Learning and decision making in monkeys during a rock–paper–scissors game

Lee, Dongsoo; McGreevy, Benjamin P.; Barraclough, Dominic J.

doi:10.1016/j.cogbrainres.2005.07.003

Cited by 110 publications

(86 citation statements)

References 29 publications

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“…Another open question is whether other animals besides humans have the capacity for sophisticated strategic computations of this sort, or whether the capacity to engage in such high-level strategies is a uniquely human trait. Although previous studies of strategic game playing in rhesus macaques indicate that these animals do use simple RL and possibly fictitious updating (29)(30)(31), it has not yet been addressed whether they are capable of higher-level strategizing as found here in our human subjects.…”

Section: Discussionmentioning

confidence: 64%

Neural correlates of mentalizing-related computations during strategic interactions in humans

Hampton

Bossaerts

O’Doherty

2008

Proc. Natl. Acad. Sci. U.S.A.

483

538

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Competing successfully against an intelligent adversary requires the ability to mentalize an opponent's state of mind to anticipate his/her future behavior. Although much is known about what brain regions are activated during mentalizing, the question of how this function is implemented has received little attention to date. Here we formulated a computational model describing the capacity to mentalize in games. We scanned human subjects with functional MRI while they participated in a simple two-player strategy game and correlated our model against the functional MRI data. Different model components captured activity in distinct parts of the mentalizing network. While medial prefrontal cortex tracked an individual's expectations given the degree of modelpredicted influence, posterior superior temporal sulcus was found to correspond to an influence update signal, capturing the difference between expected and actual influence exerted. These results suggest dissociable contributions of different parts of the mentalizing network to the computations underlying higher-order strategizing in humans.computational modeling ͉ decision making ͉ functional MRI ͉ neuroeconomics

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Section: Discussionmentioning

confidence: 64%

Neural correlates of mentalizing-related computations during strategic interactions in humans

Hampton

Bossaerts

O’Doherty

2008

Proc. Natl. Acad. Sci. U.S.A.

483

538

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“…This theory has been successfully applied to the environment that includes multiple decision-makers 17,20,21,35,36 . In reinforcement learning theory, the sum of future rewards expected from a particular action in a particular state of the environment is referred to as value function.…”

Section: Learning In Social Decision Makingmentioning

confidence: 99%

Game theory and neural basis of social decision making

2008

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Decision making in a social group displays two unique features. First, humans and other animals routinely alter their behaviors in response to changes in their physical and social environment. As a result, the outcomes of decisions that depend on the behaviors of multiple decision makers are difficult to predict, and this requires highly adaptive decision-making strategies. Second, decision makers may have other-regarding preferences and therefore choose their actions to improve or reduce the well-beings of others. Recently, many neurobiological studies have exploited game theory to probe the neural basis of decision making, and found that these unique features of social decision making might be reflected in the functions of brain areas involved in reward evaluation and reinforcement learning. Molecular genetic studies have also begun to identify genetic mechanisms for personal traits related to reinforcement learning and complex social decision making, further illuminating the biological basis of social behavior.

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“…Therefore, we examined whether and how the choice behaviour of rhesus monkeys (Macaca mulatta) deviates systematically from a Nash equilibrium during computersimulated zero-sum games. In this paper, we first summarize the results from these behavioural studies showing that similar to human subjects, monkeys showed systematic biases in their choice sequences that can be accounted for by a relatively simple reinforcement-learning algorithm (Lee et al , 2005. We then describe the findings from neurophysiological experiments conducted in monkeys performing the matching pennies task.…”

Section: Introductionmentioning

confidence: 99%

Cortical mechanisms for reinforcement learning in competitive games

Seo

Lee

2008

Phil. Trans. R. Soc. B

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Game theory analyses optimal strategies for multiple decision makers interacting in a social group. However, the behaviours of individual humans and animals often deviate systematically from the optimal strategies described by game theory. The behaviours of rhesus monkeys (Macaca mulatta) in simple zero-sum games showed similar patterns, but their departures from the optimal strategies were well accounted for by a simple reinforcement-learning algorithm. During a computer-simulated zero-sum game, neurons in the dorsolateral prefrontal cortex often encoded the previous choices of the animal and its opponent as well as the animal's reward history. By contrast, the neurons in the anterior cingulate cortex predominantly encoded the animal's reward history. Using simple competitive games, therefore, we have demonstrated functional specialization between different areas of the primate frontal cortex involved in outcome monitoring and action selection. Temporally extended signals related to the animal's previous choices might facilitate the association between choices and their delayed outcomes, whereas information about the choices of the opponent might be used to estimate the reward expected from a particular action. Finally, signals related to the reward history might be used to monitor the overall success of the animal's current decision-making strategy.

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Learning and decision making in monkeys during a rock–paper–scissors game

Cited by 110 publications

References 29 publications

Neural correlates of mentalizing-related computations during strategic interactions in humans

Neural correlates of mentalizing-related computations during strategic interactions in humans

Game theory and neural basis of social decision making

Cortical mechanisms for reinforcement learning in competitive games

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