Spliceosomal introns are a unique feature of eukaryotic genes. Previous studies have established that many introns were present in the protein-coding genes of the last eukaryotic common ancestor (LECA). Intron positions shared between genes that duplicated before LECA could in principle provide insight into the emergence of the first introns. In this study we use ancestral intron position reconstructions in two large sets of duplicated families to systematically identify these ancient paralogous intron positions. We found that 20–35% of introns inferred to have been present in LECA were shared between paralogs. These shared introns, which likely preceded ancient duplications, were wide spread across different functions, with the notable exception of nuclear transport. Since we observed a clear signal of pervasive intron loss prior to LECA, it is likely that substantially more introns were shared at the time of duplication than we can detect in LECA. The large extent of shared introns indicates an early origin of introns during eukaryogenesis and suggests an early origin of a nuclear structure, before most of the other complex eukaryotic features were established. Analysis of ancestral intron positions in two large sets of duplicated gene families suggests that introns may have originated earlier on in eukaryogenesis, before other complex eukaryotic features were established. Upon transcription, the introns are removed from the pre-mRNA by the spliceosome and the exons are spliced together to form mature mRNA, which is subsequently exported from the nucleus and translated into a functional protein. There are two types of introns; the vast majority of introns is of U2-type2, which are recognised and spliced out by the major spliceosome. U12-type introns are removed by the minor spliceosome and comprise less than a percent of introns in eukaryotic genomes2, with a recently discovered exception of 12% in Physarium polycephalum3; in many species U12-type introns are completely absent4.Protein-coding genes in eukaryotic genomes are characterised by the presence of introns6 and a complex major spliceosome with approximately 80 proteins7. LECA also had U12-type introns and a minor spliceosome8. Eukaryotic evolution after LECA predominantly involved the loss of introns, while only certain lineages including plants and animals experienced net intron gain6.Ancestral reconstructions have revealed that the last eukaryotic common ancestor (LECA) had a genome with a relatively high intron density compared with present-day eukaryotes9. These self-splicing introns can proliferate in the host genome but are rarely present within genes in prokaryotes. The most widely assumed scenario is that the self-splicing introns were introduced in the host genome from the protomitochondrion11 but we previously called other sources possible as well12. The emergence of intragenic introns underlined the importance of a nucleus—the defining feature of eukaryotes—to separate transcription and translation for splicing to take place completely prior to protein synthesis13. Furthermore, the origin of nonsense-mediated decay and the elaboration of ubiquitin signalling are proposed to be defence mechanisms against aberrant transcripts and proteins caused by the spread of introns14.It has been established that spliceosomal introns originated from prokaryotic self-splicing group II introns during the prokaryote-to-eukaryote transition17. Mainly genes involved in establishing and regulating a complex cell and relatively few metabolic genes duplicated during eukaryogenesis17.Eukaryotes are considered more complex than prokaryotes: cells are much larger and contain multiple membrane-bound compartments. Underlying the increase in cellular complexity during the transition to eukaryotes (eukaryogenesis) was an increase in the number of genes caused by gene transfers and gene duplications18. However, a study investigating the evolutionary history of a specific protein family, the spliceosomal Lsm and Sm proteins, found introns shared between multiple pre-LECA paralogs19. This implies that introns had spread through the genome before the duplications resulting in these paralogs took place. It also suggests that more of these shared intron positions could be detected in other duplicated families.Both the numerous gene duplications and the spread of introns through the genome occurred during eukaryogenesis and their interaction could inform the reconstruction of intermediate stages of this still largely unresolved transition. The relation between proto-eukaryotic gene duplications and introns can be researched by identifying positions of introns that are shared between ancient paralogs. An analysis performed on six eukaryotic genomes almost fifteen years ago identified very few shared intron positions that could represent intron insertions predating gene duplication eventsIn this study we utilise the greatly expanded set of eukaryotic genomes currently available to reassess the relation between the emergence of introns and gene duplications during eukaryogenesis. We detected many more shared intron positions than previous estimates. Our findings have implications for the dynamics of intron evolution and the timeline of events during eukaryogenesis.17 . Intron loss occurred frequently throughout eukaryotic evolution, with some lineages losing all introns in our set of genes. Intron gain only had a substantial contribution at certain branches, especially dinoflagellates, which has been described before21. These findings for the dynamics of introns from LECA to present-day eukaryotes are fully consistent with a previous study that also reconstructed intron-rich ancestors6 and with the complexity of the spliceosome in LECA and subsequent simplification in most eukaryotic lineages, as inferred before22.To investigate the interaction between the spread of introns and gene duplications during eukaryogenesis we used sets of proto-eukaryotic duplications inferred by two independent approaches: the Pfam domain trees from a recent study17 Fig.  and the  18 Fig. . Intron 18, which is probably due to a combination of the low number of six available genomes that were used and the frequent loss of introns. The percentage of shared introns was even higher for the Pfams, with 34.8% of the 7524 LECA introns in paralogous OGs being shared.The relatively high number of introns that could be traced back to LECA underlined the potential to find LECA introns that are in the same position in OGs that stem from a proto-eukaryotic duplication, which we refer to as proto-eukaryotic paralogs. For the KOGs, 19.9% of the 19,184 LECA introns considered had an equivalent LECA intron in at least one paralog Fig. . This is23) must have been the result of parallel intron gains. We compiled a set of separately acquired genes for both datasets and obtained percentages of 5.0% and 5.4% shared LECA introns between separate acquisitions for the KOGs and Pfams, respectively. Notwithstanding the influence of incorrect OG assignment inflating the estimated number of LECA introns shared between separate acquisitions ), this shows that parallel intron gain is a real phenomenon , P = 3.1 × 10−220 (Pfams)). Another potential explanation for shared introns is transfer of introns between paralogs due to gene conversion24. However, this scenario cannot account for the frequent presence of multiple LECA introns in the same position between multiple paralogs  and shared LECA introns were even more biased towards the start of the gene  LECA introns and post-LECA introns with respect to the relative position of the introns in the gene. Whereas the relative positions of post-LECA introns showed a fairly uniform distribution, unique LECA introns were more at the 5′ end of the gene  statistic = 0.034, χ2 = 966, df = 4, P = 8.1 × 10−208 (KOGs); χ2 = 182, df = 4, P = 2.4 × 10−38 (Pfams); Supplementary Tables We also compared the phases of the introns, which refers to the three possible positions of an intron in a codon. The phase distribution of the three different categories of introns was also different . Pfam clades inferred to have been a proto-eukaryotic invention or with an Asgard archaeal or diverse prokaryotic sister group in the phylogenetic tree had the highest fraction of duplications with introns . An additional explanation could be that Pfam domains are more conserved. Consequently, it would be more likely for an intron to be paired with an intron in a paralog. Notwithstanding the subtle differences between the two datasets, both revealed consistent findings.27 in a certain phase. A biased loss could also explain the phase bias28. Another plausible explanation that has been put forward is that the initial distribution was uniform and that the eventual phase bias was due to a combination of massive U12-type intron loss and the directed conversion of phase 0 U12-type to U2-type introns2. The phase bias of recent U2-type intron gains in the dinoflagellate lineage and recent U12-type intron gains in Physarium lends support to the protosplice site model in at least these eukaryotic lineages21. The similar phase bias of pre-LECA gains and losses . Other families seem conspicuously devoid of shared introns . The conservation of introns upon duplication challenges the main role of retrotransposition in creating proto-eukaryotic paralogs, which results in intronless paralogs and was proposed based on the initial lack of detected shared introns16. The protomitochondrion has been widely considered to be the source of introns, even though direct phylogenetic evidence is lacking12. Based on the analysis of shared introns, the close integration of the endosymbiont within the host by means of mitochondrial transport seemed to have occurred after substantial spread of introns. Although the symbiosis must have started before the close integration, this observation combined with the inferred timing from our branch lengths analysis is not easy to reconcile with the hypothesis that spliceosomal introns originated from mitochondrial self-splicing group II introns. The self-splicing introns could have come from another lineage instead.A notable exception to the described pattern of shared introns between most categories is the low number of duplications with shared introns in alphaproteobacterial acquisitions, which were very likely present in the protomitochondrion. It is tempting to speculate that these duplications were due to another mechanism; for example, they may have been the result of serial endosymbiotic gene transfersOur analysis was to the best of our knowledge the second large-scale investigation on the