To live with others is to make concessions. You may want to go to the movies tonight, but your partner may prefer the theatre: reaching a mutually desirable goal – that is, spending time together – requires adjusting your preferences to theirs. Many other social species also make such decisions, in particular monkeys that live in large groups.

Conceptually, these interactions are known as coordination games. In such scenarios, two players must coordinate their actions to attain a coveted reward, but they must also resolve a conflict about who gets the larger share. This makes the joint strategy non-trivial, and different pairs of players might resort to different strategies. In the laboratory, coordination games are often tested in settings which do not allow participants to monitor each other’s behaviors as they make these complex choices. In real life, however, individuals making a joint decision can often observe each other and receive immediate feedback.

In response, Moeller et al. developed a new way to test coordination games that allows more realistic social interactions. In their setup, two participants face each other and use a shared see-through touchscreen to perform a task. This new design was used to test how humans and macaque monkeys solved a simplified version of the ‘Bach or Stravinsky’ coordination game, which involves choosing between a red and blue target on the screen. Players in a pair had been trained to ‘prefer’ opposite colors. In this game, collaboration is beneficial (both individuals get a better prize if they choose the same color) but also creates unfairness (the reward is higher for the participant whose ‘favorite’ color is selected).

When paired up, both humans and monkeys learned to collaborate and to go for the same color (or, in some monkey pairs, the same side of the screen). However, only humans took turns selecting red or blue so that players could alternate getting the highest reward. Monkeys usually settled on one color throughout the game, unless they had learned the ‘turn-taking’ strategy from a human partner; in that case, the color chosen in each trial was typically determined by the monkey who was the faster to move.

These experiments show how monkeys and humans use visual information about their partner’s actions to coordinate their choices, paving the way for further decision-making studies that accurately reflect how interactions unfold in real life. Moeller et al. expect that this will help to understand how cooperation and competition emerge in these two species, including how direct face-to-face contact, or lack thereof in some aspects of our modern world, shapes our social behavior.

The majority of primate species live in complex social groups, in which interactions range from intense competition to cooperation (Byrne and Bates, 2010; Chang et al., 2013; Jaeggi et al., 2010; Joly et al., 2017; Platt et al., 2016). To adjust behavior optimally, individuals need to assess their own actions and goals, and those of other group members, while taking into account the history of interactions with and between these individuals (Frith and Frith, 2010; Platt et al., 2016; de Waal, 2007). Coordination is essential for maintaining cohesion between group members, avoiding conflicts, and achieving individual and joint action goals. Here, we focus on understanding how such coordination can be achieved and maintained in a dyadic ‘transparent’ setting in which agents can observe each other’s ongoing actions and social cues (Unakafov et al., 2020; Unakafov et al., 2019), like in many natural situations (Conty et al., 2012; Fruteau et al., 2013; Rizzolatti et al., 2001; Silk, 2007).

Game theory, developed to study strategic interactions in rational decision-makers (Von Neumann and Morgenstern, 1944), offers a powerful framework to investigate dyadic social interactions (Rilling and Sanfey, 2011; Sanfey, 2007; Tremblay et al., 2017) and the evolution of coordination (Brosnan, 2018; Santos and Rosati, 2015; Smith, 1979). In 2×2 games (Rapoport et al., 1976; Rapoport, 1973), each of the two agents chooses one of two actions and the outcome depends on the combination of their choices. Non-zero-sum 2×2 games that have both cooperative and competitive elements with aligned and opposing interests are particularly interesting for understanding many realistic scenarios. Here, the agents need to coordinate their choices to maximize individual and/or joint rewards (Rapoport, 1967). For example, in the Stag Hunt (or Assurance) game, agents choose between maximizing the individual as well as joint reward and minimizing the individual risk (Skyrms, 2003). Humans (Homo sapiens), chimpanzees (Pan troglodytes), rhesus macaques (Macaca mulatta), and capuchin monkeys (Cebus apella) coordinate at the above chance level on mutually beneficial high-reward/high-risk choices in the iterated Stag Hunt (Brosnan et al., 2012; Brosnan et al., 2011; Bullinger et al., 2011; Duguid et al., 2014). Similar to Stag Hunt, in Prisoner’s Dilemma (PD) two agents maximize their joint reward if they both choose to cooperate. Yet, an individual agent obtains the highest reward when defecting while the partner cooperates (and receives the lowest reward), creating a dilemma between the selfish temptation to defect and a riskier cooperation that requires trust. Humans often show a bias towards cooperative behavior (Camerer, 2003; Rilling et al., 2002; Wedekind and Milinski, 1996). Remarkably, it was demonstrated that macaques, while mostly defecting, chose mutual cooperation significantly more and mutual defection significantly less than expected by chance, especially after preceding cooperation, in an iterated PD game (Haroush and Williams, 2015). This finding suggests that macaques can at least partially overcome the selfish motives and reciprocate cooperation.

