Encoding and decoding models for cognitive imaging

A new approach for brain decoding, called inverse inference (or brain-reading), introduced initially in [Dehaene 98, Cox 03], has become recently popular. This method relies on statistical learning tools, and more precisely on pattern recognition approaches. The main idea is to consider the fMRI analysis as a pattern recognition problem, i.e. using a pattern of voxels to predict a behavioral, perceptual or cognitive variable. In this approach, the accuracy of the prediction can be used to validate (or invalidate) that the pattern of voxels used in the predictive model is implied in the neural coding. In short, reverse inference is an approach for decoding neural activity.

Large-scale decoding for a reverse inference and knowledge accumulation

Developing better decoders

Encoding models

Group representations and functional alignment

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