Information mapping with pattern classifiers: a comparative study

Francisco Pereira, Matthew Botvinick


Information mapping using pattern classifiers has become increasingly popular in recent years, although without a clear consensus on which classifier(s) ought to be used or how results should be tested. This paper addresses each of these questions, both analytically and through comparative analyses on five empirical datasets. We also describe how information maps in multiple class situations can provide information concerning the content of neural representations. Finally, we introduce a publically available software toolbox designed specifically for information mapping.


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