ENS, amphitheater Jaures, 29 rue d'Ulm, 75005 Paris
Abstract: The meaning, or semantic content, of natural speech is represented in highly specific patterns of brain activity across a large portion of the human cortex. Using recently developed machine learning methods and very large fMRI datasets collected from single subjects, we can construct models that predict brain responses with high accuracy. Interrogating these models enables us to map language selectivity with unprecedented precision, and potentially uncover organizing principles. The same techniques also enable us to construct surprisingly effective decoding models, which predict language stimuli from brain activations recorded using fMRI. Using these models we are able to decode language while subjects imagine telling a story, and while subjects watch silent films with no explicit language content.
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