association between introns and proto-eukaryotic duplications, yet it was the first to encounter a large-scale occurrence of introns shared between proto-eukaryotic paralogs. Besides the potential implications on the order and causality of events during eukaryogenesis, the strong association between proto-eukaryotic duplications and introns also sheds unique light on the origin and evolution of intron phases and positional biases as well as the discussion on the emergence of U2- and U12-type introns 31 using hidden Markov model profile searches30. Sverdlov et al.18 used the homologous clusters of eukaryotic orthologous groups (KOGs) and candidate orthologous groups (TWOGs) from Makarova et al.15. KOGs are included in the euNOGs and we used the euNOG corresponding to a TWOG, if present, as determined in Vosseberg et al.17. Both types of euNOGs are referred to as “KOG” in the main text. We detected a few differences between the Makarova et al. and Sverdlov et al. clusters and chose one clustering over the other on a case-by-case basis after manual inspection  proteomes, as compiled for a previous study32. In short, we selected eukaryotic sequences based on best bidirectional hits between Opimoda and Diphoda for tree inference and supplemented these with prokaryotic sequences. In the resulting phylogenetic trees, acquisition, duplication and LECA nodes were inferred. The tree sequences belonging to a LECA node were complemented with the eukaryotic sequences that had one of these tree sequences as their best BLAST33 hit, resulting in an OG. Sequences from species that are not in the set of 167 species and human sequences that are not in the primary assembly were removed from the OG. If there was only one OG for a Pfam, it was not included.We also used the Pfam LECA families and duplications that we published recently34, with the exception of Homo sapiens, whose genome was replaced with the corresponding primary assembly (ftp://ftp.ensembl.org/pub/release-87/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz), and Stentor coereleus, for which we used the file from NCBI  to be able to match the sequence file with the genome features file.For predicting the type of introns, genome sequence files of the species in our set were obtained using the links in Supplementary Table 35. Each KOG was aligned separately using the E-INS-i algorithm and the resulting KOG alignments for a cluster of KOGs were merged into a single alignment (merge option with E-INS-i). If this was not feasible due to memory issues, the alignment was made with the FFT-NS-i option, or FFT-NS-2 if that was also not feasible. Each Pfam OG was aligned separately, followed by a merged alignment of all OGs per Pfam, both with the L-INS-i algorithm. For PF00001 and PF00069, alignments had to be performed with the FFT-NS-i option.All multiple sequence alignments were performed with MAFFT v7.310We downloaded the genome annotation files from 156 species from our set that we could extract intron information from  or the acquisition of already duplicated genes. Shared intron positions in these had to be the result of parallel intron insertions. To identify these, we used the phylogenetic trees of these clusters that we inferred before32. If the sequences corresponding to different KOGs were in separate acquisitions and none of the acquisitions in the tree had another acquisition in the inferred sister group, the intron positions in these separately acquired KOGs were compared and shared introns were identified. Introns that were only shared between separate acquisitions were not included in the shared introns analysis. All introns of KOGs that were acquired separately from all other KOGs in the cluster were not used for calculating the fraction of shared introns.The different KOGs in the KOG clusters do not all represent gene duplications during eukaryogenesis; some were acquired as separate genes32.For the Pfams, separate acquisitions were identified based on phylogenetic trees as well using the same approach. For each duplication node in the trees, the intron positions that were present before the duplication event were inferred from the LECA introns using a Dollo parsimony approach. The inferred sister groups of acquisitions and the functional annotation and duplication length information were extracted from Vosseberg et al.39 (command used: cmscan -nohmmonly -rfam -cut_ga) with the spliceosomal snRNA Rfam 14.240 covariance models RF00003, RF00004, RF00007, RF00015, RF00020, RF00026, RF00488, RF00548, RF00618, RF00619, RF02491, RF02492, RF02493 and RF02494. Introns from species for which none of the snRNA genes specific for the minor spliceosome  were detected in the genome were annotated as U2. Intron types from the remaining 70 species were predicted with intronIC v1.0.11 + 2.gf7ac7be2, using all isoforms if needed. Intron positions that were predicted as U12 in at least three species were annotated as U12-type introns.Spliceosomal snRNA genes were searched for in the genomes using Infernal v1.1.2χ2 contingency table tests or Fisher’s exact tests (in case of 2 × 2 tables). When testing the overrepresentation of functional categories, KOGs with multiple categories spanning the three main groups  were excluded for comparisons between these groups. The numbers of unique LECA introns and shared LECA introns or duplications with and without introns traced back to the pre-duplication state were compared between different functions, cellular localisations and phylogenetic origins. Differences in relative position and branch lengths were assessed with Kolmogorov–Smirnov tests. All performed tests were two-sided and P values from multiple comparisons were adjusted for the false discovery rate. Statistical analyses were performed in Python using NumPy v1.21.141, pandas v1.3.142, SciPy v1.7.043 and statmodels v0.11.244. Figures were created with Matplotlib v3.4.245, seaborn v0.11.146, ETE v3.1.147 and Jalview v2.11.1.448.Associations between two categorical variables were tested with Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary


Many real-world decisions in social contexts are made while observing a partner’s actions. To study dynamic interactions during such decisions, we developed a setup where two agents seated face-to-face to engage in game-theoretical tasks on a shared transparent touchscreen display (‘transparent games’). We compared human and macaque pairs in a transparent version of the coordination game ‘Bach-or-Stravinsky’, which entails a conflict about which of two individually-preferred opposing options to choose to achieve coordination. Most human pairs developed coordinated behavior and adopted dynamic turn-taking to equalize the payoffs. All macaque pairs converged on simpler, static coordination. Remarkably, two animals learned to coordinate dynamically after training with a human confederate. This pair selected the faster agent’s preferred option, exhibiting turn-taking behavior that was captured by modeling the visibility of the partner’s action before one’s own movement. Such competitive turn-taking was unlike the prosocial turn-taking in humans, who equally often initiated switches to and from their preferred option. Thus, the dynamic coordination is not restricted to humans but can occur on the background of different social attitudes and cognitive capacities in rhesus monkeys. Overall, our results illustrate how action visibility promotes the emergence and maintenance of coordination when agents can observe and time their mutual actions. 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  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 . 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 . To adjuHomo 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 , creating a dilemma between the selfish temptation to defect and a riskier cooperation that requires trust. Humans often show a bias towards cooperative behavior  . These aeither of two options is known as Battle of the Sexes, or Bach-or-Stravinsky game (BoS)  or anti-coordinate on opposite options (Conflict game). A third, less studied type of game that emphasizes the coordination of me (BoS) . Each agme (BoS) . Interesme (BoS) . This resimultaneously’ (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  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 . Here, wTo facilitate inter-species comparability, we paired human or macaque agents without explicit task instructions. We expected that the transparency and instantaneous coaction  would faTwo human or macaque agents sat face-to-face and performed a task on a dyadic interaction platform using a shared transparent vertical workspace . Agents 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 , and about half the pairs were close to the optimum for the dyad (3.5 units) . Note thdynamic 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 , before being paired with a conspecific with the opposite target color preference. We then collected the data from nine macaque pairs for multiple sessions to assess on which strategy they converged and how their behavior evolved over time. In the late dyadic sessions, all pairs converged on coordination, in one of two distinct ways: either by converging on the same color target .Across all pairs, the average joint reward increased from early to late sessions , and in 8 of 9 pairs, the proportion of coordinated trials increased significantly  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 . 