Expertise increases planning depth in human gameplay.

A hallmark of human intelligence is the ability to plan multiple steps into the future1,2. Despite decades of research3-5, it is still debated whether skilled decision-makers plan more steps ahead than novices6-8. Traditionally, the study of expertise in planning has used board games such as chess, but the complexity of these games poses a barrier to quantitative estimates of planning depth. Conversely, common planning tasks in cognitive science often have a lower complexity9,10 and impose a ceiling for the depth to which any player can plan. Here we investigate expertise in a complex board game that offers ample opportunity for skilled players to plan deeply. We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times and eye movements. We also perform a Turing test and a reconstruction experiment. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Experts memorize and reconstruct board features more accurately. Using complex tasks combined with precise behavioural modelling might expand our understanding of human planning and help to bridge the gap with progress in artificial intelligence.

De novo protein design by citizen scientists.

Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now-starting from an extended polypeptide chain-generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds-including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem.

Same allocation proposed by an individual or a group elicits distinct responses: Evidence from event-related potentials and neural oscillation.

People tend to perceive the same information differently depending on whether it is expressed in an individual or a group frame. It has also been found that the individual (vs. group) frame of expression tends to lead to more charitable giving and greater tolerance of wealth inequality. However, little is known about whether the same resource allocation in social interactions elicits distinct responses depending on proposer type. Using the second-party punishment task, this study examined whether the same allocation from different proposers (individual vs. group) leads to differences in recipient behavior and the neural mechanisms. Behavioral results showed that reaction times were longer in the unfair (vs. fair) condition, and this difference was more pronounced when the proposer was the individual (vs. group). Neural results showed that proposer type (individual vs. group) influenced early automatic processing (indicated by AN1, P2, and central alpha band), middle processing (indicated by MFN and right frontal theta band), and late elaborative processing (indicated by P3 and parietal alpha band) of fairness in resource allocation. These results revealed more attentional resources were captured by the group proposer in the early stage of fairness processing, and more cognitive resources were consumed by processing group-proposed unfair allocations in the late stage, possibly because group proposers are less identifiable than individual proposers. The findings provide behavioral and neural evidence for the effects of "individual/group" framing leading to cognitive differences. They also deliver insights into social governance issues, such as punishing individual and/or group violations.

Repeated rock, paper, scissors play reveals limits in adaptive sequential behavior.

How do people adapt to others in adversarial settings? Prior work has shown that people often violate rational models of adversarial decision-making in repeated interactions. In particular, in mixed strategy equilibrium (MSE) games, where optimal action selection entails choosing moves randomly, people often do not play randomly, but instead try to outwit their opponents. However, little is known about the adaptive reasoning that underlies these deviations from random behavior. Here, we examine strategic decision-making across repeated rounds of rock, paper, scissors, a well-known MSE game. In experiment 1, participants were paired with bot opponents that exhibited distinct stable move patterns, allowing us to identify the bounds of the complexity of opponent behavior that people can detect and adapt to. In experiment 2, bot opponents instead exploited stable patterns in the human participants' moves, providing a symmetrical bound on the complexity of patterns people can revise in their own behavior. Across both experiments, people exhibited a robust and flexible attention to transition patterns from one move to the next, exploiting these patterns in opponents and modifying them strategically in their own moves. However, their adaptive reasoning showed strong limitations with respect to more sophisticated patterns. Together, results provide a precise and consistent account of the surprisingly limited scope of people's adaptive decision-making in this setting.

Role of right temporoparietal junction for counterfactual evaluation of partner's decision in ultimatum game.

Humans assess the distributions of resources based on their aversion to unfairness. If a partner distributes in an unfair manner even though the partner had a less unfair distribution option, a recipient will believe that the partner should have chosen the counterfactual option. In this study, we investigated the neural basis for fairness evaluation of actual and counterfactual options in the ultimatum game. In this task, a partner chose one distribution option out of two options, and a participant accepted or rejected the option. The behavioral results showed that the acceptance rate was influenced by counterfactual evaluation (CE), among others, as defined by the difference of monetary amount between the actual and counterfactual options. The functional magnetic resonance imaging results showed that CE was associated with the right ventral angular gyrus (vAG) that provided one of convergent inputs to the supramarginal gyrus related to decision utility, which reflects gross preferences for the distribution options. Furthermore, inhibitory repetitive transcranial magnetic stimulation administered to the right vAG reduced the behavioral component associated with CE. These results suggest that our acceptance/rejection of distribution options relies on multiple processes (monetary amount, disadvantageous inequity, and CE) and that the right vAG causally contributes to CE.