Another type of conflict that contrasts selfish and prosocial preferences is implemented in the Conflict game (also known as Hawk-Dove, or game of Chicken) (Brosnan et al., 2017; Smith, 1979). These anti-coordination games model a competition over a shared resource that can be monopolized by one agent, while an actual clash is highly detrimental to both. Humans, capuchins, and macaques often converge on anti-coordination (Brosnan et al., 2017), but importantly, only humans alternate between the two individually-optimal anti-coordinated choices (Brosnan et al., 2017; Grueneisen and Tomasello, 2017; Helbing et al., 2005). Such alternating behavior results in the maximal and equal payoff for both agents and is called ‘cooperative turn-taking’ (Bornstein et al., 1997; Colman and Browning, 2009; Noë, 2006).

In the above games, reward-optimizing rational agents should coordinate on one option (Stag Hunt and Prisoner’s Dilemma) or anti-coordinate on opposite options (Conflict game). A third, less studied type of game that emphasizes the coordination of either of two options is known as Battle of the Sexes, or Bach-or-Stravinsky game (BoS) (Kilgour and Fraser, 1988). Each agent has an individually preferred option, but coordinating on either one of these options adds the same bonus to both agents. This renders any coordinated choice better than no coordination for both agents, but one coordinated choice is better for the first agent and the other better for the second agent. The rational choice is to coordinate, but unlike the conceptually simpler optimal convergence on one option in Stag Hunt and PD, BoS includes an inherent conflict about who profits the most. This combination of cooperation and conflict offers an interesting opportunity for studying social interactions in nonhuman primates. As in the Conflict game, games of the BoS type revealed that humans often take turns, switching between the two coordinated options to ensure fairness (Ioannou and Romero, 2014; Sonsino and Sirota, 2003). Interestingly, in a rope-pulling task that requires cooperation but rewards only one agent at a time, trial-by-trial turn-taking frequently took place in 5-year-old children, but neither in 3-year-old children nor in chimpanzees (Melis et al., 2016). This result begs the question whether turn-taking requires special social or cognitive abilities that are unique to humans. We therefore compared the behavior of (adult) humans and rhesus macaques in the BoS economic game.

Traditionally, economic games are played either ‘simultaneously’ (neither agent knows the choice of the other before making its own decision) or sequentially in a predetermined order. Yet, real dyadic interactions often play out with the partner’s actions in direct sight (Dugatkin et al., 1992; van Doorn et al., 2014). Thus, the timing of one’s own and the other’s actions becomes part of the strategy space (McDonald et al., 2019). Such a ‘transparent’ continuous time setting can change choice strategies as compared to the classic simultaneous and sequential settings (Noë, 2006).

To account for changes in strategies during action visibility compared to ‘opaque’ (simultaneous) choices, we recently developed the concept of ‘transparent games’ that extends the classical evolutionary game-theoretic analysis to real-time interactions in which the visibility of partners’ actions depends on their relative action times (Unakafov et al., 2020; Unakafov et al., 2019). Here, we use the theoretical insights from that work and analyze short- and long-term dynamics of choices, mutual information, and action times in humans and rhesus monkeys playing a transparent version of an iterated movement-based BoS game. To this end, we designed a novel dyadic interaction platform where two human or monkey agents face each other and act on the same visual objects on a vertical touch-sensitive transparent display between them. This configuration provides action visibility, i.e. it allows agents to monitor and react to each other’s actions in real-time, emulating naturalistic interactions under well-controlled laboratory conditions.

