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Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning
Open accessAbstract
Decision-making in the presence of other competitive intelligent agents is fundamental for social and economic behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions of others competing for the same rewards. However, whereas we know much about strategic learning at both theoretical and behavioral levels, we know relatively little about the underlying neural mechanisms. Here, we show using a multi-strategy competitive learning paradigm that strategic choices can be characterized by extending the reinforcement learning (RL) framework to incorporate agents’ beliefs about the actions of their opponents. Furthermore, using this characterization to generate putative internal values, we used model-based functional magnetic resonance imaging to investigate neural computations underlying strategic learning. We found that the distinct notions of prediction errors derived from our computational model are processed in a partially overlapping but distinct set of brain regions. Specifically, we found that the RL prediction error was correlated with activity in the ventral striatum. In contrast, activity in the ventral striatum, as well as the rostral anterior cingulate (rACC), was correlated with a previously uncharacterized belief-based prediction error. Furthermore, activity in rACC reflected individual differences in degree of engagement in belief learning. These results suggest a model of strategic behavior where learning arises from interaction of dissociable reinforcement and belief-based inputs.
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“Additionally, ventral striatum and medial prefrontal cortex signals encode information learned about strategic actions of competitors 72, 88, 90.”
From The social brain and reward: Social information processing in the human striatum (2014) · cites this paper as [90]
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BackgroundResultNeural correlates of recursive thinking during interpersonal strategic interactions
Zhen, S., & Yu, R. (2021) · Human Brain Mapping · doi:10.1002/hbm.25355 · Cited by 14
“…may share similar Bayesian inference mechanisms (Ahn, Krawitz, Kim, Busemeyer, & Brown, 2013; Aitchison & Lengyel, 2017; Baker & Tenenbaum, 2014; Behrens, Hunt, Woolrich, & Rushworth, 2008; Devaine, Hollard, & Daunizeau, 2014; Khalvati et al., 2019; Robalino & Robson, 2012; Zhu et al., 2012).”
BackgroundNeural mechanisms of social learning and decision-making
Ni, Y., & Li, J. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1833-8 · Cited by 16
Hierarchical Control of Behaviour in Human Prefrontal Cortex
Duverne, S., & Koechlin, É. (2017) · Wiley eBooks · doi:10.1002/9781118920497.ch12 · Cited by 14
Neurocomputational mechanism of real-time distributed learning on social networks
Jiang, Y., Mi, Q., & Zhu, L. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01258-y · Cited by 16
“4a (peak x, y, z = 60, 11, 23).”
Self-citationDoes It Pay to Increase Competition in Combinatorial Conservation Auctions?
Iftekhar, M. S., Hailu, A., & Lindner, R. K. (2014) · Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · doi:10.1111/cjag.12028 · Cited by 13
Neuroeconomic Measures of Social Decision-Making Across the Lifespan
Zhu, L., Walsh, D., & Hsu, M. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00128 · Cited by 13
“This is in line with the findings through fMRI that there may exist significant tissue loss in gray matter volume in mPFC, which is indicated to be involved in belief-based learning in our previous study (Zhu et al., 2012).”
BackgroundMethodResultInfluentialSelf-citationExcessive deliberation in social anxietyPreprint
Hunter, L. E., Meer, E. A., Gillan, C. M., Hsu, M., & Daw, N. D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/522433 · Cited by 13
“Via their web browser, all participants completed the Liebowitz Social Anxiety Scale (LSAS), Liebowitz, 1987), an abbreviated nine-item Ravens’ matrix test to approximate IQ (Bilker et al., 2012), and 80 rounds of a patent race game (Zhu et al., 2012).”
BackgroundMethodResultInfluentialSelf-citationPatients with basal ganglia damage show preserved learning in an economic game
Zhu, L., Jiang, Y., Scabini, D., Knight, R. T., & Hsu, M. (2019) · Nature Communications · doi:10.1038/s41467-019-08766-1 · Cited by 15
Self-citationAgency Modulates the Lateral and Medial Prefrontal Cortex Responses in Belief-Based Decision Making
Xue, G., He, Q., Lu, Z., Levin, I. P., Dong, Q., & Bechara, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065274 · Cited by 12
“Accordingly, it has been implicated in strategic reasoning in complex social games [37], and the representation of belief-based prediction errors [3].”
