Context: Difference between revisions
Computational Linguistics and Information Processing
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Bhattasali, S., & Resnik, P. (2021). Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 3786-3798). [https://aclanthology.org/2021.findings-acl.332.pdf Link] | Bhattasali, S., & Resnik, P. (2021). Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 3786-3798). [https://aclanthology.org/2021.findings-acl.332.pdf Link] | ||
Resources for computational cognitive neuroscience of language work can be found here: | |||
[[CogNeuro]] | |||
Latest revision as of 15:21, 25 August 2022
Investigating Context
Through fMRI and MEG studies, the goal of this project is to investigate the role of context during naturalistic language comprehension. Utilizing language models and topic models, we are examining how they can represent different sources of contextual knowledge and embody cognitive hypotheses, both during speech perception and during sentence processing.
Recent Papers
Brodbeck, C., Bhattasali, S., Heredia, A. A. C., Resnik, P., Simon, J. Z., & Lau, E. (2022). Parallel processing in speech perception with local and global representations of linguistic context. elife, 11, e72056. Link
Bhattasali, S., & Resnik, P. (2021). Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 3786-3798). Link
Resources for computational cognitive neuroscience of language work can be found here: CogNeuro