NIPS 2006
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New directions on decoding mental states from fMRI data

John-Dylan Haynes · Tom Mitchell · Francisco Pereira

Alpine C-E

In the past five years machine learning classifiers have met great interest in the field of cognitive neuroscience. They have been used to make predictions about the mental state of subjects directly from fMRI data, as well as study the neural encoding of specific mental contents in the human brain, in ways that transcend the limitations of conventional methods. The goals of this workshop are to present the current challenges in the field from the points of view of cognitive neuroscience and machine learning practitioners, as well as bring to the fore new work that goes beyond mere decoding to the study of the structure present in fMRI activity. More broadly, we will be concerned with the implications of multivariate decoding results for theories of cognitive neuroscience, as well as the manner in which those theories may influence the practice of decoding.

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