New Directions on
Decoding Mental States from fMRI Data

NIPS 06 workshops, December 8, Whistler, Canada


In the past five years machine learning classifiers have met great interest in the field of cognitive neuroscience. The use of classifiers for decoding has emerged as a powerful technique that enables researchers to make predictions about the mental state of a subject directly from fMRI data. This work has received considerable attention because it is seen as a way to overcome limitations of more conventional fMRI analysis methods. Whereas conventional fMRI research is focused on spatially localising cognitive modules, decoding-based research allows for the first time the study of the neural encoding of specific mental contents in the human brain.

More than hot air, hopefully.

The recent progress has also raised a number of fundamental questions, about the practice of decoding, the interpretation of results and their implications for theories of cognitive neuroscience. At a high level, one would like to know how decoding can help model-building in cognitive neuroscience and, ultimately, help develop theories of neural representation that explain the decoding-identified structure in the fMRI data. Conversely, we have the question of whether classifiers can be used as a confirmatory scientific tool for existing theories or hypotheses.

We think that, given the number and type of specific open questions, this is more than just another application domain and thus there is space for machine learning researchers to come up with new methods or creative application and combination of existing ones. This workshop is designed to facilitate their entry into this field and put them in contact with cognitive neuroscientists receptive to their methods, as well as provide a venue for discussion of those questions as a possible agenda for the field.


Invited Speakers

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