To facilitate inter-species comparability, we paired human or macaque agents without explicit task instructions. We expected that the transparency and instantaneous coaction (van Doorn et al., 2014) would facilitate efficient coordination in both species. Based on their cognitive abilities (Proto et al., 2019), propensity for fairness (Peysakhovich et al., 2014), and perspective-taking (Devaine et al., 2017; Herrmann et al., 2007), we predicted humans to engage in pro-social turn-taking, where the reward maximization is alternated between partners. We predicted that macaques would also utilize the information about the partner’s actions, but would be less likely to grant an advantage to the other player. Instead, we expected that the monkeys would coordinate to increase individual (and joint) rewards, but without turn-taking to balance rewards between players.

Two human or macaque agents sat face-to-face and performed a task on a dyadic interaction platform using a shared transparent vertical workspace (Figure 1A). Agents could simultaneously see the stimuli and each other’s actions. In our variant of the Bach-or-Stravinsky (BoS) paradigm (Materials and methods), each agent could choose one of two simultaneously presented color targets (red and blue, Figure 1B). The left/right position of each color was randomized across trials (50% left, 50% right). The rewards followed the payoff matrix shown in Figure 1C and depended on the choices of both agents. Prior to the first dyadic session, each human or macaque agent in a pair was individually trained to associate one of the two color targets with a larger reward, so that the two agents preferred different colors. In the dyadic trials, selecting the same target resulted in an additional reward on top of the individually trained values - such that choosing the same target was always better than choosing different targets, for both agents individually, and as a group. But in such coordinated trials, the agent whose individually preferred (‘own’) color was selected receives a larger reward. Therefore, agents may attempt to behave selfishly/competitively in order to gain more by insisting on their own target color – which pays off as long as the partner accommodates. This paradigm probes the ability to coordinate in order to obtain higher rewards, and the capacity to deal with the unfairness introduced in each coordinated trial.

Figure 1

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Human pairs mainly converge on fair coordination

We recruited 38 naïve human subjects to assess performance in the transparent BoS paradigm (Supplementary file 1). Figure 1E and F illustrates an exemplary human pair that developed such alternating turn-taking behavior, showing the fraction of choosing own color (FCO) - the likelihood for each agent to select their own preferred color, and the fraction of choosing left side (FCL) - the likelihood for each agent to select the target on the left side of the display. These measures provide direct insight into each agent’s target and side biases. Here, the FCO curves show that this pair mostly jointly selected A’s or B’s color. After an initial period of long blocks of trials, the pair switched to shorter blocks. The FCL curves show that the agents did not have a side bias, as was also the case for the majority of pairs (Figure 2—figure supplement 1B).

Human dyads showed mainly one of three different choice patterns. Figure 2A plots the larger of the two average fractions of choosing own values in a pair versus the smaller of the two, for all 19 pairs. The FCOs of 53% (10 of 19 pairs, teal underlay) had balanced values close to 0.5 for both agents, indicating alternating between colors, using either trial-by-trial or block-wise turn-taking. The remaining 47% (9 of 19 pairs) had FCO of at least one agent close to one or to zero (both cases indicate a fixed color selection). Five pairs coordinated on a fixed color favoring one agent (blue underlay). Of the four pairs that did not coordinate successfully, in two pairs both agents selected only their own preferred color (red underlay). In the remaining two pairs, one agent selected the non-preferred color while the other switched between the two colors or selected one side. Based on the debriefing responses (Appendix 1, Supplementary file 5), most subjects from the turn-taking dyads (17/20, 85%) realized the coordination benefit, as compared to only half of non-turn-takers (9/18, 50%; Fisher’s exact test p=0.035). In addition, 14 of 20 turn-takers (70%) understood the payoff structure, compared to only 1 of 18 non-turn-takers (6%; Fisher’s exact test p=0.00005).

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All but three pairs achieved an average joint reward above the chance level (2.5 units), and about half the pairs were close to the optimum for the dyad (3.5 units) (Figure 2C). Note that turn-taking is not maximizing average joint reward over coordination on a fixed color (in fact, out of the three pairs reaching the maximum average joint reward of 3.5, only one did so by perfect turn-taking, while the other two used static coordination); it does, however, equalize the rewards within a pair. Over all pairs, the average joint reward increased significantly in the course of the session (Figure 2—figure supplement 1A).

