10.1016/j.cognition.2007.05.006Google Scholar
Lewis, R. L., & Vasishth, S. (2005). An activation‐based model of sentence processing as skilled memory retrieval. Cognitive Science, 29, 375419. https://doi.org/10.1207/s15516709cog0000_25CrossRefGoogle ScholarPubMed
Linzen, T., & Baroni, M. (2021). Syntactic structure from deep learning. Annual Reviews of Linguistics, 7, 195212.Google Scholar
Lopopolo, A., & Rabovsky, M. (2021). Predicting the N400 ERP component using the Sentence Gestalt model trained on a large scale corpus. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society.Google Scholar
MacDonald, M. C., Pearlmutter, N. J., & Seidenberg, M. S. (1994). The lexical nature of syntactic ambiguity resolution. Psychological Review, 101 (4), 676703. https://doi.org/10.1037/0033-295X.101.4.676Google Scholar
Magnuson, J. S., Mirman, D., & Harris, H. D. (2012). Computational models of spoken word recognition. In Spivey, M., McRae, K., & Joanisse, M. (Eds.), The Cambridge Handbook of Psycholinguistics (pp. 76103). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Marcus, M. P. (1980). A Theory of Syntactic Recognition for Natural Language. Cambridge, MA: MIT Press.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: W. H. Freeman.Google Scholar
Mayberry, M. R., Crocker, M. W., & Knoeferle, P. (2009). Learning to attend: a connectionist model of situated language comprehension. Cognitive Science, 33 (3), 449496.Google Scholar
McClelland, J. L., St. John, M. F., & Taraban, R. (1989). Sentence comprehension: a parallel distributed processing approach. Language and Cognitive Processes, 4, 287336.Google Scholar
McRae, K., Spivey-Knowlton, M. J., & Tanenhaus, M. K. (1998). Modeling the influence of thematic fit (and other constraints) in on-line sentence comprehension. Journal of Memory and Language, 38 (3), 283312.Google Scholar
Michaelov, J., & Bergen, B. (2020). How well does surprisal explain N400 amplitude under different experimental conditions? In Proceedings of the 24th Conference on Computational Natural Language Learning.CrossRefGoogle Scholar
Newell, A. (1973). You can’t play 20 questions with nature and win: projective comments on the papers of this symposium. In Chase, W. G. (Ed.), Visual Information Processing: Proceedings of the Eighth Annual Carnegie Symposium on Cognition. New York, NY: Academic Press.Google Scholar
Pado, U., Crocker, M. W., & Keller, F. (2009). A probabilistic model of semantic plausibility in sentence processing. Cognitive Science, 33, 794838.CrossRefGoogle ScholarPubMed
Pereira, F. C. N. (1985). A new characterization of attachment preferences. In Dowty, D., Karttunen, L., & Zwicky, A. (Eds.), Natural Language Parsing: Psychological, Computational, and Theoretical Perspectives. Cambridge: Cambridge University Press.Google Scholar
Pritchett, B. L. (1988). Garden path phenomena and the grammatical basis of language processing. Language, 64 , 539576.Google Scholar
Rabovsky, M., & McRae, K. (2014). Simulating the N400 ERP component as semantic network error: insights from a feature-based connectionist attractor model of word meaning. Cognition, 132, 6889. https://doi.org/10.1016/j.cognition.2014.03.010.Google Scholar
Rabovsky, M., Hansen, S. S., & McClelland, J. L. (2018). Modelling the N400 brain potential as change in a probabilistic representation of meaning. Nature Human Behavior, 2, 693705. https://doi.org/10.1038/s41562-018-0406-4Google Scholar
Rabovsky, M., & McClelland, J. L. (2019). Quasi-compositional mapping from form to meaning: a neural network-based approach to capturing neural responses during human language comprehension. Philosophical Transactions of the Royal Society B: Biological Sciences, 375 (1791). https://doi.org/10.1098/rstb.2019.0313Google ScholarPubMed
Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124 (3), 372422.Google Scholar
Rayner, K., Carlson, M., & Frazier, L. (1983). The interaction of syntax and semantics during sentence processing. Journal of Verbal Learning and Verbal Behavior, 22, 358374.Google Scholar
