Concept classification with Bayesian multi-task learning

Authors:
Marcel van Gerven;Irina Simanova
Affiliations:
Radboud University Nijmegen, Nijmegen, The Netherlands;Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
Venue:
CN '10 Proceedings of the NAACL HLT 2010 First Workshop on Computational Neurolinguistics
Year:
2010

Citing 5
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Abstract

Multivariate analysis allows decoding of single trial data in individual subjects. Since different models are obtained for each subject it becomes hard to perform an analysis on the group level. We introduce a new algorithm for Bayesian multi-task learning which imposes a coupling between single-subject models. Using the CMU fMRI dataset it is shown that the algorithm can be used for concept classification based on the average activation of regions in the AAL atlas. Concepts which were most easily classified correspond to the categories shelter, manipulation and eating, which is in accordance with the literature. The multi-task learning algorithm is shown to find regions of interest that are common to all subjects which therefore facilitates interpretation of the obtained models.