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Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation
Author(s) -
Paape Dario,
Avetisyan Serine,
Lago Sol,
Vasishth Shravan
Publication year - 2021
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.13019
Subject(s) - attraction , phrase , computer science , agreement , feature (linguistics) , substitution (logic) , artificial intelligence , bayesian probability , subject (documents) , natural language processing , linguistics , philosophy , programming language , library science
We present computational modeling results based on a self‐paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k‐fold cross‐validation. We find that our data are better accounted for by an encoding‐based model of agreement attraction, compared to a retrieval‐based model. A novel methodological contribution of our study is the use of comprehension questions with open‐ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.