To characterize the dynamics of coordinated behavior, we derived a measure of the dynamic coordination reward (DCR). The DCR describes the average reward a pair earned above (or below) the reward expected for independent random choices, given the observed color choice frequencies per agent. Zero DCR indicates coordination by chance, values significantly different from zero indicate above chance levels of dynamic coordination. The DCR values (Figure 2D) and the fairly balanced average rewards (Figure 2—figure supplement 2B) suggest that 10 of 19 human pairs converged on a form of turn-taking, or reciprocal coordination. These are the same 10 pairs that showed roughly balanced FCO values around 0.5 (teal underlay; Figure 2A).

Comparison of behavior between species

To better understand what drives high dynamic coordination in humans (Figure 2D), we calculated the mutual information (MI) between the sequences of target color choice (MI target) and side choice (MI side) of both agents. These measures indicate how well one agent’s choice of color or side can be predicted from the other’s choice. Comparing the DCR and the MI measures revealed that high DCR values coincided with non-zero values of both MI side and MI color, indicating that dynamic coordination corresponds to both correlated side and correlated color choices between the agents (Figure 2—figure supplement 2D and E).

In contrast to humans, monkeys showed either high values of MI color or MI side indicating that the resulting coordination was not achieved by dynamic turn-taking (Figure 2D). Instead, the four pairs in the middle of Figure 2B (green underlay) that had roughly equal FCO and appeared to be similar to the human turn-takers achieved the coordination by consistently selecting the same side regardless of the target color, which was randomized by the task (Materials and methods).

To directly compare the strategies of human and macaque pairs, we plotted the mutual information values for the side and target color choices of each pair (Figure 3). The distance of each pair’s location to the origin denotes the strength of (anti)coordination. Locations close to the x-axis denote static side strategies; locations close to the y-axis denote static target color strategies. Ten of 19 human pairs were close to the main diagonal, corresponding to similar values of MI side and MI color. These locations denote trial-by-trial or block-wise turn-taking – a signature of dynamic coordination. In contrast to humans, nine macaque pairs showed simple static side-based (four pairs) or single target color-based (five pairs) coordination, with different levels of coordination strength, but did not employ dynamic coordination.

Figure 3

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We next asked whether a difference in action times between the two agents in a pair influenced the coordination by affecting the visibility of each other’s actions. Remarkably, all five macaque pairs coordinating on the fixed color converged on the faster monkey’s preferred color (Supplementary file 2). Likewise, for the static side coordination, all three (out of four) pairs where there was a significant difference in action times between the two agents converged on the faster monkey’s less effortful side closer to the acting arm (Supplementary file 2). In humans, such static Leader-Follower relationship also transpired in non-turn-taking dyads coordinating on the fixed color (Supplementary file 1), where the faster agent led to its own color and the slower agent followed (e.g., pairs 10 and 14), or the faster agent led to the partner’s color and the partner followed (e.g. pair 18). This implies that the static coordination in macaques and some human dyads was shaped by the faster agent’s preferences.

Macaques paired with a human confederate learn to follow

The absence of dynamic coordination in macaque pairs does not preclude the possibility that a slower agent could have monitored the faster partner trial-by-trial in order to coordinate. Alternatively, the faster agent might have reduced the possible outcomes for the slower agent to two options by consistently selecting a fixed side or color, and the slower agent then found the best of the remaining options without resorting to the trial-by-trial following. To test if macaques can actively monitor and adjust to dynamic choice behavior, we trained two monkeys in the dyadic condition with a human confederate who followed a strict pattern of alternating between the two colors in blocks of approximately 20 trials (‘confederate training’). If the rhesus monkeys were insensitive to partner’s diverse choices, the pattern of the confederate’s choices should not affect the monkey’s behavior.

