IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0077661.html
   My bibliography  Save this article

Rapid Expectation Adaptation during Syntactic Comprehension

Author

Listed:
  • Alex B Fine
  • T Florian Jaeger
  • Thomas A Farmer
  • Ting Qian

Abstract

When we read or listen to language, we are faced with the challenge of inferring intended messages from noisy input. This challenge is exacerbated by considerable variability between and within speakers. Focusing on syntactic processing (parsing), we test the hypothesis that language comprehenders rapidly adapt to the syntactic statistics of novel linguistic environments (e.g., speakers or genres). Two self-paced reading experiments investigate changes in readers’ syntactic expectations based on repeated exposure to sentences with temporary syntactic ambiguities (so-called “garden path sentences”). These sentences typically lead to a clear expectation violation signature when the temporary ambiguity is resolved to an a priori less expected structure (e.g., based on the statistics of the lexical context). We find that comprehenders rapidly adapt their syntactic expectations to converge towards the local statistics of novel environments. Specifically, repeated exposure to a priori unexpected structures can reduce, and even completely undo, their processing disadvantage (Experiment 1). The opposite is also observed: a priori expected structures become less expected (even eliciting garden paths) in environments where they are hardly ever observed (Experiment 2). Our findings suggest that, when changes in syntactic statistics are to be expected (e.g., when entering a novel environment), comprehenders can rapidly adapt their expectations, thereby overcoming the processing disadvantage that mistaken expectations would otherwise cause. Our findings take a step towards unifying insights from research in expectation-based models of language processing, syntactic priming, and statistical learning.

Suggested Citation

  • Alex B Fine & T Florian Jaeger & Thomas A Farmer & Ting Qian, 2013. "Rapid Expectation Adaptation during Syntactic Comprehension," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-18, October.
  • Handle: RePEc:plo:pone00:0077661
    DOI: 10.1371/journal.pone.0077661
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0077661
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0077661&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0077661?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
    2. Yoshiyuki Sato & Kazuyuki Aihara, 2011. "A Bayesian Model of Sensory Adaptation," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-7, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kirsten Weber & Ellen F Lau & Benjamin Stillerman & Gina R Kuperberg, 2016. "The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-25, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    2. Loreen Hertäg & Katharina A. Wilmes & Claudia Clopath, 2025. "Uncertainty estimation with prediction-error circuits," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    3. Wen-Hao Zhang & Si Wu & Krešimir Josić & Brent Doiron, 2023. "Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    4. Adam N Sanborn & Ulrik R Beierholm, 2016. "Fast and Accurate Learning When Making Discrete Numerical Estimates," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-28, April.
    5. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Brocas, Isabelle & Carrillo, Juan D., 2012. "From perception to action: An economic model of brain processes," Games and Economic Behavior, Elsevier, vol. 75(1), pages 81-103.
    7. Jennifer Laura Lee & Wei Ji Ma, 2021. "Point-estimating observer models for latent cause detection," PLOS Computational Biology, Public Library of Science, vol. 17(10), pages 1-29, October.
    8. Alkis M Hadjiosif & J Ryan Morehead & Maurice A Smith, 2023. "A double dissociation between savings and long-term memory in motor learning," PLOS Biology, Public Library of Science, vol. 21(4), pages 1-32, April.
    9. Vassilios N Christopoulos & Paul R Schrater, 2009. "Grasping Objects with Environmentally Induced Position Uncertainty," PLOS Computational Biology, Public Library of Science, vol. 5(10), pages 1-11, October.
    10. Christopher L Hewitson & David M Kaplan & Matthew J Crossley, 2023. "Error-independent effect of sensory uncertainty on motor learning when both feedforward and feedback control processes are engaged," PLOS Computational Biology, Public Library of Science, vol. 19(9), pages 1-45, September.
    11. Guido Marco Cicchini & Giovanni D’Errico & David Charles Burr, 2022. "Crowding results from optimal integration of visual targets with contextual information," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    12. Alice Soldà & Changxia Ke & Lionel Page & William von Hippel, 2020. "Strategically delusional," Experimental Economics, Springer;Economic Science Association, vol. 23(3), pages 604-631, September.
    13. Daniel Bjasch & Christopher J Bockisch & Dominik Straumann & Alexander A Tarnutzer, 2012. "Differential Effects of Visual Feedback on Subjective Visual Vertical Accuracy and Precision," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-11, November.
    14. Philipp Schustek & Rubén Moreno-Bote, 2018. "Instance-based generalization for human judgments about uncertainty," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-27, June.
    15. Lukas K. Amann & Virginia Casasnovas & Alexander Gail, 2025. "Visual target and task-critical feedback uncertainty impair different stages of reach planning in motor cortex," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    16. Daniel Durstewitz, 2017. "A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-33, June.
    17. Joshua G A Cashaback & Christopher K Lao & Dimitrios J Palidis & Susan K Coltman & Heather R McGregor & Paul L Gribble, 2019. "The gradient of the reinforcement landscape influences sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-27, March.
    18. Michael Bergin & Kylie Tucker & Bill Vicenzino & Paul W Hodges, 2021. "“Taking action” to reduce pain—Has interpretation of the motor adaptation to pain been too simplistic?," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-19, December.
    19. Nils Neupärtl & Fabian Tatai & Constantin A Rothkopf, 2020. "Intuitive physical reasoning about objects’ masses transfers to a visuomotor decision task consistent with Newtonian physics," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-26, October.
    20. Tianhe Wang & Yingrui Luo & Richard B Ivry & Jonathan S Tsay & Ernst Pöppel & Yan Bao, 2023. "A unitary mechanism underlies adaptation to both local and global environmental statistics in time perception," PLOS Computational Biology, Public Library of Science, vol. 19(5), pages 1-23, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0077661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.