Alona Fyshe
Appointment
- Fellow
- Canada CIFAR AI Chair
- CIFAR Azrieli Global Scholar 2016-2018
- Learning in Machines & Brains
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About
Alona Fyshe is a computer scientist by training, but her research straddles multiple areas including neuroscience, machine learning, and computational linguistics.
Her lab explores how the human brain processes and represents language meaning (semantics) and how it combines words to make complex meaning (semantic composition). Traditionally, models of semantics have been based on large text datasets. These are useful to a point, but to build complete semantic models we need to incorporate experiential knowledge. One way to do this is to study semantics ‘in vivo,’ using brain imaging technology such as fMRI and EEG.
Currently, brain image datasets are very small, representing about an hour of acquisition time per participant. Fyshe proposes moving brain imaging to ‘the field’ – having people record their own brain images, using consumer-grade EEG systems deployed in the home. This will produce much larger datasets, allowing her to more fully explore the complexities of language. Furthermore, developing techniques to collect quality EEG data in the home could change how we detect and diagnose neurological disorders, and how we monitor therapeutic interventions for mental illness.
Relevant Publications
- Haoyan, X., Murphy, B., & Fyshe, A. (2016). BrainBench: A Brain-Image Test Suite for Distributional Semantic Models. SIGDAT Conference on Empirical Methods for Natural Language Processing, Austin, Texas.
- Fyshe, A., Talukdar, P.P, Murphy, B., & Mitchell, T.M. (2014). Interpretable Semantic Vectors from a Joint Model of Brain- and Text-Based Meaning. Annual meeting of the Association for Computational Linguistics, Baltimore, Maryland.
- Wehbe, L., Murphy, B., Talukdar, P., Fyshe, A., et al. (2014). Simultaneously Uncovering the patterns of brain regions involved in different story reading subprocesses. Plos One. DOI: https://doi.org/10.1371/journal.pone.0112575
- Fyshe, A., Murphy, B., Talukdar, P., & Mitchell, T. (2013, August). Documents and dependencies: an exploration of vector space models for semantic composition. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning (pp. 84-93).
- Sudre, G. Pomerleau, D., Palatucci, M., Wehbe, L., Fyshe A., et al. (2012). Tracking neural coding of perceptual and semantic features of concrete nouns. Neuroimage, 62(1), 451–63. DOI: 10.1016/j.neuroimage.2012.04.048
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