We know that we don't know: Children's understanding of common ignorance in a coordination game.

Common ground is the knowledge, beliefs, and suppositions shared between partners in an interaction. Previous research has focused extensively on what partners know they know together, that is, "common knowledge." However, another important aspect of common ground is what partners know they do not know together, that is, "common ignorance." A new coordination game was designed to investigate children's use of common ignorance. Without communicating or seeing each other's decisions, 4- to 8-year-olds needed to make the same decision as their partner about whether to try to retrieve a reward. To retrieve it, at least one of them needed to know a secret code. The knowledge/ignorance of both partners was ostensively manipulated by showing one partner, both partners, or neither partner the secret code in four conditions: common knowledge (both knew the code), common ignorance (neither partner knew the code), common privileged self knowledge (only children knew the code), and common privileged other knowledge (only their partner knew the code). Children's decisions, latency, and uncertainty were coded. Results showed that the common ignorance states were generally more difficult than the common knowledge states. Unexpectedly, children at all ages had difficulty with coordinating when their partner knew the code but they themselves did not (common privileged other knowledge). This study shows that, along with common knowledge, common ignorance and common privileged self knowledge and other knowledge also play important roles in coordinating with others but may develop differently.

An experimental game to assess hunter's participation in zoonotic diseases surveillance.

BACKGROUND: Strengthening the surveillance of zoonotic diseases emergence in the wild meat value chains is a critical component of the prevention of future health crises. Community hunters could act as first-line observers in zoonotic pathogens surveillance systems in wildlife, by reporting early signs of the possible presence of a disease in the game animals they observe and manipulate on a regular basis. METHODS: An experimental game was developed and implemented in a forested area of Gabon, in central Africa. Our objective was to improve our understanding of community hunters' decision-making when finding signs of zoonotic diseases in game animals: would they report or dissimulate these findings to a health agency? 88 hunters, divided into 9 groups of 5 to 13 participants, participated in the game, which was run over 21 rounds. In each round the players participated in a simulated hunting trip during which they had a chance of capturing a wild animal displaying clinical signs of a zoonotic disease. When signs were visible, players had to decide whether to sell/consume the animal or to report it. The last option implied a lowered revenue from the hunt but an increased probability of early detection of zoonotic diseases with benefits for the entire group of hunters. RESULTS: The results showed that false alerts-i.e. a suspect case not caused by a zoonotic disease-led to a decrease in the number of reports in the next round (Odds Ratio [OR]: 0.46, 95% Confidence Interval [CI]: 0.36-0.8, p < 0.01). Hunters who had an agricultural activity in addition to hunting reported suspect cases more often than others (OR: 2.05, 95% CI: 1.09-3.88, p < 0.03). The number of suspect case reports increased with the rank of the game round (Incremental OR: 1.11, CI: 1.06-1.17, p < 0.01) suggesting an increase in participants' inclination to report throughout the game. CONCLUSION: Using experimental games presents an added value for improving the understanding of people's decisions to participate in health surveillance systems.

The effect of serious game and problem-based learning on nursing students' knowledge and clinical decision-making skill regarding the application of transfusion medicine in pediatric nursing.

PURPOSE: Comparing the effect of serious game and problem-based learning on nursing students' knowledge and clinical decision-making skill regarding the application of transfusion medicine in pediatric nursing. DESIGN AND METHODS: In this quasi-experimental study, 76 undergraduate nursing students were enrolled through a convenience sampling method, and were allocated to one of the three groups of serious game, problem-based learning, and control through the block randomization method. Data were collected using a valid and reliable 3-part researcher-made tool, completed before and two weeks after the intervention. Statistical analysis was performed using paired t-test, analysis of covariance, and Bonferroni post hoc test. A significance level of <0.05 was considered. RESULTS: After the intervention, mean scores of both knowledge and clinical decision-making skill increased significantly in both intervention groups (p < 0.05). Mean post-test scores of both knowledge and clinical decision-making skill in the serious game group, and only clinical decision-making skill in the problem-based learning group were significantly higher than the control group (p < 0.05). However, no significant difference was observed regarding mean post-test scores of both knowledge and clinical decision-making skill between the intervention groups (p > 0.05). CONCLUSIONS: Both serious game and problem-based learning are proven to be effective in improving nursing students' knowledge and clinical decision-making skill regarding the application of transfusion medicine in pediatric nursing. PRACTICE IMPLICATIONS: Since learning now occurs beyond classrooms and the new generation of students spend most of their time in virtual places, utilizing technology-based teaching methods like serious games can benefit both educators and students by providing continuous education, saving their time and expenses, etc.