Before confederate training, both monkeys adopted either a fixed side or fixed color strategy with the macaque partners (Supplementary file 2). When paired with the confederate, both monkeys started with a strong bias for selecting their own preferred color but over time changed to reliable coordination (Figure 4A, B, and Figure 4—figure supplement 1A, B for another monkey). Their action times (Figure 4 and Figure 4—figure supplement 1, right columns) were initially faster than those of the confederate, and did not vary strongly depending on the confederate’s actions. In later sessions, the animals reacted considerably slower in those blocks in which the confederate was selecting the animals’ non-preferred color, as if waiting for the confederate to commit to an action. We therefore labeled this as ‘following’ behavior.

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The aggregate measures of choice behavior and coordination development over multiple confederate-paired sessions showed similarities in monkeys’ learning (Figure 4—figure supplement 2). Both animals reached close to 0.8 DCR units, similar to the values reached by humans employing turn-taking (Figure 2C). These results indicate that macaques can take information from their partner into consideration when making decisions in the BoS paradigm. Action time dynamics suggested that the macaques started to actively monitor the confederate’s actions and only selected their non-preferred color if the partner started to reach there first.

Due to the repeating block strategy employed by the human confederate, it is not immediately clear whether the monkeys made use of the action visibility and based their decision on the action observed within a trial, or rather on the reward history. We therefore performed a control experiment in which we placed an opaque barrier on the confederate’s side of the display for roughly the middle third of a session. The barrier blocked the monkey’s view of the confederate hand, while keeping the face visible. In both monkeys, the ‘following’ behavior ceased and they reverted to selecting their own preferred colors irrespective of the confederate’s choices, who – by design – continued the block strategy (Figure 4C, Figure 4—figure supplement 1C). In line with this observation, there was a significant difference in the monkey’s action times when selecting the monkey’s preferred (blue) and the non-preferred (red) color in the transparent condition, but no difference in the opaque condition. In other words, monkeys failed to coordinate when they could not see the confederate’s actions. Note that reverting to mostly own choices in combination with a block-switching confederate resulted in a reward alternating between two and four units, clearly different from the constant reward of two units in solo trials. Hence, the experienced reward schedule and the visible presence of a confederate render the opaque condition distinct from the solo trials. These results imply that the immediate visual information about partner’s actions and not merely monitoring reward history drove and maintained the macaques’ ‘following’ behavior in transparent settings.

Confederate-trained macaque pair shows dynamic coordination

After two macaques had learned to monitor and follow a partner’s actions, we tested how these animals would perform when paired with each other again. At the end of the initial naïve sessions of this pair, and before the training with a human confederate, monkey F (agent A) mostly selected his own preferred color while monkey C (agent B) either selected monkey F’s preferred color or the left side. At the end of the confederate training, both F and C followed the confederate. The question was: would they now express turn-taking behavior or would they fall back to their initial static color or side coordination behavior?

The pair’s coordination behavior changed noticeably after training with a confederate. In the first session, after some initial back and forth, the pair converged on mostly selecting B’s color, with B acting faster (Figure 5A). The most interesting phase was during the second session, in which the animals repeatedly alternated between the two colors in long blocks (Figure 5B). In the third session, the pattern was opposite to the first session, with the pair converging mostly on A’s color who acted faster (Figure 5C). This time, B tried occasionally to switch to his preferred color, and A followed for three short periods. The mutual information measures as well as the DCR values indicate that after confederate training, the coordination strength increased and dynamic coordination resulted in turn-taking behavior in two sessions (Figure 5D, Figure 5—figure supplement 1).

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If action visibility is driving this form of dynamic coordination, then the question of who successfully ‘insists’ on own preference and who ‘accommodates’ the other’s preference should depend on the relative action times of the two agents. Indeed, in the session in which the animals exhibited apparent turn-taking behavior (Figure 5B), the faster animal (Leader) selected the preferred color, while the slower animal (Follower) had to accommodate. Thus, the dynamic coordination arose within a competitive interaction.