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., and Before confederate training, both monkeys adopted either a fixed side or fixed color strategy with the macaque partners . When paFigure 4A, BThe aggregate measures of choice behavior and coordination development over multiple confederate-paired sessions showed similarities in monkeys’ learning . Both anDue 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 . In lineAfter 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 . The mosIf 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 , the fasAt first glance, the turn-taking in the confederate-trained macaques resembled the long turn-taking observed in some human pairs . Yet, thIn contrast, human participants did not show a strong trial-by-trial relationship between relative action time differences and choices . Even inThe 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 . Turn-taAlternative 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 . All macIn 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 . This meWe 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.Ten of the 19 human pairs converged on dynamic coordination that balanced the reward, developing fair, cooperative turn-taking, while another five pairs coordinated on the same fixed color throughout the session. Similar to the approach of Brosnan and colleagues , the humWhat sets the turn-takers apart from the dyads that coordinated on a fixed color, or did not coordinate well? Based on their debriefing responses, turn-takers displayed a much better understanding of the implicit reward rules such as the value of coordination and unfairness. The non-turn-taker group was more diverse: some subjects did not understand the game or only understood some aspects; other subjects had a better understanding but were selecting selfishly or could only follow the partner. Furthermore, the turn-taking dyads utilized action visibility driven by action time differences more extensively, although some non-turn-taking dyads overall also exploited action visibility to coordinate on a fixed color.In contrast to humans, macaque pairs converged on simpler strategies, either selecting the same color (56%) or selecting the same side (44%). These two strategies require less cognitive resources and trial-by-trial coordination effort than turn-taking, because they allow each agent to make decisions without paying attention to the trial-specific actions of the partner. Although we found that all macaque pairs that exhibited a significant difference in action times between the agents (8 of 9 pairs) converged on the faster agent’s preferred choice, it seems that initially, macaques did not rely on trial-by-trial action visibility. This is suggested by the fact that it required relatively long training before macaques started to follow a human confederate dynamically. Instead, we speculate that slower macaques adapted to their partner’s static choice by maximizing their reward over the remaining options.However, all but one animal coordinated both via fixed color or fixed side selection depending on the specific partner, and no animal persisted on the own color with all tested partners. For instance, monkey F insisted on his preferred color when playing with monkey C, accommodated to the other’s color when playing with monkey T, and converged on his right side when playing with monkey M . This reWhile our study is, to the best of our knowledge, the first implementation of a BoS paradigm in macaques, other 2×2 games have been investigated in several primate species . So far, the data suggest that monkeys, unlike humans, do not spontaneously adopt turn-taking, despite coordinated reciprocity being a successful evolutionary strategy in simulated non-communicating agents . For insThe lack of spontaneous dynamic coordination such as turn-taking in naïve macaques in our experiment cannot be attributed to their inability to infer the upcoming choices of the partner, because they could observe the partner’s actions through the transparent display. Especially for nonhuman species, coordinating based on mutual choice history might be more demanding than coordinating using the immediately observable behavior of others. Coordination (or anti-coordination) typically is improved with increasing information about the other’s choice by shifting from the strictly simultaneous to the sequential mode . The traIn the studies of Brosnan and colleagues, subjects looked at the same split monitor while sitting next to each other, and could observe each other’s decisions as cursor movements. In our setup, agents sat opposite to each other and saw actual eye, head and hand movements, combining face-to-face and action visibility. In humans, face-to-face visibility in a simultaneous iterated Prisoner’s Dilemma (iPD) and in the Ultimatum game significantly improves mutual cooperation, even without action visibility . These fThe importance of action visibility is also supported by computational modeling. In the continuous-time cooperation games such as iPD and Stag Hunt where agents could observe and respond to each other’s actions in real-time, cooperation by coaction is more easily obtained and stabilized against exploitation than the cooperation that relies on delayed reciprocity . For theGiven that many human pairs exhibited spontaneous dynamic coordination, while naïve macaques did not, we can ask if and how these two species utilized the action visibility. Both species exploited action visibility to achieve and/or maintain the turn-taking, emphasizing the importance of the ‘transparency’ of interactive behavior as an important determinant of emerging strategies. Humans relied on action visibility for seamless switches from one color to another, and for maintaining coordination within a block of trials. But besides one pair, there was no indication that humans employed the competitive variant of a Leader-Follower strategy. Instead, faster agents were as likely to switch from their own to the other’s preferred color, indicating benevolent turn-taking. These results add to the body of literature indicating that humans in a social setting might base their decisions not only on pure reward maximization . Such noVery different dynamics transpired in additional experiments with macaques. Pairing them with a human confederate who consistently alternated between colors in short blocks but did not intentionally provide any additional social cues demonstrated that macaques are capable of closely follow partner’s actions , and adoNotably, we considered the interactions in the pairs where one monkey underwent the confederate training, and other did not . In all Species differences in general cooperative behavior may explain both the reluctance of naïve macaques to coordinate dynamically, as well as the competitive nature of dynamic interactions when they occur. The transparent BoS game requires the agents to cooperate in close proximity in the pursuit of an immediate reward, which necessitates some degree of social tolerance by both partners. If we take natural food-sharing behavior as a proxy for social tolerance, it is not equally prevalent among primate species. Humans, apes, some baboon species , and somBeyond social attitudes, the lack of the cooperative turn-taking in nonhuman primates might reflect cognitive limitations in long-term planning and perspective-taking . In humaThere are several limitations in our study that have to be considered and possibly addressed in future experiments. First, we only tested six macaques (and nine pairs), all males, and only 1 pair was housed together (others were from neighboring but separate enclosures). Therefore, we cannot say much about the influence of social rank. Anecdotally, however, it did not seem that the more dominant agent always prevailed. For instance, one animal (monkey T), the smallest and likely to be subordinate to all other partners, successfully ‘insisted’ on his own color in two out of three cases . FurtherSecond, due to the task target color/side randomization, selecting targets on the same side of the display resulted in low effort, efficient and fair coordination. We cannot say if fairness was a contributing factor in convergence of the four macaque pairs to this strategy, although we deem it highly unlikely. A new experiment where the probability of a specific color to appear on the same side is parametrically modulated is needed to evaluate the conditions under which this strategy emerges (or what level of unfairness is accepted). Furthermore, we prompted the human participants to pay attention to the partner’s choices, to facilitate the emergence of coordination within a single session. It would be interesting to test human pairs in several sessions and without any instructions, similar to macaques, to see if humans might also converge on the fixed side strategy as means of fair and seamless coordination. It requires preventing the human participants from any verbal contact after and between the sessions, however.Third, it would be important to vary the ratio and the range of reward magnitude (‘stakes’), as it has been shown that stakes may strongly affect the individual  and sociFinally, it would be very interesting to relate diverse in-game behavioral strategies observed in the human pairs to individual differences in cognitive and socio-economic personality traits , and linIn summary, we show that both, humans and nonhuman primates, dynamically coordinate in real-time transparent interactions, but macaques compete while humans strive for fairness by prosocial turn-taking. Enabling action visibility with our transparent setup provides novel comparative insights into social decision processes as they unfold in real-time during transparent interactions, and offers a new route to behavioral and neural investigations of dynamic decision-making in cooperative and competitive contexts .38 right-handed subjects  participated in the study as paid volunteers. Subjects were tested as 19 unique pairs, i.e., each participant contributed only once. Instructions given to the subjects before the experiment are provided in Appendix 1 . In shorResearch with nonhuman primates represents a small but indispensable component of neuroscience research. The scientists in this study are committed to the responsibility they have in ensuring the best possible science with the least possible harm to the animals . The expSix adult male rhesus monkeys  participated in the study, yielding nine pairs (each monkey participated in two or more pairs). Animals were extensively trained with positive reinforcement to climb into and stay seated in a primate chair. The animals were pair- or group-housed in facilities of the German Primate Center (DPZ) in accordance with all applicable German and European regulations. The facility provides the animals with an enriched environment, including a multitude of toys and wooden structures , naturalFor maximal comparability of the human and the monkey behavior, we developed a novel dyadic interaction platform in which two human or nonhuman primate subjects co-act in a shared workspace while sitting face-to-face . Joint dSince we are interested in the effect of mutual action visibility on coordination behavior, we implemented a transparent version of a Bach-or-Stravinsky (BoS) game, in which each player’s time-continuous visuomotor behavior can be seen by the other player. Conceptually, in the BoS game two agents are choosing between going to Bach or Stravinsky concerts. Agent A prefers Bach, agent B prefers Stravinsky; yet, both prefer going to the concert together . Thus, aIn our implementation, the agents were choosing between two options represented by two differently colored targets placed on the left and the right side of the display. Each target color was associated with