Our ability to selectively engage with our environment enables us to guide our learning and to take advantage of its benefits. When facing multiple possible actions, our choices are a critical aspect of learning. In the case of learning from rewarding feedback, there has been substantial theoretical and empirical progress in elucidating the associated behavioral and neural processes, predominantly in terms of a reward prediction error, a measure of the discrepancy between actual versus expected reward. Nevertheless, the distinct influence of choice on prediction error processing and its neural dynamics remains relatively unexplored. In this study we used a novel paradigm to determine how choice influences prediction error processing and to examine whether there are correspondingly distinct neural dynamics. We recorded scalp electroencephalogram while healthy adults were administered a rewarded learning task in which choice trials were intermingled with control trials involving the same stimuli, motor responses, and probabilistic rewards. We used a temporal difference learning model of subjects' trial-by-trial choices to infer subjects' image valuations and corresponding prediction errors. As expected, choices were associated with lower overall prediction error magnitudes, most notably over the course of learning the stimulus-reward contingencies. Choices also induced a higher-amplitude relative positivity in the frontocentral event-related potential about 200. ms after reward signal onset that was negatively correlated with the differential effect of choice on the prediction error. Thus choice influences the neural dynamics associated with how reward signals are processed during learning. Behavioral, computational, and neurobiological models of rewarded learning should therefore accommodate a distinct influence for choice during rewarded learning.
|Original language||English (US)|
|Number of pages||10|
|State||Published - Jan 15 2011|
Bibliographical noteFunding Information:
We thank Genela Morris and Hagai Bergman for helpful discussions on the paradigm, Julie Onton and Klaus Gramann for helpful discussions about the EEG analysis, Andrey Vankov for assistance with the custom acquisition software and Alice Ahn for help with data collection. This work was supported by the National Science Foundation grant SBE-0542013 to the Temporal Dynamics of Learning Center, an NSF Science of Learning Center, Office of Naval Research MURI grant N00014-10-1-0072 , and the National Institutes of Health grant 2 R01 NS036449-11 .
- Decision making
- Event related potential