Comparison of dynamic coordination between species

At first glance, the turn-taking in the confederate-trained macaques resembled the long turn-taking observed in some human pairs (Figure 5D with Figure 3). Yet, the analysis of action time distributions suggests that macaques employed a competitive version of turn-taking in which the faster agent selects its own color (insists) while the slower agent follows (accommodates). Such behavior would be in line with theoretical predictions derived from our evolutionary simulation (Unakafov et al., 2020), showing that competitive turn-taking provides the most effective strategy for a BoS type game when players have a high probability to observe partner’s choices. To further test the hypothesis that this macaque pair developed a competitive Leader-Follower strategy, we performed a correlation analysis between the modeled visibility of the faster agent’s action by the slower agent and the observed likelihood of following to the faster agent’s color (the faster animal nearly always selected his own color). Figure 6A shows macaque agent A’s probability to select his non-preferred color, the ‘fraction of choosing other’s’ (the inverted fraction of choosing own) and the modeled probability for A to see B’s choice before A made own choice, and the same for macaque agent B (see Materials and methods for details on modeling). In both monkeys, there was a strong correlation between the model and the observation, ranging from 0.92 for smoothed data to 0.64 for unsmoothed data, all significant at p<0.001. This analysis further supports the notion that the confederate-trained macaque pair had developed a competitive turn-taking (alternating Leader-Follower) strategy. Strong positive correlations between action visibility and ‘following’ behavior were present in all six post-confederate-trained sessions, indicating bouts of dynamic coordination (Figure 6—figure supplement 1; Supplementary file 3).

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In contrast, human participants did not show a strong trial-by-trial relationship between relative action time differences and choices (Supplementary file 4). Even in the pair with the strongest positive correlations (pair 12, Figure 6B), the ‘following’ behavior was weak and resulted from a gradual transition from B initially following A (to both colors) to A following B (predominately to B’s color) later in the session, rather than from alternating competitive turn-taking. The lack of such a correlation does not mean that action times and action visibility played no role in human choices. Indeed, in 47% (9 of 19) of human pairs, the absolute difference in action times between the agents significantly differed in coordinated trials compared to uncoordinated trials (Supplementary file 1). Furthermore, turn-taking dyads had a larger absolute action time difference (102.7 ± 38.7 ms, N 10) than non-turn-taking dyads (61.3 ± 21.6 ms, N 9; t-test correcting for unequal variance t(14.4) 2.918; p=0.011); this difference was mostly driven by the coordinated trials (105.0 ± 41.5 ms, N 10 versus 64.7±24.1 ms, N 7; t(14.7) 2.523; p=0.024), while a similar trend in trials without coordination did not reach significance (93.8 ± 29.2 ms, N 10 versus 69.5 ± 39.7 ms, N 9; t(14.6) 1.512; p=0.152). This implies that turn-takers tended to pay more attention to partner’s actions and wait more for each other during the coordination. Hence, similarly to macaques, seeing the faster agent’s choice can help the slower agent to coordinate. But unlike the confederate-trained macaque pair, dynamic human coordination was not driven by the faster agents striving to select only the own preferred color (except human pair 12 shown in Figure 6B): for instance, in the turn-taking pairs 3 and 17, the faster agent B led both to own and A’s preferred colors, while A followed (Supplementary file 1).

The action time analyses show that for the confederate-trained macaques, the visibility of the other’s action before making one’s own decision correlated with the probability to follow the choice of the other, with the faster monkey ‘selfishly’ selecting its own preference. Turn-taking in humans, however, did not rely on temporal competition. To highlight this difference further, we asked how the two species maintained the coordination. Macaques transitioned between periods of coordination using short periods of non-coordination, while turn-taking humans tended to switch seamlessly between the two coordination options without verbal communication (Figure 7—figure supplement 1A and B). Turn-taking humans showed a high and roughly equal amount of benevolent and selfish seamless switches by a faster player (Figure 7A, teal underlay). The four macaque pairs showing the highest number of seamless switches used a side-based strategy, which due to the color-to-side randomization trivially generated seamless color switches. Confederate-trained macaques and non-turn-taking humans showed only very few seamless switches.

