Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Direct-access Model of Retrieval

Paula Lissón, Dorothea Pregla, Dario Paape, Frank Burchert, Nicole Stadie, Shravan Vasishth


Abstract
Several researchers have argued that sentence comprehension is mediated via a content-addressable retrieval mechanism that allows fast and direct access to memory items. Initially failed retrievals can result in backtracking, which leads to correct retrieval. We present an augmented version of the direct-access model that allows backtracking to fail. Based on self-paced listening data from individuals with aphasia, we compare the augmented model to the base model without backtracking failures. The augmented model shows quantitatively similar performance to the base model, but only the augmented model can account for slow incorrect responses. We argue that the modified direct-access model is theoretically better suited to fit data from impaired populations.
Anthology ID:
2021.cmcl-1.22
Volume:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
Month:
June
Year:
2021
Address:
Online
Editors:
Emmanuele Chersoni, Nora Hollenstein, Cassandra Jacobs, Yohei Oseki, Laurent Prévot, Enrico Santus
Venue:
CMCL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
177–185
Language:
URL:
https://aclanthology.org/2021.cmcl-1.22
DOI:
10.18653/v1/2021.cmcl-1.22
Bibkey:
Cite (ACL):
Paula Lissón, Dorothea Pregla, Dario Paape, Frank Burchert, Nicole Stadie, and Shravan Vasishth. 2021. Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Direct-access Model of Retrieval. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 177–185, Online. Association for Computational Linguistics.
Cite (Informal):
Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Direct-access Model of Retrieval (Lissón et al., CMCL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.cmcl-1.22.pdf
Optional supplementary data:
 2021.cmcl-1.22.OptionalSupplementaryData.zip