a higher reward for one of the agents resulting in individually preferred targets. This reward schedule, 2 reward units for one color and 1 unit for the other color, was trained in ‘solo’ trials to establish an ‘own’ color preference in each subject. In dyadic trials, an agent selecting own preferred target was assured to get at least 2 reward units, while selecting the other’s target  yielded at least 1 unit. Additionally, when both agents selected the same target, a bonus of 2 reward units was added to the payoff of each agent. Thus, the maximum average joint reward (3.5 units) was obtained on coordinated trials when one agent selected own preferred color (getting 4 units) while the other agent chose non-preferred color (getting 3 units). Note that on any given coordinated trial, i.e., when agents selected the same target, this payoff matrix resulted in an unequal  reward distribution . Hence, On every trial, each subject chose between the individually-preferred and non-preferred target on the touchscreen. Targets were light blue circles of 35 mm diameter with either a red  or a yellow  rings .Subjects had to place both hands on the two home buttons for 500 ms to start a trial after the inter-trial interval (ITI). This allowed us to control which effector (acting hand) was used . Then anThe side of the targets was randomized on each trial, i.e., in ~50% the red target was on the right, the blue on the left, and vice versa. This design can lead to the three following coordination patterns (or any mix of the three):Coordinating statically by both agents repeatedly selecting the same fixed color of the target, irrespective of unfair distribution of the rewards.Coordinating statically by both agents repeatedly selecting the same fixed side. Due to color/side randomization, this pattern ‘automatically’ ensures a fair reward distribution.Coordinating dynamically by both agents selecting the same target, while picking from both colors and both sides trial-by-trial . This could result in a fair or unfair reward distribution depending on the ratio of red to blue color selection.Subjects were not informed about the randomization procedure, so recognizing the ‘automatic’ fairness of the 2nd pattern would have required keeping a count of color occurrences per side from both subjects.Human subjects were recruited via a university job website and pairs were selected based on matching schedules. Subjects were given a brief introduction to the experiment and a set of verbal instructions on the task mechanics . Dyads containing monkeys  were instructed to use the left hand. The animals performed the solo version of the task with differential rewards to develop a preference for one of the color targets and were only paired with a conspecific after selecting a higher rewarded target in ≥75% of the trials. Thereafter, pairs of macaques worked together in the dyadic version of the task, for 11 ± 7 sessions (range 4–25). After the dyadic task, we offered monkeys the opportunity to obtain additional liquid in the solo version of the task for as long as they continued to work , essentially making participation in the dyadic trials optional.aggregate measures of choice behavior and coordination, explained below: fraction of choosing own color, fraction of choosing (objective) left side, mutual information for color choice, mutual information for side choice, average reward, and DCR. Unless noted otherwise, we report these measures for the last 200 trials of each session in order to assess the ‘steady-state’ behavior after allowing for an initial period of exploration.We computed the following six w=8 trials. In the latter case, FCO and FCL can take the values of 0, 1/8, 2/8, …, 1. For instance, FCO = 0 means that an agent has selected individually non-preferred target for eight trials in a row.The FCO (fraction of choosing own) is the fraction of trials (within a certain window) where an agent has selected the individually preferred color (red for agent A and blue for agent B). Similarly, the FCL (fraction of choosing left) is a fraction of trials where an agent has selected the target on the ‘objective’ left side of the display (which is the left side for agent A and right side for agent B). Fractions range from 0 to 1. For figures showing FCO/FCL over the course of a session, we also calculated both measures for the session as a whole, and in running windows of Mutual information (MI) represents the reduction of uncertainty regarding the values of one time series provided by knowing the values of the other time series. Here, we consider mutual information for the color of the target (MI color) and side (MI side) choices of the two agents, showing how much information the target/side choices of one agent provide about the respective choices of the other. Mutual information is measured in bits. Since both target and side choices are binary , both MI color and MI side range from 0 bit (the choices of one agent provide no information about the choices of the other) to 1 bit (the choices of one agent can be inferred precisely from the choices of the other agent).For instance, if both agents select the objective left target in every odd trial and the objective right target in every even trial, both MI color = MI side = 1, since for every trial the target and the side selected by one agent entirely describe the target/side selected by the other. At the same time, the choices of each agent individually are highly uncertain: both sides and both targets are selected with the same probability of 0.5. If both agents constantly select the objective left side, this would result in MI color = 1 due to side-randomization of target color, but MI side = 0, since there is no uncertainty regarding the side selection and thus no additional knowledge about the other’s choice can reduce the uncertainty.Formally, mutual information of time series x in time series y in time series X and Y, respectively, and x and y can be either 0 or 1, so that the sum is over all four combinations. Since in our case time series X and Y have finite length, we simply replace probabilities with relative frequencies. This is known as a naïve estimation of mutual information, but it is sufficiently precise for binary time series  .Average reward (AR) is computed as the average of an agent’s payoff across the session. Note that the reward of an individual agent can be in the range of 1–4 units, while the joint reward of a pair ([reward A+reward B]/2) cannot exceed 3.5 . The average reward for completely random independent choices of two agents with 50% probability for either target is 2.5 (but note that an achieved reward of 2.5 is not a positive proof of independent choices).playing randomly we mean that the choices of the agents in each round are independent of the history and of the current choices of the partner. The ‘random reward’ is computed by selecting the two color targets with the same probabilities as actually observed in the two agents, but randomly permuting the choices over trials. For our payoff matrix, the range of DCR is , with –1 corresponding to very inefficient playing , while 1 corresponds to very efficient playing with explicit coordination . DCR is hence a measure of dynamic  coordination. For instance, if both agents would coordinate statically by constantly selecting one and the same side, this would result in DCR is the surplus reward of the two agents compared to the reward they would get by playing randomly. By Ractual) minus the reward the agents would get if they were playing randomly , we use the fact that For the main analysis presented in the Results, we used last 200 trials to compute DCR values. Using the last 150 or 250 trials for DCR analysis resulted in the exact same 10 human pairs with significant results as using the last 200.We measured the reaction time from the onset of the choice targets to the release time of the initial fixation target, We use To compare action times (AT) between agents we performed three tests. First, we compared the action times over all trials between both agents of a pair to assess stable differences that might indicate temporal leader-follower relations. Second, we compared per-trial difference in action times of the two agents (AT A – AT B) between coordinated trials on A’s preferred color and coordinated trials on B’s preferred color. Third, we compared the absolute per-trial difference in action times of the two agents between coordinated and uncoordinated trials. The absolute per-trial difference is equivalent to subtracting for each individual trial the action time of the faster agent  from the action time of the slower partner. All comparisons used a two-sample t-test using Satterthwaite’s approximation to allow for unequal variance.When an agent is acting slower than the partner, there is a chance for this agent to see the partner’s choice and use this information to make the own choice. We therefore modeled the probability to see the partner’s choice as a logistic function of the difference of the agents' action times, in each trial. The logistic function with ’s color . We also This study investigates and compares spontaneous turn-taking behavior in pairs of macaque monkeys and human participants. The study is well-designed and uses a novel format for dynamic interaction. The analyses are rigorous and support the overall conclusion that there are differences between species in their tendencies toward cooperative, mutually beneficial behaviors, with humans exhibiting more prosocial tendencies. This finding, as well as the rich description of pair interactions in each species, is likely to be relevant to a broad range of researchers interested in social behavior. