To behave adaptively, we must learn from the consequences of our actions. Research on reinforcement learning has guided ideas about how such adaptation occurs. In reinforcement learning models, differences between actual and expected outcomes, or reward prediction errors, allow the individual to learn about the utility of performing different actions.
In my research, I have applied reinforcement learning to the types of challenging problems that individuals routinely face. These include (1) sequential choice, (2) learning from instruction and experience, and (3) acquiring abstract response rules. I use behavioral and neuroimaging measures (event-related potentials) to develop computational models of behavioral adaptation and the underlying neural change.
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Walsh, M. M., & Anderson, J. R. (2014). Navigating complex decision spaces: Problems and paradigms in sequential choice. Psychological Bulletin, 140, 466-486.
Walsh, M. M., & Anderson, J. R. (2013). Electrophysiological responses to feedback during the application of abstract rules. Journal of Cognitive Neuroscience, 25, 1986-2002.
Walsh, M. M., & Anderson, J. R. (2012). Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience and Biobehavioral Reviews, 36, 1870-1884.
Walsh, M. M., & Anderson, J. R. (2011). Modulation of the feedback-related negativity by instruction and experience. Proceedings of the National Academy of Science, 108, 19048-19053.
Walsh, M. M., & Anderson, J. R. (2011). Learning from delayed feedback: Neural responses in temporal credit assignment. Cognitive, Affective, and Behavioral Neuroscience, 11, 131-143.
Matthew Walsh 