TY - GEN
T1 - Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process
T2 - 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition, CogSci 2017
AU - Vasishth, Shravan
AU - Chopin, Nicolas
AU - Ryder, Robin
AU - Nicenboim, Bruno
N1 - Publisher Copyright:
© CogSci 2017.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the retrieval time (the distance-based account). An alternative theory, direct-access, assumes that retrieval times are a mixture of two distributions: one distribution represents successful retrievals (these are independent of dependency distance) and the other represents an initial failure to retrieve the correct dependent, followed by a reanalysis that leads to successful retrieval. We implement both models as Bayesian hierarchical models and show that the direct-access model explains Chinese relative clause reading time data better than the distance account.
AB - We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the retrieval time (the distance-based account). An alternative theory, direct-access, assumes that retrieval times are a mixture of two distributions: one distribution represents successful retrievals (these are independent of dependency distance) and the other represents an initial failure to retrieve the correct dependent, followed by a reanalysis that leads to successful retrieval. We implement both models as Bayesian hierarchical models and show that the direct-access model explains Chinese relative clause reading time data better than the distance account.
KW - Bayesian Hierarchical Finite Mixture Models
KW - Chinese Relative Clauses
KW - Direct-Access Model
KW - K-fold Cross-Validation
KW - Psycholinguistics
KW - Sentence Comprehension
M3 - Conference contribution
AN - SCOPUS:85139553537
T3 - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition
SP - 1278
EP - 1283
BT - CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society
PB - The Cognitive Science Society
Y2 - 26 July 2017 through 29 July 2017
ER -