Prediction and uncertainty in an artificial language

Tal Linzen, Noam Siegelman, Louisa Bogaerts

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Probabilistic prediction is a central process in language comprehension. Properties of probability distributions over predictions are often difficult to study in natural language. To obtain precise control over these distributions, we created artificial languages consisting of sequences of shapes. The languages were constructed to vary the uncertainty of the probability distribution over predictions as well as the probability of the predicted item. Participants were exposed to the languages in a self-paced presentation paradigm, which provides a measure of processing difficulty at each element of a sequence. There was a robust pattern of graded predictability: shapes were processed faster the more predictable they were, as in natural language. Processing times were also affected by the uncertainty (entropy) over predictions at the point at which those predictions were made; this effect was less consistent, however.

Original languageAmerican English
Title of host publicationCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
Subtitle of host publicationComputational Foundations of Cognition
PublisherThe Cognitive Science Society
Pages2592-2597
Number of pages6
ISBN (Electronic)9780991196760
StatePublished - 2017
Event39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017 - London, United Kingdom
Duration: 26 Jul 201729 Jul 2017

Publication series

NameCogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition

Conference

Conference39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
Country/TerritoryUnited Kingdom
CityLondon
Period26/07/1729/07/17

Bibliographical note

Funding Information:
Acknowledgments: We thank Lizz Karuza for sharing her materials. This research was supported by the European Research Council (grant ERC-2011-AdG 295810 BOOTPHON) and the Agence Nationale pour la Recherche (grants ANR-10-IDEX-0001-02 PSL and ANR-10-LABX-0087 IEC).

Publisher Copyright:
© CogSci 2017.

Keywords

  • Entropy
  • artificial language
  • prediction
  • psycholinguistics
  • statistical learning

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