BackgroundThe interaction of perceived control and Gambler's fallacy in risky decision making: An f MRI study
Shao, R., Sun, D., & Lee, T. M. C. (2016) · Human Brain Mapping · doi:10.1002/hbm.23098 · Cited by 11
“Our data are also consistent with previous evidence implicating the ACC and DLPFC in strategic decision making based on the response patterns of other agents [Steinbeis et al., 2012; Zhu et al., 2012].”
ResultLearning whom to cooperate with: neurocomputational mechanisms for choosing cooperative partners
Jin, T., Zhang, S., Lockwood, P. L., Vilares, I., Wu, H., Liu, C., & Ma, Y. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac365 · Cited by 10
Opponent Identity Influences Value Learning in Simple Games
Vickery, T. J., Kleinman, M. R., Chun, M. M., & Lee, D. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3530-14.2015 · Cited by 10
“Further, mPFC carries value signals specifically related to beliefbased value learning (Burke et al., 2010; Zhu et al., 2012), and associative learning of the reliability of social signals (Behrens et al., 2008).”
BackgroundRealpe-Gómez, J., Vilone, D., Andrighetto, G., Nardin, L. G., & Montoya, J. A. (2018) · Games · doi:10.3390/g9040090 · Cited by 10
“This hybrid approach has been extensively explored in the field of behavioral economics and it has been rather successful in explaining the interactive learning of humans in games [11,12,15,16].”
BackgroundLatent goal models for dynamic strategic interaction
Iqbal, S., Yin, L., Drucker, C. B., Qian, K., Gariépy, J., Platt, M. L., & Pearson, J. M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006895 · Cited by 10
Wong, A., Merholz, G., & Maoz, U. (2021) · Scientific Reports · doi:10.1038/s41598-021-99967-6 · Cited by 10
“Studies in animal models have shown that a competitive setting triggers strategic counterprediction, rather than stochastic behavior, even when acting as random as possible maximizes reward 36–38 .”
BackgroundA Neural Mechanism of Strategic Social Choice under Sanction-Induced Norm Compliance
Makwana, A., Grön, G., Fehr, E., & Hare, T. A. (2015) · eNeuro · doi:10.1523/eneuro.0066-14.2015 · Cited by 9
“…has been associated with value computation in nonstrategic decision contexts ranging from primary to social rewards for both self and others (Nicolle et al., 2012; Bartra et al., 2013; Clithero and Rangel, 2013) and in choices during competitive games (Hampton et al., 2008; Zhu et al., 2012).”
BackgroundMethodResultInfluentialHow mood shapes belief updating bias in depression
Bottemanne, H., Frileux, S., Sévoz‐Couche, C., Pelloux, Y., Colle, R., & Corruble, E. (2025) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-025-01297-x · Cited by 8
“…plays a key role in representing the expected value of stimuli (Kuzmanovic et al., 2018), whereas the ventral striatum, which is involved in reward processing, tracks prediction errors related to rewards and punishments, prompting the brain to adjust its expectations accordingly (Zhu et al., 2012).”
Reinforcement Learning and Strategic Reasoning During Social Decision-Making
Seo, H., & Lee, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805308-9.00018-x · Cited by 9
Aeberhard, A., Gschwind, L., Kossowsky, J., Lukšys, G., Papassotiropoulos, A., de Quervain, D. J. ‐., & Vogler, C. (2018) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-018-0071-5 · Cited by 8
Sazhin, D., Wyngaarden, J. Β., Dennison, J. B., Zaff, O., Fareri, D. S., McCloskey, M. S., Alloy, L. B., Jarcho, J. M., & Smith, D. V. (2023) · Biological Psychology · doi:10.1016/j.biopsycho.2024.108857 · Cited by 9
Navigating uncertainty in human social decision‐making: Consequences and coping strategies
Li, T., Li, S., Li, B., Zhang, Z., Luo, Y., & Feng, C. (2023) · Social and Personality Psychology Compass · doi:10.1111/spc3.12756 · Cited by 8
The neural correlates of reaching focal points
Chowdhury, A., Liu, C., & Yu, R. (2020) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2020.107397 · Cited by 6
Neurocognitive Underpinnings of Aggressive Predation in Economic Contests
Rojek-Giffin, M., Lebreton, M. P., Scholte, H. S., van Winden, F. A. A. M., Ridderinkhof, K. R., & Dreu, C. K. W. D. (2020) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01545 · Cited by 6
“We approached this issue with a computational framework modeling reward and risk prediction errors based on k-level reasoning in belief formation (Zhu et al., 2012; Camerer et al., 2004; Nagel, 1995).”
