Pure correlates of exploration and exploitation in the human brain
- 79 Downloads
Balancing exploration and exploitation is a fundamental problem in reinforcement learning. Previous neuroimaging studies of the exploration–exploitation dilemma could not completely disentangle these two processes, making it difficult to unambiguously identify their neural signatures. We overcome this problem using a task in which subjects can either observe (pure exploration) or bet (pure exploitation). Insula and dorsal anterior cingulate cortex showed significantly greater activity on observe trials compared to bet trials, suggesting that these regions play a role in driving exploration. A model-based analysis of task performance suggested that subjects chose to observe until a critical evidence threshold was reached. We observed a neural signature of this evidence accumulation process in the ventromedial prefrontal cortex. These findings support theories positing an important role for anterior cingulate cortex in exploration, while also providing a new perspective on the roles of insula and ventromedial prefrontal cortex.
Keywordsreinforcement learning fMRI decision making
We are grateful to Joel Voss for helpful comments on an earlier draft. This research was carried out at the Harvard Center for Brain Science with the support of the Pershing Square Fund for Research on the Foundations of Human Behavior. This work involved the use of instrumentation supported by the NIH Shared Instrumentation Grant Program, Grant No. S10OD020039. We acknowledge the University of Minnesota Center for Magnetic Resonance Research for use of the multiband-EPI pulse sequences.
Compliance with ethical standards
Conflict of interest
The authors declare no competing financial interests.
- Boorman, E. D., Rushworth, M. F., & Behrens, T. E. (2013). Ventromedial prefrontal and anterior cingulate cortex adopt choice and default reference frames during sequential multi-alternative choice. The Journal of Neuroscience, 33, 2242–2253.Google Scholar
- Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362, 933–942.CrossRefPubMedPubMedCentralGoogle Scholar
- d’Acremont, M., Fornari, E., & Bossaerts, P. (2013). Activity in inferior parietal and medial prefrontal cortex signals the accumulation of evidence in a probability learning task. PLOS ONE, 9, e1002895.Google Scholar
- Erev, I., & Roth, A. E. (1998). Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 88, 848–881.Google Scholar
- Feinberg, D. A., Moeller, S., Smith, S. M., Auerbach, E., Ramanna, S., Glasser, M. F., … Yacoub, E. (2010). Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging. PLOS ONE, 5, e15710.Google Scholar
- Moeller, S., Yacoub, E., Olman, C. A., Auerbach, E., Strupp, J., Harel, N., & Uğurbil, K. (2010). Multiband multislice GE-EPI at 7 Tesla with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magnetic Resonance in Medicine, 63, 1144–1153.CrossRefPubMedPubMedCentralGoogle Scholar
- Quilodran, R., Rothe, M., & Procyk, E. (2008). Behavioral shifts and action valuation in the anterior cingulate cortex. Neuron, 57, 314–325.Google Scholar
- Speekenbrink, M., & Konstantinidis, E. (2015). Uncertainty and exploration in a restless bandit problem. Topics in Cognitive Science, 7, 351–367.Google Scholar
- Spunt, B. (2016). spunt/bspmview: BSPMVIEW v.20161108. Zenodo. Retrieved from https://zenodo.org/record/168074
- Stan Development Team (2016). RStan: The R interface to Stan (R Package Version 2.14.1) [Computer software]. Retrieved from http://mc-stan.org
- Xu, J., Moeller, S., Auerbach, E. J., Strupp, J., Smith, S. M., Feinberg, D. A., … Ugurbil, K. (2013). Evaluation of slice accelerations using multiband echo planar imaging at 3 T. NeuroImage, 83, 991–1001.Google Scholar