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Joshua Gold as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Steve W C Chang (Reviewer #2).Thank you for submitting your article "Human and macaque pairs employ different coordination strategies in a transparent decision game" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. Full comments by each of the reviewers are appended to this summary for your reference.Essential revisions:1. Some human pairs  did not employ a turn-taking strategy. The authors should discuss potential reasons for these differences, for instance, whether some subjects were not as socially engaged or were less flexible when choosing different options. It would be most helpful if the authors are able to perform additional data analyses to characterize the differences between the two groups of humans  to examine why the turn-taking and non-turn-taking pairs differed.2. The authors should further consider the degree to which maximizing one's own rewards could drive different strategies in the monkeys. Although cooperating maximized the group's rewards, it wasn't clear whether one monkey could do even better by behaving competitively.3. It was felt that the manuscript would benefit from including additional information on the monkeys' interactions with the human confederate. Specifically, how did their behavior differ with humans compared to other monkeys, how long do effects of training last, and would the behavior of a trained monkey be expected to translate to an untrained monkey?4. A notable limitation of the study's ability to compare across species is that the rewards given to the monkeys and humans differed qualitatively (primary vs. secondary rewards). Although this is touched on in the discussion, it was felt that further consideration is warranted. For instance, to what extent might motivation to obtain each outcome drive the tendency to be 'selfish' versus cooperative? Is there any evidence that monkeys would adopt different strategies if they were more sated?5. Can the authors more precisely consider the role of action visibility? Specifically, whether visibility influences social cognition or aides sensorimotor coordination.6. Finally, can the authors provide further discussion of the effects of learning and socialization on this task across species?Reviewer #1 (Recommendations for the authors):1. It would be helpful to know more about how the goal of maximizing reward might impact the behaviors described here. To start, how does turn-taking compare with the simpler strategies used by the monkeys in terms of reward received? Figure 2C seems to show that the average reward earned by two subjects was actually quite similar between monkeys and humans. The highest-earning human pairs (presumably those who were turn-taking) might have earned slightly more than the monkeys, but there doesn't appear to be a big advantage to turn-taking, in terms of simple reward maximization. Is this true, and if so, is it possible to interpret their behavior as arising from other advantages ? What about rewards received by each individual and not the average across pairs? If an agent only values rewards received by themself, is there a more or less optimal approach to this task?2. Related to the above, it is notable that rewards given to the monkeys and humans differed qualitatively, as did their motivation to pursue them. The humans were receiving small amounts of money in a fairly abstract arrangement in which money was obtained at the end of the session, whereas the monkeys were thirsty and received fluid rewards continuously depending on trial outcomes. To what extent is the motivation to be 'selfish' versus cooperative driven by the motivation to obtain each outcome? For instance, if monkeys were not water deprived, would they be more likely to cooperate rather than compete? Is there any evidence that motivation to get the juice reward changed toward the end of the session?3. Are there action time plots similar to those shown for the monkeys for the human pairs? Although Figure 6B is compelling, if I understand it correctly, it just shows that the humans' choices can't be predicted based on who responded first. But it's hard to understand, then, how this type of coordination would emerge if one participant didn't watch and follow the other. The results in 6B could occur if the person who responds faster also drives the shifts to the other's target from time to time, as suggested in Figure 7. Therefore, is it possible that there is a leader and follower in the human pairs (perhaps these roles switch off), but the leader just doesn't choose their own color reliably?Reviewer #2 (Recommendations for the authors):This manuscript is nicely organized and very thorough. However, it could be improved even further if some points can be discussed in more detail or with additional data analyses.1. Related to Figure 2D: the authors mentioned that some human pairs  were not employing the turn-taking strategy. From this figure, these pairs were actually not a small number (9 out of 19). The authors should discuss what might be the reason behind this. Is it possible that these subjects were not very socially engaged  or were less flexible when choosing different options ? It will be better if the authors could perform additional data analyses to characterize the differences between the two groups of humans  to examine why they were different from the turn-taking pairs and more similar to the monkey pairs.2. Related to Figure 5: It is still a little puzzling that monkeys' behaviors can be improved after being trained with humans. What was the reason for this change? Did the human subjects provide extra social cues to the monkeys in order to encourage them to pay more attention, or did the monkeys learn turn-taking simply from the diverse choice patterns of the human subjects? The authors need to have further discussion about this point.3. It is not very clear whether the action visibility is influencing social cognition or simply aiding sensorimotor coordination. The authors should discuss this further in Discussion. Essential revisions:1. Some human pairs  did not employ a turn-taking strategy. The authors should discuss potential reasons for these differences, for instance, whether some subjects were not as socially engaged or were less flexible when choosing different options. It would be most helpful if the authors are able to perform additional data analyses to characterize the differences between the two groups of humans  to examine why the turn-taking and non-turn-taking pairs differed.This point is related to the questions raised by Reviewer 1, Recommendations to the authors Comment 3  and Reviewer 2, Recommendations to the authors Comment 1 .We thank the Reviewers for bringing up this question. First, we performed additional analysis of action times as well as absolute action time differences between the two agents – i.e. mean(|ATA – ATB|) – to compare the two groups: turn-takers and non turn-takers. There was no significant difference in mean action times  577.3 SD 80.7 N 20; non turn-takers M 536.1 SD 62.9 N 18; t(35.3) 1.766; p 0.086). Therefore, both groups appear to have a similar engagement in the task. However, when comparing means of the absolute trial-by-trial action time differences between the two agents, turn-taking dyads had a larger difference than non turn-taking dyads. We added this information to Results (lines 416-422) and the revised Table S1 (Supplementary file 1) (columns 11-17):“Furthermore, turn-taking dyads had a larger absolute action time difference  than non turn-taking dyads  2.918; p = 0.011); this difference was mostly driven by the coordinated trials  2.523; p = 0.024), while a similar trend in trials without coordination did not reach significance  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.”We emphasize however that the leader-follower dynamics also transpired in non-turn-taking dyads and in macaques statically coordinating on a fixed color or side. We now address this in lines 254-263:“We next asked whether a difference in action times between the two agents in a pair influenced the coordination by affecting visibility of each other’s actions. Remarkably, all 5 macaque pairs coordinating on the fixed color converged on the faster monkey’s preferred color . Likewise, for the static side coordination, all 3 (out of 4) 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 . In humans, such static Leader-Follower relationship also transpired in non-turn-taking dyads coordinating on the fixed color , where the faster agent led to 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.”If the mutual action visibility serves as means of coordination, one may expect that the action time differences should be larger when coordination took place. Therefore, we also directly compared absolute AT differences between the two agents in coordinated vs non-coordinated trials . Six of 10 turn-taker dyads showed a significant difference between coordinated and uncoordinated trials, as well as 3 of 9 non turn-taker dyads . In 4 turn-taking dyads but also in 2 non turn-taking dyads the difference was larger for coordinated trials, further suggesting leader-follower dynamics that was not limited to turn-takers.Since our human subjects, like macaques, learned the payoff contingencies while playing the game, we asked several debriefing questions after the experiment . We extracted from these free form reports whether subjects explicitly understood that coordination on “same” targets was beneficial and whether they also realized that the two coordination options were more favorable for one or another player. We now discuss these additional insights in lines 145-148:“Based on the debriefing responses (Supplementary Information), most subjects from the turn-taking dyads  realized the coordination benefit, as compared to only half of non turn-takers . In addition, 14 of 20 turn-takers (70%) understood the payoff structure, compared to only 1 of 18 non turn-takers .”and lines 501-508:“What sets the turn-takers apart from the dyads that coordinated on a fixed color, or did not coordinate well? Based on their debriefing responses, turn-takers displayed a much better understanding of the implicit reward rules such as the value of coordination and unfairness. The non turn-taker group was more diverse: some subjects did not understand the game or only understood some aspects; other subjects had a better understanding but were selecting selfishly or could only follow the partner. Furthermore, the turn-taking dyads utilized action visibility driven by action time differences more extensively, although some non-turn-taking dyads overall also exploited action visibility to coordinate on a fixed color.”We also note that 4 subjects from non-turn-taking pairs (and no subjects from turn-taking pairs) showed <25% own target color selection in the end of solo training , in line with the Reviewer’s suggestion of less flexibility in non-social situations.Finally, the Reviewer 1 is absolutely correct that in many human dyads the faster leaders also drove the shifts to the other's target, as highlighted in Figures 7 and S8, and lines 422-426:“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 : 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 .”To sum up, as a group human turn-takers explored more, better understood the game and used action visibility more to coordinate, but the leaders did not exclusively strive to select their preferred option – either due to understanding of fairness or because they did not