Rayner, K., & Well, A. D. (1996). Effects of contextual constraint on eye movements in reading: a further examination. Psychonomic Bulletin & Review, 3, 504509.Google Scholar
Roark, B., Bachrach, A., Cardenas, C., & Pallier, C. (2009). Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (pp. 324–333).Google Scholar
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323 (6088), 533536.Google Scholar
Sanford, A. J., Leuthold, H., Bohan, J., & Sanford, A. J. S. (2011). Anomalies at the borderline of awareness: an ERP study. Journal of Cognitive Neuroscience, 23 (3), 514523.Google Scholar
Sanford, A. J., & Sturt, P. (2002). Depth of processing in language comprehension: not noticing the evidence. Trends in Cognitive Sciences, 6 (9), 382386.CrossRefGoogle Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27 (3), 379423.Google Scholar
Smith, N. J., & Levy, R. (2013). The effect of word predictability on reading time is logarithmic. Cognition, 128 (3), 302319.Google Scholar
Spivey, M., McRae, K., & Joanisse, M. (Eds.). (2012). The Cambridge Handbook of Psycholinguistics. Cambridge: Cambridge University Press.Google Scholar
Staudte, M., Ankener, C., Drenhaus, H., & Crocker, M. W. (2021). Graded expectations in visually situated comprehension: costs and benefits as indexed by the N400. Psychonomic Bulletin & Review, 28, 624631.Google Scholar
Stevenson, S. (1994). Competition and recency in a hybrid network model of syntactic disambiguation. Journal of Psycholinguistic Research, 23 (4), 295322.CrossRefGoogle Scholar
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 (5217), 16321634. https://doi.org/10.1126/science.7777863Google Scholar
Tanenhaus, M. K., Trueswell, J. C., & Hanna, J. E. (2000). Modeling thematic and discourse context effects with a multiple constraints approach: implications for the architecture of the language comprehension system. In Crocker, M. W., Pickering, M. J., & Clifton, C. (Eds.), Architectures and Mechanism for Language Processing (pp. 90118). Cambridge: Cambridge University Press.Google Scholar
Taylor, W. L. (1953). “Cloze procedure”: a new tool for measuring readability. Journalism Quarterly, 30, 415433.Google Scholar
Townsend, D., & Bever, T. G. (2001). Sentence Comprehension: The Integration of Habits and Rules. Cambridge, MA: MIT Press.Google Scholar
Trueswell, J. C., Tanenhaus, M. K., & Garnsey, S. M. (1994). Semantic influences on parsing: use of thematic role information in syntactic ambiguity resolution. Journal of Memory and Language, 33, 285318.Google Scholar
van Dijk, T. A., & Kintsch, W. (1983). Strategies of Discourse Comprehension. New York, NY: Academic Press.Google Scholar
van Herten, M., Kolk, H. H. J., & Chwilla, D. J. (2005). An ERP study of P600 effects elicited by semantic anomalies. Cognitive Brain Research, 22 (2), 241255.Google Scholar
Venhuizen, N. J., Crocker, M. W., & Brouwer, H. (2019). Expectation-based comprehension: modeling the interaction of world knowledge and linguistic experience. Discourse Processes, 56 (3), 229255. https://doi.org/10.1080/0163853X.2018.1448677CrossRefGoogle Scholar
Venhuizen, N. J., Hendriks, P., Crocker, M. W., & Brouwer, H. (2022). Distributional formal semantics. Information and Computation, 287, 104763. https://doi.org/10.1016/j.ic.2021.104763Google Scholar
Warren, T., & Dickey, M. W. (2021). The use of linguistic and world knowledge in language processing. Language and Linguistics Compass, 15, e12411. https://doi.org/10.1111/lnc3.12411Google Scholar
Wehbe, L., Murphy, B., Talukdar, P., Fyshe, A., Ramdas, A., & Mitchell, T. (2014). Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses. PLoS One, 9 (11), e112575.CrossRefGoogle ScholarPubMed
Zwaan, R. A., & Radvansky, G. A. (1998). Situation models in language comprehension and memory. Psychological Bulletin, 123 (2), 162185. https://doi.org/10.1037/0033-2909.123.2.162CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×