InfluentialDeliberation during online bargaining reveals strategic information
Cotet, M., Zhao, W. J., & Krajbich, I. M. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2410956122 · Cited by 5
“…models ( 77 , 78 ) where people use limited steps of thinking, Experience Weighted Attraction where people learn strategies based on pre-dispositions and payoff experience ( 79 , 80 ), and Quantal Response Equilibrium where people are noisy and believe that others are noisy as well ( 81 ).”
Neurocomputations of strategic behavior: From iterated to novel interactions
Jiang, Y., Wu, H., Mi, Q., & Zhu, L. (2022) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1598 · Cited by 7
“, instead of fixed pairing, repairing participants randomly on a trial-by-trial basis in learning experiments), strategic learning in competitive scenarios sometimes involves a hybrid of belief and reward learning, rather than mental-state learning (Zhu et al., 2012).”
BackgroundMethodInfluentialSelf-citationOrbitofrontal-Hippocampal State Coding Dynamics during Reversal Learning
Sanchez, A. N., Ford, C. F., Balewski, Z. Z., & Wallis, J. D. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1228-24.2025 · Cited by 5
A Bayesian cognitive hierarchy model with fixed reasoning levels
Jun, F., Qin, X., & Wang, X. (2021) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2021.10.030 · Cited by 5
Autonomous behaviour and the limits of human volition
Ota, K., Charles, L., & Haggard, P. (2023) · Cognition · doi:10.1016/j.cognition.2023.105684 · Cited by 4
Learning in Games : Neural Computations underlying Strategic Learning
Hsu, M., & Zhu, L. (2012) · Recherches économiques de Louvain · doi:10.3917/rel.783.0047 · Cited by 4
“" Here we review recent efforts, including both our own work (Zhu et al. 2012) and those of others (Behrens et al. 2008; Hampton et al. 2008), toward a more quantitative, model-building approach to understand the neural underpinnings of strategic behavior by studying the adaptation process itself.”
BackgroundSelf-citationThe computational and neural substrates underlying social learning
Li, S., Chen, X., Zhai, Y., Zhang, Y., Zhang, Z., & Chunliang, F. E. N. G. (2021) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2021.00677 · Cited by 3
The neural underpinnings of allocentric thinking in a novel signaling task
Zhen, S., Chowdhury, A., & Yu, R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117808 · Cited by 3
“In social tasks, striatal activation has also been observed in social learning and implicated in social prediction error and belief updating ( Behrens et al., 2008 ; Hampton et al., 2008 ; Hertz et al., 2017 ; Xiang et al., 2012 ; Zhu et al., 2012 ).”
Ritter, M., Wang, M., Pritz, J., Menssen, O., & Boos, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259963 · Cited by 3
Young, M., Killen, M., Lee–Kim, J., & Park, Y. (2012) · International Journal of Game-Based Learning · doi:10.4018/ijgbl.2012100105 · Cited by 3
Metadata
- Authors
- Zhu, L., Mathewson, K. E., & Hsu, M.
- Affiliations
- Department of Economics and
- University of Alberta (CA)
- University of California, Berkeley (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-01-18
- Volume
- 109
- Issue
- 5
- Pages
- 1419-1424
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3277161
- OpenAlex
- W2165958648
- Cited by
- 125
- References
- 45
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 6 · 2023: 4 · 2022: 4 · 2021: 14 · 2020: 9 · 2019: 7 · 2018: 7 · 2017: 8 · 2016: 11 · 2015: 11 · 2014: 13 · 2013: 11 · 2012: 7
- FWCI
- 6.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- strategic learning, competitive decision-making, opponent modeling, belief learning, ventral striatum, rostral anterior cingulate cortex, model-based reinforcement learning, internal values, individual differences
- MeSH terms
- Decision Making, Humans, Learning, Motivation
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