Learning reference biases from language input: A cognitive modelling approach

Abigail Grace Toth*, Niels Taatgen, Petra Hendriks, Jacolien van Rij

*Corresponding author for this work

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Abstract

In order to gain insight into how people acquire certain reference biases in language and how those biases influence online language processing, we constructed a cognitive model and presented it with a dataset containing reference asymmetries. Via prediction and reinforcement learning, the model was able to pick up on the asymmetries in the input. The model predictions have implications for various accounts of reference processing and demonstrate that seemingly complex behavior can be explained by simple learning mechanisms
Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Cognitive Modelling (ICCM 2021)
EditorsTerrence C. Stewart
Place of PublicationUniversity Park, PA
PublisherApplied Cognitive Science Lab, Penn State
Pages282-288
Number of pages7
ISBN (Electronic)978-0-9985082-5-2
Publication statusPublished - 2021
EventInternational Conference on Cognitive Modeling 2021 - Online
Duration: 29-Jun-20219-Jul-2021
https://mathpsych.org/conference/7/schedule

Conference

ConferenceInternational Conference on Cognitive Modeling 2021
Abbreviated titleICCM 2021
Period29/06/202109/07/2021
Internet address

Keywords

  • implicit causality; reference; cognitive modellin

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