realize the payoff asymmetry. The non turn-taker group was diverse, where some subjects did not understand the game or only understood some aspects, whereas other subjects had a better understanding but could only follow the partner. We also note that the proportion of turn-takers in our study, in absence of explicit payoff instructions, is similar to what has been reported in the literature in similar tasks with more explicit instructions, suggesting the facilitation by direct action visibility (lines 497-500):“The observed turn-taking frequency was close to other variants of BoS games with explicit instructions of the payoff matrix, where approximately 50-60% of human subjects developed turn-taking .”While the number of human dyads in our study is not sufficient to make a systematic link between in-game behavior to other measures such as personality traits, this should be addressed in future work in a larger sample (lines 692-696):“Finally, it would be very interesting to relate diverse in-game behavioral strategies observed in the human pairs to individual differences in cognitive and socio-economic personality traits , and link them to stable behavioral phenotypes . This however will require a much larger sample and personality-oriented study design.”2. The authors should further consider the degree to which maximizing one's own rewards could drive different strategies in the monkeys. Although cooperating maximized the group's rewards, it wasn't clear whether one monkey could do even better by behaving competitively.This point is related to the question raised by Reviewer 1, Recommendations to the authors Comment 1 .It is an important point to clarify. To recap, the BoS paradigm is constructed such that coordination (selecting the same target) is invariably more rewarded than not coordinating (selecting different targets). This is related to the fact that converging on either of the two targets results in a game theoretic Nash equilibrium – a strategy in which no individual player can improve its payoff by changing course unilaterally. . If an agent is dissatisfied with the current mode of coordination , he/she can try to force/nudge the partner to coordinate on his/her own preferred target by insisting on it, but for this to be a successful reward maximizing strategy the partner needs to follow suit – otherwise the agent initiating the change risks a reward loss if the partner does not follow. In other words (lines 118-124):“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.”We indeed observed such behavior in both humans and macaques.Concerning human dyads, as can be seen in the revised Figure 2C (where we now indicate turn-taking dyads), all 10 turn-taking dyads earned >3 units average joint reward, as compared to only 4 of the 9 non turn-taker dyads . But as should be clearer now from the revised figure and lines 150-153:“Note that turn-taking is not maximizing average joint reward over coordination on a fixed color ; it does however equalize the rewards within a pair.”The turn-taking does optimize “fairness” . For coordinating non turn-takers, the individual reward reaches maximum (4) for the selfish agent and 3 for the accommodating agent, lower than 3.5, but still higher than 2 for no coordination (e.g. dyads 10 and 18). The revised Figure 2—figure supplement 2B shows the individual rewards and now also indicates the turn-taking pairs.For macaques, the most parsimonious explanation is that they indeed learned to maximize their own reward, either by finding one of the pure Nash equilibria , or by converging on the same side :“Secondly, due to the task target color/side randomization, selecting targets on the same side of the display resulted in low effort, efficient and fair coordination. We cannot say if fairness was a contributing factor in convergence of the four macaque pairs to this strategy, although we deem it highly unlikely.”As can be seen in revised Figure 2—figure supplement 2G which now indicates side strategy pairs, the individual reward is equalized in those macaque pairs but differs by ~0.7-1 units in fixed color-coordinating pairs.To sum up, “well-coordinating” human dyads  and all macaque dyads maximized their individual reward, achieving a high average reward above 3 units. The slight difference between well coordinating humans and macaques was not significant  and is likely due to more coordination lapses in macaques. For human and macaque dyads who coordinated statically on a fixed color, the difference between higher and lower individual rewards in a dyad was 0.7-1 units, but the accommodating agents still received more than they would for not coordinating and only selecting their own color.3. It was felt that the manuscript would benefit from including additional information on the monkeys' interactions with the human confederate. Specifically, how did their behavior differ with humans compared to other monkeys, how long do effects of training last, and would the behavior of a trained monkey be expected to translate to an untrained monkey?This point is related to the question raised by Reviewer 2, Recommendations to the authors Comment 1 . We answer each of these important aspects below.How did their behavior differ with humans compared to other monkeys?The Reviewer is asking which aspects of macaque experience with the human confederate could have led to a turn-taking behavior in monkeys. As we now clarify in lines 280-283 and new Table S2:“Before confederate training, both monkeys adopted either fixed side or fixed color strategy with the macaque partners . 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 .”The human confederates did not intentionally provide additional social cues. The monkeys learned – in case of monkey F, fairly slowly, in the span of 15 sessions  – to follow the diverse choices of consistent blockwise human turn-taker in order to maximize their reward. This learning was facilitated by the direct visibility of human actions.While our new analysis shows that 8 of 9 monkey pairs, including pairs with monkeys F and C, converged on the faster agent’s preferred choice, we speculate that this type of “static” following was indeed different from trial-by-trial monitoring and following the dynamic human actions. We clarify these points in lines 265-279:“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 confederate’s choices should not affect monkey’s behavior.”in lines 512-518:“Although we found that all macaque pairs that exhibited a significant difference in action times between the agents (8 of 9 pairs) converged on the faster agent’s preferred choice, it seems that initially macaques did not rely on trial-by-trial action visibility. This is suggested by the fact that it required relatively long training before macaques started to follow a human confederate dynamically. Instead, we speculate that slower macaques adapted to their partner’s static choice by maximizing their reward over the remaining options.”and lines 614-618:“Very different dynamics transpired in additional experiments with macaques. Pairing them with a human confederate who consistently alternated between colors in short blocks but did not intentionally provide any additional social cues demonstrated that macaques are capable to closely follow partner’s actions , and adopt the imposed “turn-taking” to maximize their reward.”How long do effects of training last?As shown in Would the behavior of a trained monkey be expected to translate to an untrained monkey?We thank the Reviewer for this insightful question. It seems that for turn-taking behavior to occur a single confederate-trained monkey is not sufficient, since turn-taking is a joint strategy that requires active participation of both agents. We now mention this in the Discussion (lines 634-639) and in the legend of the new Table S2:“Notably, we considered the interactions in the pairs where one monkey underwent the confederate training, and other did not . In all 4 instances, static strategies prevailed, suggesting that turn-taking as a joint strategy behavior requires active participation of both agents. We note, however, that in all 3 cases of fixed color strategy, the confederate-trained monkeys accommodated the selfish choice of their partners, perhaps because they had learned to monitor and follow partner’s actions.”4. A notable limitation of the study's ability to compare across species is that the rewards given to the monkeys and humans differed qualitatively (primary vs. secondary rewards). Although this is touched on in the discussion, it was felt that further consideration is warranted. For instance, to what extent might motivation to obtain each outcome drive the tendency to be 'selfish' versus cooperative? Is there any evidence that monkeys would adopt different strategies if they were more sated?This point is related to the question raised by Reviewer 1, Recommendations to the authors Comment 2 .It is indeed an important point; we agree that the difference between primary vs secondary rewards might be crucial and it is essential to consider potential changes in motivation during macaque sessions. We considered the two aspects of this question: (i) coordination vs no coordination and (ii) selfish vs benevolent coordination.Concerning the first aspect, in the BoS game the coordination is invariably better than no coordination . Therefore, “selfish” tendency to select only own preferred color would result in less reward, unless a partner always accommodates. A “rational” less sated monkey should select the coordination on either color to gain more reward, so from this perspective one might expect that there would be more coordination at the start of the sessions compared to the end. We however observed the effect in the opposite direction: when the difference was significant, the coordination (as assessed by the average joint reward) was stronger in the last 200 trials of a session compared to the first 100. In fact, we see a similar effect in the human dyads, suggesting the development of coordination irrespective of the reward type . We now illustrate how the individual average reward, average action time, and the control measure “fraction of choosing left” (pertinent for the side coordination strategy) change between the start and the end of a session  and 3. IOf course, we cannot rule out that in a less sated monkey the urge of (reflexively) selecting “own” pre-trained color might override the prospect of the coordination benefit, resulting in less coordination at the start of a session. But we cannot disentangle this effect from a need to (re)establish the coordination regime. We emphasize however that both