Pattern-information analysis: from stimulus decoding to computational-model testing.

Pattern-information analysis has become an important new paradigm in functional imaging. Here I review and compare existing approaches with a focus on the question of what we can learn from them in terms of brain theory. The most popular and widespread method is stimulus decoding by response-pattern classification. This approach addresses the question whether activity patterns in a given region carry information about the stimulus category. Pattern classification uses generic models of the stimulus-response relationship that do not mimic brain information processing and treats the stimulus space as categorical-a simplification that is often helpful, but also limiting in terms of the questions that can be addressed. We can address the question whether representations are consistent across different stimulus sets or tasks by cross-decoding, where the classifier is trained with one set of stimuli (or task) and tested with another. Beyond pattern classification, a major new direction is the integration of computational models of brain information processing into pattern-information analysis. This approach enables us to address the question to what extent competing computational models are consistent with the stimulus representations in a brain region. Two methods that test computational models are voxel receptive-field modeling and representational similarity analysis. These methods sample the stimulus (or mental-state) space more richly, estimate a separate response pattern for each stimulus, and can generalize from the stimulus sample to a stimulus population. Computational models that mimic brain information processing predict responses from stimuli. The reverse transform can be modeled to reconstruct stimuli from responses. Stimulus reconstruction is a challenging feat of engineering, but the implications of the results for brain theory are not always clear. Exploratory pattern analyses complement the confirmatory approaches mentioned so far and can reveal strong, unexpected effects that might be missed when testing only a restricted set of predefined hypotheses.