Individual Differences in Cue Weighting in Sentence Comprehension: An Evaluation Using Approximate Bayesian Computation

Author:

Yadav Himanshu1ORCID,Paape Dario1ORCID,Smith Garrett1,Dillon Brian W.2,Vasishth Shravan1ORCID

Affiliation:

1. Department of Linguistics, University of Potsdam, Germany

2. Department of Linguistics, University of Massachusetts, USA

Abstract

Abstract Cue-based retrieval theories of sentence processing assume that syntactic dependencies are resolved through a content-addressable search process. An important recent claim is that in certain dependency types, the retrieval cues are weighted such that one cue dominates. This cue-weighting proposal aims to explain the observed average behavior, but here we show that there is systematic individual-level variation in cue weighting. Using the Lewis and Vasishth cue-based retrieval model, we estimated individual-level parameters for reading speed and cue weighting using 13 published datasets; hierarchical approximate Bayesian computation (ABC) was used to estimate the parameters. The modeling reveals a nuanced picture of cue weighting: we find support for the idea that some participants weight cues differentially, but not all participants do. Only fast readers tend to have the predicted higher weighting for structural cues, suggesting that reading proficiency (approximated here by reading speed) might be associated with cue weighting. A broader achievement of the work is to demonstrate how individual differences can be investigated in computational models of sentence processing without compromising the complexity of the model.

Funder

Deutscher Akademischer Austauschdienst

Deutsche Forschungsgemeinschaft

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Linguistics and Language,Developmental and Educational Psychology,Experimental and Cognitive Psychology

Reference72 articles.

1. An integrated theory of list memory;Anderson;Journal of Memory and Language,1998

2. The Atomic Components of Thought

3. Random effects structure for confirmatory hypothesis testing: Keep it maximal;Barr;Journal of Memory and Language,2013

4. lme4: Linear mixed-effects models using Eigen and S4 [Computer software manual];Bates,2014

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