prior to and during the confederate training, there was no significant difference in the coordination strength or individual rewards between the start and the end, in the late sessions – on which we base our strategy descriptions (while this difference was present in early sessions when the dyadic learning just started). In other words, the selection behavior was fairly stable in late macaque-macaque and last confederate-macaque sessions. Likewise, there was no significant increase in rewards in the macaque FC pair after the confederate training.No change in the fraction of choosing left confirms that there was no shift in side-based strategies in the course of sessions. Finally, we checked how the action times changed in the course of each session. There was an increase from early to late trials (except in confederate-trained FC pair and in humans). This increase can be due to decreased motivation because of satiation and/or fatigue. But to re-iterate, these effects did not result in a shift of strategy.Concerning the second aspect, selfish vs benevolent coordination, we now analyzed if the proportion of selfish vs benevolent coordinated trials might change in the course of each session . In the “To assess if the balance between selfish vs benevolent coordination changed in the course of each session, we analyzed the proportion of coordinating on F’s preferred color among all coordinated trials (note that this is complementary to the proportion of coordinating on C’s color) in first 100 vs last 200 trials (Fisher’s exact test with Bonferroni correction for the number of tested sessions). In several sessions  one of the two agents became more selfishly insistent on his preferred color towards the end of the session – while the other accommodated. Interestingly, the roles reversed in different sessions: in FC-1 C is the leader, while in FC-3 and FC-6 F is the leader. We conclude that during the dynamic coordination, there is a tendency for this pair to gradually adopt more consistent leader-follower roles, potentially because one monkey accommodates and follows more easily in a more sated state.”Furthermore, we added the number of solo trials monkeys performed after each dyadic session, to show that the end of dyadic sessions did not correspond to fully waning motivation (lines 864-866):“After the dyadic task we offered monkeys the opportunity to obtain additional liquid in the solo version of the task for as long as they continued to work , essentially making participation in the dyadic trials optional.”To sum up, while it is indisputable that the nature of the reward and the underlying motivations differed between the two species, there is very little indication that variations in satiation level in the course of each session played a substantial role in shaping macaque strategies – further highlighted in lines 682-691:“Inherently, the nature of reward differed between the species: humans received monetary compensation while macaques worked for a liquid reward. Note, however, that in each session macaques were offered an opportunity to work and obtain more liquid in a solo regime after the dyadic experiment , so the participation in a dyadic interaction was not forced, and the end of the dyadic sessions was not associated with a fully waning motivation. We also did not observe that variations in satiation level in the course of each session played a substantial role in shaping macaque strategies. Nevertheless, it is important to acknowledge differences between primary and secondary rewards, and potential divergence in the underlying motivations and learning, beyond the species differences in socialization and cognitive capacities considered above.”5. Can the authors more precisely consider the role of action visibility? Specifically, whether visibility influences social cognition or aides sensorimotor coordination.This point is related to the question raised by Reviewer 2, Recommendations to the authors Comment 3.We thank the Reviewer for highlighting this aspect. The answer is both. Action visibility is certainly influencing the sensorimotor coordination, as demonstrated by the differences in action times between opaque and transparent conditions , convergence to a faster agent’s preference during static coordination , a tight relationship between action visibility and choice behavior , and inter-agent action time differences in coordinated vs non-coordinated trials . Furthermore, anecdotally human confederates reported that macaques paid close attention to their actions and often did not even start the trial unless they saw a human doing so. At the same time, we firmly believe  that also the social cognition “per se” was influenced by the direct face-to-face context and by touching the “same” targets. We expanded discussion on this in lines 582-589:“In our study, action visibility facilitated sensorimotor coordination, as demonstrated by action time differences in transparent vs. opaque conditions, convergence to a faster agent’s preference during static coordination, a tight relationship between action visibility and choice behavior in turn-taking macaques, and inter-agent action time differences in coordinated vs. non-coordinated trials. Seeing actual movements rather than relying on abstract representations of others such as cursor motions might be especially crucial in nonhuman experiments. But beyond immediate sensorimotor aspects, acting face-to-face on the same targets in close proximity likely influenced social cognition on a more general level.”6. Finally, can the authors provide further discussion of the effects of learning and socialization on this task across species?This point is related to the question raised by Reviewer 3. We readily acknowledge the differences between primary vs secondary rewards  and different underlying motivations and expectations of the two species. We see within-session learning effects in both species, but only in monkeys we could study learning across multiple sessions, with the same and different partners. We extended the acknowledgment of these differences in lines 688-696:“Nevertheless, it is important to acknowledge differences between primary and secondary rewards, and potential divergence in the underlying motivations and learning, beyond the species differences in socialization and cognitive capacities considered above.Finally, it would be very interesting to relate diverse in-game behavioral strategies observed in the human pairs to individual differences in cognitive and socio-economic personality traits , and link them to stable behavioral phenotypes . This however will require a much larger sample and personality-oriented study design.”Reviewer #1 (Recommendations for the authors):1. It would be helpful to know more about how the goal of maximizing reward might impact the behaviors described here. To start, how does turn-taking compare with the simpler strategies used by the monkeys in terms of reward received? Figure 2C seems to show that the average reward earned by two subjects was actually quite similar between monkeys and humans. The highest-earning human pairs (presumably those who were turn-taking) might have earned slightly more than the monkeys, but there doesn't appear to be a big advantage to turn-taking, in terms of simple reward maximization. Is this true, and if so, is it possible to interpret their behavior as arising from other advantages ? What about rewards received by each individual and not the average across pairs? If an agent only values rewards received by themself, is there a more or less optimal approach to this task?Please see the response to the point 2 of essential revisions.In brief, the revised Figure 2C and associated analysis indeed show that the average joint reward was similar between monkeys and humans, and the advantage of the turn-taking (or a side strategy resulting in “automatic turn-taking” adopted by 4 monkey pairs) is not in maximizing the joint reward per pair, but in simultaneously maximizing both individual rewards. We surmise that human turn-takers – but not side-coordinating macaques – were indeed at least in part motivated by equity considerations.The individual rewards are shown in Figure 2—figure supplement 2B,G. Indeed, if the agent only values own reward, the optimal approach is to force coordination on his/her preferred color, as long as the partner plays along. Clear examples of such behavior are human pairs 10, 18, 16 and macaque pairs CL, TF and TC . From a purely economic value perspective of a selfish agent, the turn-taking only makes sense to ensure coordination in a long term .2. Related to the above, it is notable that rewards given to the monkeys and humans differed qualitatively, as did their motivation to pursue them. The humans were receiving small amounts of money in a fairly abstract arrangement in which money was obtained at the end of the session, whereas the monkeys were thirsty and received fluid rewards continuously depending on trial outcomes. To what extent is the motivation to be 'selfish' versus cooperative driven by the motivation to obtain each outcome? For instance, if monkeys were not water deprived, would they be more likely to cooperate rather than compete? Is there any evidence that motivation to get the juice reward changed toward the end of the session?Please see the response to the point 4 of essential revisions.3. Are there action time plots similar to those shown for the monkeys for the human pairs? Although Figure 6B is compelling, if I understand it correctly, it just shows that the humans' choices can't be predicted based on who responded first. But it's hard to understand, then, how this type of coordination would emerge if one participant didn't watch and follow the other. The results in 6B could occur if the person who responds faster also drives the shifts to the other's target from time to time, as suggested in Figure 7. Therefore, is it possible that there is a leader and follower in the human pairs (perhaps these roles switch off), but the leader just doesn't choose their own color reliably?Please see the response to the point 1 of essential revisions.Since the point 1 of essential revisions also deals with a different emphasis on comparing turn-takers vs non turn-takers, raised by the Reviewer 2, we briefly summarize the specific answer here. The Reviewer is absolutely correct – indeed, there were temporal leaders and followers in the human pairs, and indeed, the faster players equally often selected their own color and the color of the partner, to equalize the rewards, as suggested by Figure 7 and Figure 7—figure supplement 1. We now emphasize this point