Papers by Shohini Bhattasali
Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension (2021.findings-acl)
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| Challenge: | a prior work using surprisal only considered within-sentence context, using n-grams, neural language models, or syntactic structure as conditioning context. |
| Approach: | They extend the surprisal approach to use broader topical context . they identify distinct patterns of neural activation for lexical surprised and topical surpresed . |
| Outcome: | The proposed method captures effects of local and topical contexts on processing . it shows that local and broad contextual cues recruit different brain regions . |
The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension (2020.lrec-1)
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| Challenge: | "naturalistic" stimuli are now offering a new way to study language comprehension in the brain, in synergy with natural language processing tools. |
| Approach: | They propose to use a set of datasets from a story in English to test new linguistic and computational hypotheses about natural language comprehension in the brain. |
| Outcome: | The Alice Datasets are a set of datasets based on magnetic resonance and electrophysiological data, collected while participants heard a story in English. |