The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension

Shohini Bhattasali, Jonathan Brennan, Wen-Ming Luh, Berta Franzluebbers, John Hale


Abstract
The Alice Datasets are a set of datasets based on magnetic resonance data and electrophysiological data, collected while participants heard a story in English. Along with the datasets and the text of the story, we provide a variety of different linguistic and computational measures ranging from prosodic predictors to predictors capturing hierarchical syntactic information. These ecologically valid datasets can be easily reused to replicate prior work and to test new hypotheses about natural language comprehension in the brain.
Anthology ID:
2020.lrec-1.15
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
120–125
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.15
DOI:
Bibkey:
Cite (ACL):
Shohini Bhattasali, Jonathan Brennan, Wen-Ming Luh, Berta Franzluebbers, and John Hale. 2020. The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 120–125, Marseille, France. European Language Resources Association.
Cite (Informal):
The Alice Datasets: fMRI & EEG Observations of Natural Language Comprehension (Bhattasali et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.15.pdf