more clearly in lines 422-426:“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 : 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 :This manuscript is nicely organized and very thorough. However, it could be improved even further if some points can be discussed in more detail or with additional data analyses.1. Related to Figure 2D: the authors mentioned that some human pairs  were not employing the turn-taking strategy. From this figure, these pairs were actually not a small number (9 out of 19). The authors should discuss what might be the reason behind this. Is it possible that these subjects were not very socially engaged  or were less flexible when choosing different options ? It will be better if the authors could perform additional data analyses to characterize the differences between the two groups of humans  to examine why they were different from the turn-taking pairs and more similar to the monkey pairs.Please see the response to the point 1 of essential revisions.2. Related to Figure 5: It is still a little puzzling that monkeys' behaviors can be improved after being trained with humans. What was the reason for this change? Did the human subjects provide extra social cues to the monkeys in order to encourage them to pay more attention, or did the monkeys learn turn-taking simply from the diverse choice patterns of the human subjects? The authors need to have further discussion about this point.Please see the response to the point 3 of essential revisions.3. It is not very clear whether the action visibility is influencing social cognition or simply aiding sensorimotor coordination. The authors should discuss this further in Discussion.Please see the response to the point 5 of essential revisions.


A subgroup analysis highlighted that high levels of sPD-L1 were associated with worse survival in patients affected by NSCLC . An HR > 1 indicated that patients with low levels of sPD-L1 have the highest rates of OS/PFS. In this meta-analysis, we clarified the role of sPD-L1 in different solid cancers treated exclusively with Immune checkpoint inhibitors (ICIs). sPD-L1 could represent a non-invasive biomarker that is easily dosable in the blood of patients. The pooled data from the selected studies showed that a high circulating concentration of sPD-L1 in cancer patients correlates with worse survival, suggesting that it may be a helpful prognostic biomarker for the selection of cancer patients before immunotherapy, thus improving the efficacy of ICIs and avoiding unnecessary treatment.Blocking the Programmed Cell Death Protein 1 (PD-1)/programmed death ligand-1 (PD-L1) axis has demonstrated great efficacy in cancer immunotherapy treatment and remains the central modality of immune targeting. To support the rational and tailored use of these drugs, it is important to identify reliable biomarkers related to survival. The role of the soluble form of the PD-L1 (sPD-L1) as a prognostic biomarker related to survival in solid cancer patients treated with immunotherapy has not yet been consistently evaluated. A systematic literature search of original articles in PubMed, MEDLINE and Scopus was conducted. Studies reporting hazard ratios (HRs) with a 95% confidence interval (CI) or Kaplan–Meier curves or individual patient data for overall survival (OS) or progression-free survival (PFS) associated with baseline levels of sPD-L1 in cancer patients undergoing immunotherapy treatment were considered eligible. Twelve studies involving 1076 patients and different tumor types treated with immunotherapy were included in the analysis. High blood levels of sPD-L1 correlated with poorer OS and PFS in cancer patients treated with immunotherapy (HR = 1.49, 95%CI: 1.15, 1.93,  Immunotherapy has revolutionized cancer treatment in the last decade; indeed, blocking the Programmed Cell Death Protein 1 (PD-1)/programmed death ligand-1 (PD-L1) axis has demonstrated great efficacy in the treatment of cancer and remains the central modality of immune targeting for monotherapy and in combination treatments. Immune-checkpoint inhibitors (ICIs), such as the anti-PD1/PD-L1 blocking antibodies, mainly act on “exhausted” lymphocytes by unleashing their proliferation capacity, restoring function, inducing the release of cytokines and facilitating the activation of an antitumor immune response . To dateMoreover, PD-L1 molecules can also exist in a soluble form (sPD-L1) that is detectable in the blood. Several forms of sPD-L1 have been described: cleaved, secreted splice variants and exosomal , and thePrevious meta-analyses have suggested that sPD-L1 can predict survival in cancer and that a high concentration of sPD-L1 in blood is associated with worse prognosis ,13,14. NThis meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. On July 2022, we performed a systematic literature search of PubMed, MEDLINE and Scopus by searching the terms: “Immunotherapy” AND [“cancer” OR “tumor”] AND [“sPDL1” OR “serum” OR “plasma” OR “soluble” OR “blood”] AND [“PDL1” OR “Program death-ligand 1” OR “PD-L1”]. Studies were considered eligible if they reported hazard ratios (HRs) or Kaplan–Meier curves or individual patient data for overall survival (OS) or progression-free survival (PFS). In order to reduce the selection bias, only studies that evaluated sPD-L1 expression by ELISA or Luminex were included. The reference lists of original reports and reviews already published were also analyzed to identify other potential studies. Review articles, case reports, editorials and letters were excluded.https://automeris.io/WebPlotDigitizer/, accessed on 15 July 2022) was used to estimate data from the figures and extract the proportion of surviving patients at 12 and 24 (if reported) months for patient groups with low or high levels of sPD-L1. The estimated data were also plotted and superimposed on the original Kaplan–Meier curves for final refinement. Individual patients’ data were subsequently pooled.The estimates of the HRs for PFS and OS were extracted from the studies. When data was presented only as a Kaplan–Meier curve, the WebPlotDigitizer tool . We conducted all analyses with the R-package. In our study, we used a p-value < 0.10 in the Cochrane-Q test or an I2 statistic value higher than 60%. A random-effects model (REM) was chosen to estimate the pooled estimate of HRs and the impact of baseline sPD-L1 values on the patient outcomes, owing to the expected heterogeneity. The cause of the heterogeneity (if I2 > 60%) was investigated using a sub-group analysis . Significant heterogeneity was indicated by a The quality of the case-control studies eligible for inclusion was evaluated using the Newcastle-Ottawa Scale (NOS) . Studiesn = 20); no investigation between sPD-L1 levels and PFS or OS (n = 4); no dichotomy of sPD-L1 levels as “high” or “low” (n = 2); no solid tumor (n = 4); no data for sPD-L1 values at baseline (n = 1). Finally, 12 studies involving several tumors  and a tThe quality assessment of the studies was calculated using the Newcastle–Ottawa Quality Assessment Scale . StudiesThe inspection of the funnel plots showed an asymmetry for both the investigated outcomes that was caused by between-study heterogeneity, indicating a publication bias . In agrep < 0.01, I2 = 77%).Nine studies reporting the OS of 880 cases were included in the meta-analysis. Among the studies reporting the OS, the one-year OS average(range) was 62.2 % (55.6–69.7%) and 34.9% (14–55%) for cancer patients with low and high values of sPD-L1, respectively, while the two-year OS average(range) was 38.0% (19.6–54%) and 16.8% (0–30%), respectively . Moreovep < 0.01, I2 = 82%). Among studies reporting the PFS, the one-year PFS average(range) was 38.5 % (24.8–50%) and 27.5% (0–100%) for cancer patients with low and high values of PD-L1, respectively; while the two-year PFS average(range) was 27.5% (0–100%) and 9.0% (0–21.6%), respectively . The forThus, our analysis indicated that the value of sPD-L1 in the blood can be considered a prognostic factor for OS and PFS in cancer patients with advanced disease treated with immunotherapy.p = 0.02, I2 = 83%; p < 0.01, I2 = 88%, p = 0.49, I2 = 83%, p = 0.62, I2 = 52%, The twelve studies included in the meta-analysis mainly considered three different tumor types treated with immunotherapy: NSCLC, RCC and melanoma. Therefore, a subgroup analysis was performed for each tumor. The subgroup meta-analysis, which pooled data for OS from five studies, including 542 patients affected by NSCLC, indicated that a higher concentration of sPD-L1 in the blood of these patients was significantly associated with worse survival, with an HR = 1.81 ; thus, the random-effects meta-analysis was adopted and a subgroup investigation was conducted. Of note, random-effects meta-analyses allow for heterogeneity by assuming that the underlying effects follow a normal distribution. Thus, results must be interpreted considering this assumption . Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (updated March 2011). The Cochrane Collaboration, 2011. Available from www.handbook.cochrane.org). Moreover, the cut-off values of sPD-L1 in the 12 studies were quite different, contributing to the heterogeneity and limiting the analysis of subgroups for patients with homogenous cut-offs. However, given that sPD-L1 has different sources, such as tumor and immune cells, the heterogeneous levels of this molecule in the blood can reflect several tumor settings, different microenvironments and various immune infiltration profiles. Previous therapies and different molecular signatures from several tumor types could also influence the release of sPDL-1 in the blood. Moreover, although the studies were selected for similar assays, the different kits and biological samples used in the studies could contribute to heterogeneity. In fact, when the same kit  wg number  that utiIn conclusion, this meta-analysis shows that sPD-L1 could represent a prognostic biomarker for survival in cancer patients undergoing immunotherapy treatment. In particular, low sPD-L1 was significantly correlated with better OS and PFS. Future ad hoc studies with standard assessment methods could be carried out to define the optimal cut-off value for sPD-L1 and validate its prognostic role in regard to immunotherapy response in cancer. Further studies are also needed on different solid tumor settings to better clarify the role of sPD-L1 in these tumors. Therefore, sPD-L1 could be a non-invasive, reproducible, and easily measurable biomarker in the blood, able to identify and select patients who will benefit from immunotherapy treatment, which has now become the first-line therapy for many cancers.