Abstract
Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this ‘exploration–exploitation’ dilemma1, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning2 (RL) theory, indicates that a dopaminergic3,4, striatal5,6,7,8,9 and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical1 and ethological10 perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Gittins, J. C. & Jones, D. in Progress in Statistics (ed. Gani, J.) 241–266 (North-Holland, Amsterdam, 1974)
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, Cambridge, Massachusetts, 1998)
Montague, P. R., Dayan, P. & Sejnowski, T. J. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. J. Neurosci. 16, 1936–1947 (1996)
Bayer, H. M. & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron 47, 129–141 (2005)
Delgado, M. R., Nystrom, L. E., Fissell, C., Noll, D. C. & Fiez, J. A. Tracking the hemodynamic responses to reward and punishment in the striatum. J. Neurophysiol. 84, 3072–3077 (2000)
Knutson, B., Westdorp, A., Kaiser, E. & Hommer, D. fMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 12, 20–27 (2000)
McClure, S. M., Berns, G. S. & Montague, P. R. Temporal prediction errors in a passive learning task activate human striatum. Neuron 38, 339–346 (2003)
O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H. & Dolan, R. J. Temporal difference models and reward-related learning in the human brain. Neuron 38, 329–337 (2003)
O'Doherty, J. P. et al. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004)
Charnov, E. L. Optimal foraging: The marginal value theorem. Theor. Popul. Biol. 9, 129–136 (1976)
Owen, A. M. Cognitive planning in humans: Neuropsychological, neuroanatomical and neuropharmacological perspectives. Prog. Neurobiol. 53, 431–450 (1997)
Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioural control. Nature Neurosci. 8, 1704–1711 (2005)
Kakade, S. & Dayan, P. Dopamine: Generalization and bonuses. Neural Netw. 15, 549–559 (2002)
Kaelbling, L. P. Learning in Embedded Systems (MIT Press, Cambridge, Massachusetts, 1993)
McClure, S. M., Laibson, D. I., Loewenstein, G. & Cohen, J. D. Separate neural systems value immediate and delayed monetary rewards. Science 306, 503–507 (2004)
O'Doherty, J., Kringelbach, M. L., Rolls, E. T., Hornak, J. & Andrews, C. Abstract reward and punishment representations in the human orbitofrontal cortex. Nature Neurosci. 4, 95–102 (2001)
O'Doherty, J. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Curr. Opin. Neurobiol. 14, 769–776 (2004)
Gottfried, J. A., O'Doherty, J. & Dolan, R. J. Encoding predictive reward value in human amygdala and orbitofrontal cortex. Science 301, 1104–1107 (2003)
Tanaka, S. C. et al. Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nature Neurosci. 7, 887–893 (2004)
Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001)
Ramnani, N. & Owen, A. M. Anterior prefrontal cortex: Insights into function from anatomy and neuroimaging. Nature Rev. Neurosci. 5, 184–194 (2004)
Koechlin, E., Ody, C. & Kouneiher, F. A. The architecture of cognitive control in the human prefrontal cortex. Science 302, 1181–1185 (2003)
Braver, T. S. & Bongiolatti, S. R. The role of frontopolar cortex in subgoal processing during working memory. Neuroimage 15, 523–536 (2002)
Platt, M. L. & Glimcher, P. W. Neural correlates of decision variables in parietal cortex. Nature 400, 233–238 (1999)
Sugrue, L. P., Corrado, G. S. & Newsome, W. T. Matching behaviour and the representation of value in the parietal cortex. Science 304, 1782–1787 (2004)
Dorris, M. C. & Glimcher, P. W. Activity in posterior parietal cortex is correlated with the relative subjective desirability of action. Neuron 44, 365–378 (2004)
Grefkes, C. & Fink, G. R. The functional organization of the intraparietal sulcus in humans and monkeys. J. Anat. 207, 3–17 (2005)
Burgess, P. W., Veitch, E., de Lacy Costello, A. & Shallice, T. The cognitive and neuroanatomical correlates of multitasking. Neuropsychologia 38, 848–863 (2000)
Usher, M., Cohen, J. D., Servan-Schreiber, D., Rajkowski, J. & Aston-Jones, G. The role of locus coeruleus in the regulation of cognitive performance. Science 283, 549–554 (1999)
Doya, K. Metalearning and neuromodulation. Neural Netw. 15, 495–506 (2002)
Acknowledgements
We thank J. Li, S. McClure, B. King-Casas and P. R. Montague for sharing their unpublished data on exploration, and Y. Niv, Z. Gharamani and C. Camerer for discussions. Funding was from a Royal Society USA Research Fellowship (N.D.), the Gatsby Foundation (N.D., P.D.), the EU BIBA project (N.D., P.D.), and a Wellcome Trust Programme Grant (J.O.D., R.D.).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.
Supplementary information
Supplementary Notes
This file contains Supplementary Methods, Supplementary Discussion and Supplementary Tables 1–5. (PDF 371 kb)
Rights and permissions
About this article
Cite this article
Daw, N., O'Doherty, J., Dayan, P. et al. Cortical substrates for exploratory decisions in humans. Nature 441, 876–879 (2006). https://doi.org/10.1038/nature04766
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1038/nature04766
This article is cited by
-
Dynamic computational phenotyping of human cognition
Nature Human Behaviour (2024)
-
Exploring the steps of learning: computational modeling of initiatory-actions among individuals with attention-deficit/hyperactivity disorder
Translational Psychiatry (2024)
-
Corticostriatal activity related to performance during continuous de novo motor learning
Scientific Reports (2024)
-
How do animals weigh conflicting information about reward sources over time? Comparing dynamic averaging models
Animal Cognition (2024)
-
Exploring global trends and future directions in advertising research: A focus on consumer behavior
Current Psychology (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.