Prefrontal cortex and decision making in a mixed-strategy game
Abstract
In a multi-agent environment, where the outcomes of one's actions change dynamically because they are related to the behavior of other beings, it becomes difficult to make an optimal decision about how to act. Although game theory provides normative solutions for decision making in groups, how such decision-making strategies are altered by experience is poorly understood. These adaptive processes might resemble reinforcement learning algorithms, which provide a general framework for finding optimal strategies in a dynamic environment. Here we investigated the role of prefrontal cortex (PFC) in dynamic decision making in monkeys. As in reinforcement learning, the animal's choice during a competitive game was biased by its choice and reward history, as well as by the strategies of its opponent. Furthermore, neurons in the dorsolateral prefrontal cortex (DLPFC) encoded the animal's past decisions and payoffs, as well as the conjunction between the two, providing signals necessary to update the estimates of expected reward. Thus, PFC might have a key role in optimizing decision-making strategies.
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Neural activity in all three cortical areas carries the information of past reward events.
“Neural activity in all three cortical areas carries the information of past reward events19-22.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [22]
In the matching pennies task, the animals made their decisions largely on the basis of their reward history.
“Although the global optimal strategy for the matching pennies task is to choose randomly and therefore does not require memory, the animals made their decisions largely on the basis of their reward history11,19-22.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [22]
Although the overall performance of monkeys was nearly optimal, their trial-by-trial decisions, locally in time, were influenced by previous rewards and actions.
“Although the overall performance of monkeys was nearly optimal, their trial-by-trial decisions, locally in time, were influenced by previous rewards and actions11,19-22.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [22]
Single neurons in a number of cortical areas have activity correlated with reinforcer frequency and magnitude.
“Single neurons in a number of cortical areas have activity correlated with reinforcer frequency (Barraclough, Conroy, & Lee, 2004; Platt & Glimcher, 1999; Sugrue et al., 2004) and magnitude (Platt & Glimcher, 1999).”
From DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS (2005) · cites this paper as Barraclough, Conroy, & Lee, 2004
These neurons send dense projections to prefrontal cortex and striatum, and current evidence suggests these areas may use a prediction error to estimate reinforcer rate.
“These neurons send dense projections to the prefrontal cortex and the striatum, and current evidence suggests that these areas may use a prediction error to estimate reinforcer rate (Barraclough et al., 2004; Haruno et al., 2004; O'Doherty et al., 2004).”
From DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS (2005) · cites this paper as Barraclough et al., 2004
Barraclough et al. (2004) present evidence from a repeated-choice task that some neurons in prefrontal cortex encode the identity of the last response, independent of whether the last response was reinforced or not.
“Barraclough et al. (2004) present evidence from a repeated-choice task that some neurons in prefrontal cortex encode the identity of the last response, independent of whether the last response was reinforced or not.”
From DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS (2005) · cites this paper as Barraclough et al. (2004)
In reinforcement models for reward-dependent choice, the DV is defined by values of response options which are deterministically updated according to past rewards, whereas the choice is generated by a probabilistic decision rule (e.g., a softmax criterion) based on the DV.
“On the other hand, in reinforcement models for reward-dependent choice (Barraclough et al., 2004; Sugrue et al., 2004, 2005; Lau and Glimcher, 2005), the DV is defined by values of response options which are deterministically updated according to past rewards, whereas the choice is generated by a probabilistic decision rule (e.g., a softmax criterion) based on the DV.”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Barraclough et al., 2004
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Self-citationYarkoni, T., Gray, J. R., Chrastil, E. R., Barch, D. M., Green, L., & Braver, T. S. (2005) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2005.01.013 · Cited by 59 · see citing works
Amemori, K.-I., Amemori, S., & Graybiel, A. M. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.1731-14.2015 · Cited by 49 · see citing works
Effects of α-2A adrenergic receptor agonist on time and risk preference in primates
Kim, S., Bobeica, I., Gamo, N. J., Arnsten, A. F. T., & Lee, D. (2012) · Psychopharmacology · doi:10.1007/s00213-011-2520-0 · Cited by 47 · see citing works
Self-citationValuation of uncertain and delayed rewards in primate prefrontal cortex
Kim, S., Hwang, J., Seo, H., & Lee, D. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.03.010 · Cited by 37 · see citing works
Self-citationActivity in the superior temporal sulcus highlights learning competence in an interaction game
Haruno, M., & Kawato, M. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2707-08.2009 · Cited by 33 · see citing works
A computational model of language functions in flexible goal-directed behaviour
Granato, G., Borghi, A. M., & Baldassarre, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-78252-y · Cited by 34 · see citing works
A computational model of inner speech supporting flexible goal-directed behaviour in autism
Granato, G., Borghi, A. M., Mattera, A., & Baldassarre, G. (2022) · Scientific Reports · doi:10.1038/s41598-022-18445-9 · Cited by 8 · see citing works
Colas, J. T., O’Doherty, J. P., & Grafton, S. T. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011950 · Cited by 6 · see citing works
- Authors
- Barraclough, D. J., Conroy, M. L., & Lee, D.
- Affiliations
- University of Rochester (US)
- Corresponding author
- Lee, Daeyeol
- Venue
- Nature Neuroscience
- Published
- 2004-03-07
- Volume
- 7
- Issue
- 4
- Pages
- 404-410
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2102068574
- Cited by
- 678
- References
- 58
- Citations by year
- 2026: 13 · 2025: 24 · 2024: 26 · 2023: 23 · 2022: 21 · 2021: 37 · 2020: 28 · 2019: 26 · 2018: 25 · 2017: 29 · 2016: 26 · 2015: 28 · 2014: 35 · 2013: 29 · 2012: 41
- FWCI
- 19.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (EY01319, P30EY001319)
- National Institute of Neurological Disorders and Stroke (R01NS044270, NS44270)
- 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
- prefrontal cortex, dorsolateral prefrontal cortex, decision-making, mixed strategy game, reinforcement learning, opponent modeling, reward history, multi-agent decision making
- MeSH terms
- Animals, Choice Behavior, Electrophysiology, Evoked Potentials, Game Theory, Macaca mulatta, Male, Mental Processes, Neurons, Practice, Psychological, Probability Learning, Prefrontal Cortex
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