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Alternative to seamless switches, agents can end the current coordination block by switching to non-coordination. These trials can be used to initiate or ‘signal’ a switch of coordination. When turn-taking humans showed uncoordinated trials they lay along the unity diagonal, indicating a benevolent intention to initiate the switch to the other’s preferred color in half of these trials (Figure 7B). All macaque pairs were below the unity diagonal, indicating a selfish preference for own color. In the confederate-trained macaques, the coordination epochs were separated by epochs of non-coordination that looked like each agent ‘challenged’ the other to accommodate. Indeed, during challenge initiations and resolutions, the faster macaques tended to act selfishly, while turn-taking humans showed balanced switching behavior (Figure 7—figure supplement 1C and D). The analysis of action times in confederate-trained macaques showed that the faster agent slowed down and the slower agent sped up when transiting to non-coordination, and conversely, the insisting agent sped up to initiate the coordination on the own color and the accommodating agent slowed down (Figure 7—figure supplement 2).

In sum, there were no indications of competitive turn-taking in humans, as the faster agents showed a balanced selection of both coordination options. Furthermore, in four out of the ten turn-taking pairs, one agent was significantly faster than the other in coordinated trials as compared to uncoordinated trials, regardless if it was coordination on its own or the other’s color (Supplementary file 1). This means that such an agent initiated switches to and from the own color; while being able to see the faster agent’s action likely helped the slower agent to maintain coordination and to accomplish seamless switches. In contrast, the confederate-trained macaques showed no seamless switches, but displayed temporal competition in which the faster agent led to his own color.

We studied macaque and human pairs in a coordination game, which offered higher rewards for selecting the same option but entailed an inherent conflict about which of the two coordinated options to select. Both species largely converged on coordinated behavior but in a markedly different fashion. Half of the human pairs achieved nearly optimal coordination and fair (cooperative) turn-taking that equalized the rewards of the two partners, without explicit communication. Macaques, instead, exploited simpler strategies that maximized reward without the need to track the actions of the partner in real-time. That is, they used static instead of dynamic coordination. After two macaques were trained to observe and attend to the partner’s choice with a human confederate, their behavior shifted to competitive turn-taking. In post-confederate-training sessions, the choice behavior was strongly correlated with trial-by-trial differences in the reach action times: the faster monkey chose his preferred option and the slower one followed. Our results show that both humans and macaques make use of action visibility by taking information about the other’s action into account before making their own decisions. There was, however, a fundamental difference: when coordinating dynamically, monkeys showed competitive turn-taking while in humans the faster agent often ‘offered’ switching to the partner’s preferred option, exhibiting a form of benevolent and fair turn-taking.

The datasets used in the current study and the links to the public GitHub code repositories are uploaded to a public Open Science Framework data repository (https://osf.io/f5u8z/).

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Author details

1. Sebastian Moeller

   1. Cognitive Neuroscience Laboratory, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
   2. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0381-6449

2. Anton M Unakafov

   1. Cognitive Neuroscience Laboratory, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
   2. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany
   3. Georg-Elias-Müller-Institute of Psychology, University of Gottingen, Göttingen, Germany
   4. Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
   5. Campus Institute for Dynamics of Biological Networks, Gottingen, Germany

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

3. Julia Fischer

   1. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany
   2. Cognitive Ethology Laboratory, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
   3. Department of Primate Cognition, Johann-Friedrich-Blumenbach Institute for Zoology and Anthropology, University of Gottingen, Göttingen, Germany

   Contribution

   Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5807-0074

4. Alexander Gail

   1. Cognitive Neuroscience Laboratory, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
   2. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany
   3. Georg-Elias-Müller-Institute of Psychology, University of Gottingen, Göttingen, Germany
   4. Bernstein Center for Computational Neuroscience, Göttingen, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   Additional information

   Co-senior authors

   "This ORCID iD identifies the author of this article:" 0000-0002-1165-4646

5. Stefan Treue

   1. Cognitive Neuroscience Laboratory, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
   2. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany
   3. Georg-Elias-Müller-Institute of Psychology, University of Gottingen, Göttingen, Germany
   4. Bernstein Center for Computational Neuroscience, Göttingen, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   Additional information

   Co-senior authors

6. Igor Kagan

   1. Cognitive Neuroscience Laboratory, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
   2. Leibniz ScienceCampus Primate Cognition, Göttingen, Germany

   Contribution

   Conceptualization, Data curation, Supervision, Funding acquisition, Visualization, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   ikagan@dpz.eu

   Competing interests

   No competing interests declared

   Additional information

   Co-senior authors

   "This ORCID iD identifies the author of this article:" 0000-0002-1814-4200

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