In this article, the authors address two topics relevant to the study of the brain basis of associative learning. In Part 1, they compare and contrast the patterns and time course of dynamic learning-related neural activity that have been reported in the medial temporal lobe, premotor cortex, prefrontal cortex, and striatum during various associative learning tasks. In Part 2, they examine the statistical methodologies that have been used to analyze both behavioral learning and learning-related neural activity. They describe a state-space model of behavioral learning that provides accurate estimates of dynamic learning processes and a point-process filter algorithm that tracks the dynamic changes in neural activity on a millisecond time scale. Future challenges for these statistical methodologies and their application to the study of the brain basis of associative learning are discussed.
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