Intention affects fairness processing: Evidence from behavior and representational similarity analysis of event-related potential signals.

In an ultimatum game, the responder must decide between pursuing self-interest and insisting on fairness, and these choices are affected by the intentions of the proposer. However, the time course of this social decision-making process is unclear. Representational similarity analysis (RSA) is a useful technique for linking brain activity with rich behavioral data sets. In this study, electroencephalography (EEG) was used to measure the time course of neural responses to proposed allocation schemes with different intentions. Twenty-eight participants played an ultimatum game as responders. They had to choose between accepting and rejecting the fair or unfair money allocation schemes of proposers. The schemes were offered based on the proposer's selfish intention (monetary gain), altruistic intention (donation to charity), or ambiguous intention (unknown to the responder). We used a spatiotemporal RSA and inter-subject RSA (IS-RSA) to explore the connections between event-related potentials (ERPs) after offer presentation and intention presentation with four types of behavioral data (acceptance, response time, fairness ratings, and pleasantness ratings). The spatiotemporal RSA results revealed that only response time variation was linked with the difference in ERPs at 432-592 ms after offer presentation on the posterior parietal and prefrontal regions. Meanwhile, the IS-RSA results found a significant association between inter-individual differences in response time and differences in ERP activity at 596-812 ms after the presentation of ambiguous intention, particularly in the prefrontal region. This study expands the intention-based reciprocal model to the third-party context and demonstrates that brain activity can represent response time differences in social decision-making.

Altered social decision making in patients with chronic pain.

BACKGROUND: Chronic pain affects up to 20% of the population, impairs quality of life and reduces social participation. Previous research reported that pain-related perceived injustice covaries with these negative consequences. The current study probed whether chronic pain patients responded more strongly to disadvantageous social inequity than healthy individuals. METHODS: We administered the Ultimatum Game, a neuroeconomic social exchange game, where a sum of money is split between two players to a large sample of patients with chronic pain disorder with somatic and psychological factors (n = 102) and healthy controls (n = 101). Anonymised, and in truth experimentally controlled, co-players proposed a split, and our participants either accepted or rejected these offers. RESULTS: Chronic pain patients were hypersensitive to disadvantageous inequity and punished their co-players for proposed unequal splits more often than healthy controls. Furthermore, this systematic shift in social decision making was independent of patients' performance on tests of executive functions and risk-sensitive (non-social) decision making . CONCLUSIONS: Our findings indicate that chronic pain is associated with anomalies in social decision making (compared to healthy controls) and hypersensitivity to social inequity that is likely to negatively impact social partaking and thereby the quality of life.

Locally noisy autonomous agents improve global human coordination in network experiments.

Coordination in groups faces a sub-optimization problem and theory suggests that some randomness may help to achieve global optima. Here we performed experiments involving a networked colour coordination game in which groups of humans interacted with autonomous software agents (known as bots). Subjects (n = 4,000) were embedded in networks (n = 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.

Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning.

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use-using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the "sample, simulate, update" (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.

Integration of social status and trust through interpersonal brain synchronization.

Trust can be a dynamic social process, during which the social identity of the interacting agents (e.g., an investor and a trustee) can bias trust outcomes. Here, we investigated how social status modulates trust and the neural mechanisms underlying this process. An investor and a trustee performed a 10-round repeated trust game while their brain activity was being simultaneously recorded using functional near-infrared spectroscopy. The social status (either high or low) of both investors and trustees was manipulated via a math competition task. The behavioral results showed that in the initial round, individuals invested more in low-status partners. However, the investment ratio increased faster as the number of rounds increased during trust interaction when individuals were paired with a high-status partner. This increasing trend was particularly prominent in the low (investor)-high (trustee) status group. Moreover, the low-high group showed increased investor-trustee brain synchronization in the right temporoparietal junction as the number of rounds increased, while brain activation in the right dorsolateral prefrontal cortex of the investor decreased as the number of rounds increased. Both interpersonal brain synchronization and brain activation predicted investment performance at the early stage; furthermore, two-brain data provided earlier predictions than did single-brain data. These effects were detectable in the investment phase in the low-high group only; no comparable effects were observed in the repayment phase or other groups. Overall, this study demonstrated a multi-brain mechanism for the integration of social status and trust.

You are excusable! Neural correlates of economic neediness on empathic concern and fairness perception.

