From Brain Space to Distributional Space: The Perilous Journeys of fMRI Decoding
| Autor: | Gosse Minnema, Aurélie Herbelot |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Point (typography)
business.industry Computer science media_common.quotation_subject 02 engineering and technology Space (commercial competition) Cognitive neuroscience Machine learning computer.software_genre 03 medical and health sciences 0302 clinical medicine Neuroimaging 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Quality (business) Artificial intelligence Computational linguistics business computer 030217 neurology & neurosurgery Decoding methods media_common |
| Zdroj: | ACL (2) Università degli di Trento-IRIS |
| DOI: | 10.18653/v1/p19-2021 |
| Popis: | Recent work in cognitive neuroscience has introduced models for predicting distributional word meaning representations from brain imaging data. Such models have great potential, but the quality of their predictions has not yet been thoroughly evaluated from a computational linguistics point of view. Due to the limited size of available brain imaging datasets, standard quality metrics (e.g. similarity judgments and analogies) cannot be used. Instead, we investigate the use of several alternative measures for evaluating the predicted distributional space against a corpus-derived distributional space. We show that a state-of-the-art decoder, while performing impressively on metrics that are commonly used in cognitive neuroscience, performs unexpectedly poorly on our metrics. To address this, we propose strategies for improving the model’s performance. Despite returning promising results, our experiments also demonstrate that much work remains to be done before distributional representations can reliably be predicted from brain data. |
| Databáze: | OpenAIRE |
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