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Decoding Visual Percepts Induced by Word Reading with fMRI

Abstract : Word reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs.
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Contributor : Alexandre Gramfort Connect in order to contact the contributor
Submitted on : Tuesday, September 11, 2012 - 9:54:28 AM
Last modification on : Friday, November 18, 2022 - 9:23:33 AM
Long-term archiving on: : Friday, December 16, 2016 - 12:10:50 PM


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Alexandre Gramfort, Christophe Pallier, Gaël Varoquaux, Bertrand Thirion. Decoding Visual Percepts Induced by Word Reading with fMRI. Pattern Recognition in NeuroImaging (PRNI), 2012 International Workshop on, Jul 2012, Londres, United Kingdom. pp.13-16, ⟨10.1109/PRNI.2012.20⟩. ⟨hal-00730768⟩



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