There is ample experimental evidence showing that the proposers' social role is related to individuals' fairness perception in the Ultimatum Game (UG). However, various social roles, e.g., degree of economic neediness, have different influences on fairness perception, yet it has not been well studied. In this study, we adapted the UG paradigm and recorded electroencephalography (EEG) to probe the neural signatures of whether and how the degree of neediness influences fairness perception. Behavioral results showed that responders are prone to accept unfair offers from proposers in need more than those who are not in need. At the brain level, MFN (medial frontal negativity) was more negative-going in response to unfair than fair offers for not-in-need proposers. In contrast, we found a reversed MFN difference response to unfair and fair offers for in-need proposers, showing a strongly pure altruistic phenomenon. Moreover, we found smaller P300 amplitude was induced in the proposer-in-need condition, compared with its counterpart, while a negative correlation between empathy rating and P300 amplitude in the proposer-in-need condition regardless of the offers' fairness. The current results indicate that the degree of neediness might reduce fairness perception by promoting the empathic concern toward the in-need proposers rather than decreasing the empathic concern for the not-in-need proposers.

Third-party punishers do not compete to be chosen as partners in an experimental game.

Third-party punishment is thought to act as an honest signal of cooperative intent and such signals might escalate when competing to be chosen as a partner. Here, we investigate whether partner choice competition prompts escalating investment in third-party punishment. We also consider the case of signalling via helpful acts to provide a direct test of the relative strength of the two types of signals. Individuals invested more in third-party helping than third-party punishment and invested more in both signals when observed compared to when investments would be unseen. We found no clear effect of partner choice (over and above mere observation) on investments in either punishment or helping. Third-parties who invested more than a partner were preferentially chosen for a subsequent Trust Game although the preference to interact with the higher investor was more pronounced in the help than in the punishment condition. Third-parties who invested more were entrusted with more money and investments in third-party punishment or helping reliably signalled trustworthiness. Individuals who did not invest in third-party helping were more likely to be untrustworthy than those who did not invest in third-party punishment. This supports the conception of punishment as a more ambiguous signal of cooperative intent compared to help.

Exploring the quantum speed limit with computer games.

Humans routinely solve problems of immense computational complexity by intuitively forming simple, low-dimensional heuristic strategies. Citizen science (or crowd sourcing) is a way of exploiting this ability by presenting scientific research problems to non-experts. 'Gamification'--the application of game elements in a non-game context--is an effective tool with which to enable citizen scientists to provide solutions to research problems. The citizen science games Foldit, EteRNA and EyeWire have been used successfully to study protein and RNA folding and neuron mapping, but so far gamification has not been applied to problems in quantum physics. Here we report on Quantum Moves, an online platform gamifying optimization problems in quantum physics. We show that human players are able to find solutions to difficult problems associated with the task of quantum computing. Players succeed where purely numerical optimization fails, and analyses of their solutions provide insights into the problem of optimization of a more profound and general nature. Using player strategies, we have thus developed a few-parameter heuristic optimization method that efficiently outperforms the most prominent established numerical methods. The numerical complexity associated with time-optimal solutions increases for shorter process durations. To understand this better, we produced a low-dimensional rendering of the optimization landscape. This rendering reveals why traditional optimization methods fail near the quantum speed limit (that is, the shortest process duration with perfect fidelity). Combined analyses of optimization landscapes and heuristic solution strategies may benefit wider classes of optimization problems in quantum physics and beyond.

Third-party punishment as a costly signal of trustworthiness.

Third-party punishment (TPP), in which unaffected observers punish selfishness, promotes cooperation by deterring defection. But why should individuals choose to bear the costs of punishing? We present a game theoretic model of TPP as a costly signal of trustworthiness. Our model is based on individual differences in the costs and/or benefits of being trustworthy. We argue that individuals for whom trustworthiness is payoff-maximizing will find TPP to be less net costly (for example, because mechanisms that incentivize some individuals to be trustworthy also create benefits for deterring selfishness via TPP). We show that because of this relationship, it can be advantageous for individuals to punish selfishness in order to signal that they are not selfish themselves. We then empirically validate our model using economic game experiments. We show that TPP is indeed a signal of trustworthiness: third-party punishers are trusted more, and actually behave in a more trustworthy way, than non-punishers. Furthermore, as predicted by our model, introducing a more informative signal--the opportunity to help directly--attenuates these signalling effects. When potential punishers have the chance to help, they are less likely to punish, and punishment is perceived as, and actually is, a weaker signal of trustworthiness. Costly helping, in contrast, is a strong and highly used signal even when TPP is also possible. Together, our model and experiments provide a formal reputational account of TPP, and demonstrate how the costs of punishing may be recouped by the long-run benefits of signalling one's trustworthiness.