Performance error-related activity in monkey striatum during social interactions

Báez-Mendoza, Raymundo; Schultz, Wolfram

doi:10.1038/srep37199

Cited by 14 publications

(11 citation statements)

References 30 publications

(44 reference statements)

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“…One potential application of transparent games is related to experimental research on social interactions, including the emerging field of social neuroscience that seeks to uncover the neural basis of social signalling and decision-making using neuroimaging and electrophysiology in humans and animals [63][64][65][66]. So far, most studies have focused on sequential [67,68] or simultaneous games [69]. One of the main challenges in this field is extending these studies to direct real-time interactions that would entail a broad spectrum of dynamic competitive and cooperative behaviours.…”

Section: Discussionmentioning

confidence: 99%

Emergence and suppression of cooperation by action visibility in transparent games

et al. 2020

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Real-world agents, humans as well as animals, observe each other during interactions and choose their own actions taking the partners' ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously without knowing each other's current choices. To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new gametheoretic setting called transparent games, where each player has a certain probability of observing the partner's choice before deciding on its own action. By means of evolutionary simulations, we demonstrate that even a small probability of seeing the partner's choice before one's own decision substantially changes the evolutionary successful strategies. Action visibility enhances cooperation in an iterated coordination game, but reduces cooperation in a more competitive iterated Prisoner's Dilemma. In both games, "Win-stay, loseshift" and "Tit-for-tat" strategies are predominant for moderate transparency, while a "Leader-Follower" strategy emerges for high transparency. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions.

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Section: Discussionmentioning

confidence: 99%

Emergence and suppression of cooperation by action visibility in transparent games

et al. 2020

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“…So far, most studies have focused on sequential [47,48] or simultaneous games [49]. One of the main challenges in this field is extending these studies to direct real-time interactions that would entail a broad spectrum of dynamic competitive and cooperative behaviours.…”

Section: Discussionmentioning

confidence: 99%

Emergence and suppression of cooperation by action visibility in transparent games

Unakafov

Schultze

Kagan

et al. 2018

Preprint

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Real-world agents, such as humans, animals and robots, observe each other during interactions and choose their own actions taking the partners' ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously (without knowing the choices of each other). To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new game-theoretic setting called transparent game, where each player has a certain probability to observe the choice of the partner before deciding on its own action. Using evolutionary simulations, we demonstrate that even a small probability of seeing the partner's choice before one's own decision substantially changes evolutionary successful strategies. Action visibility enhances cooperation in a Bach-or-Stravinsky game, but disrupts cooperation in a more competitive iterated Prisoner's Dilemma. In both games, strategies based on the "Win-stay, lose-shift" and "Tit-for-tat" principles are predominant for moderate transparency, while for high transparency strategies of "Leader-Follower" type emerge. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions.One of the most interesting questions in economics, biological, and social sciences is the emergence and maintenance of cooperation. A popular framework for studying cooperation (or the lack thereof) is Game Theory, which is frequently used to model interactions between "rational" decision-makers. In particular, a model for repeated interactions is provided by iterated games; two settings were previously used [1]:• Simultaneous games: players act at the same time without having any information about the current choice of the partners. Consequently, all players must make a decision under uncertainty concerning the choices of others.• Sequential games: players act in a certain order and the player acting later in the sequence is guaranteed to see the choices of the preceding players. Here the burden of uncertainty only applies to the first player or -if there are more than two players -becomes lighter with every turn in the sequence.Both settings place a simplifying restriction on the decisional context: either all players have no information about the choices of the partners (simultaneous game), or some players always have more information than others (sequential game). This simplification might be disadvantageous for modelling certain behaviours, since humans and animals usually act neither strictly simultaneously nor

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“…Neurophysiological recordings in primates have shown that neurons in select brain structures encode the observed actions (Báez-Mendoza et al., 2013, Fabbri-Destro and Rizzolatti, 2008, Yoshida et al., 2011), performance errors (Báez-Mendoza and Schultz, 2016, Yoshida et al., 2012), and expected rewards (Chang et al., 2013b, Chang et al., 2015) of social others. One recent study identified neurons that explicitly predicted others’ choices in a strategic game (Haroush and Williams, 2015).…”

Section: Introductionmentioning

confidence: 99%

Primate Amygdala Neurons Simulate Decision Processes of Social Partners

Grabenhorst

Báez-Mendoza

Genest

et al. 2019

Cell

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Summary By observing their social partners, primates learn about reward values of objects. Here, we show that monkeys’ amygdala neurons derive object values from observation and use these values to simulate a partner monkey’s decision process. While monkeys alternated making reward-based choices, amygdala neurons encoded object-specific values learned from observation. Dynamic activities converted these values to representations of the recorded monkey’s own choices. Surprisingly, the same activity patterns unfolded spontaneously before partner’s choices in separate neurons, as if these neurons simulated the partner’s decision-making. These “simulation neurons” encoded signatures of mutual-inhibitory decision computation, including value comparisons and value-to-choice conversions, resulting in accurate predictions of partner’s choices. Population decoding identified differential contributions of amygdala subnuclei. Biophysical modeling of amygdala circuits showed that simulation neurons emerge naturally from convergence between object-value neurons and self-other neurons. By simulating decision computations during observation, these neurons could allow primates to reconstruct their social partners’ mental states.

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Performance error-related activity in monkey striatum during social interactions

Cited by 14 publications

References 30 publications

Emergence and suppression of cooperation by action visibility in transparent games

Emergence and suppression of cooperation by action visibility in transparent games

Emergence and suppression of cooperation by action visibility in transparent games

Primate Amygdala Neurons Simulate Decision Processes of Social Partners

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