Super-additive cooperation.

Repeated interactions provide an evolutionary explanation for one-shot human cooperation that is counterintuitive but orthodox1-3. Intergroup competition4-7 provides an explanation that is intuitive but heterodox. Here, using models and a behavioural experiment, we show that neither mechanism reliably supports cooperation. Ambiguous reciprocity, a class of strategies that is generally ignored in models of reciprocal altruism, undermines cooperation under repeated interactions. This finding challenges repeated interactions as an evolutionary explanation for cooperation in general, which further challenges the claim that repeated interactions in the past can explain one-shot cooperation in the present. Intergroup competitions also do not reliably support cooperation because groups quickly become extremely similar, which limits scope for group selection. Moreover, even if groups vary, group competitions may generate little group selection for multiple reasons. Cooperative groups, for example, may tend to compete against each other8. Whereas repeated interactions and group competitions do not support cooperation by themselves, combining them triggers powerful synergies because group competitions constrain the corrosive effect of ambiguous reciprocity. Evolved strategies often consist of cooperative reciprocity with ingroup partners and uncooperative reciprocity with outgroup partners. Results from a behavioural experiment in Papua New Guinea fit exactly this pattern. They thus suggest neither an evolutionary history of repeated interactions without group competition nor a history of group competition without repeated interactions. Instead, our results suggest social motives that evolved under the joint influence of both mechanisms.

Inequality aversion predicts support for public and private redistribution.

Rising inequality has brought redistribution back on the political agenda. In theory, inequality aversion drives people's support for redistribution. People can dislike both advantageous inequality (comparison relative to those worse off) and disadvantageous inequality (comparison relative to those better off). Existing experimental evidence reveals substantial variation across people in these preferences. However, evidence is scarce on the broader role of these two distinct forms of inequality aversion for redistribution in society. We provide evidence by exploiting a unique combination of data. We use an incentivized experiment to measure inequality aversion in a large population sample (≈9,000 among 20- to 64-y-old Danes). We link the elicited inequality aversion to survey information on individuals' support for public redistribution (policies that reduce income differences) and administrative records revealing their private redistribution (real-life donations to charity). In addition, the link to administrative data enables us to include a large battery of controls in the empirical analysis. Theory predicts that support for public redistribution increases with both types of inequality aversion, while private redistribution should increase with advantageous inequality aversion, but decrease with disadvantageous inequality aversion. A strong dislike for disadvantageous inequality makes people willing to sacrifice own income to reduce the income of people who are better off, thereby reducing the distance to people with more income than themselves. Public redistribution schemes achieve this but private donations to charity do not. Our empirical results provide strong support for these predictions and with quantitatively large effects compared to other predictors.

Preferences predict who commits crime among young men.

Understanding who commits crime and why is a key topic in social science and important for the design of crime prevention policy. In theory, people who commit crime face different social and economic incentives for criminal activity than other people, or they evaluate the costs and benefits of crime differently because they have different preferences. Empirical evidence on the role of preferences is scarce. Theoretically, risk-tolerant, impatient, and self-interested people are more prone to commit crime than risk-averse, patient, and altruistic people. We test these predictions with a unique combination of data where we use incentivized experiments to elicit the preferences of young men and link these experimental data to their criminal records. In addition, our data allow us to control extensively for other characteristics such as cognitive skills, socioeconomic background, and self-control problems. We find that preferences are strongly associated with actual criminal behavior. Impatience and, in particular, risk tolerance are still strong predictors when we include the full battery of controls. Crime propensities are 8 to 10 percentage points higher for the most risk-tolerant individuals compared to the most risk averse. This effect is half the size of the effect of cognitive skills, which is known to be a very strong predictor of criminal behavior. Looking into different types of crime, we find that preferences significantly predict property offenses, while self-control problems significantly predict violent, drug, and sexual offenses.

Testing models at the neural level reveals how the brain computes subjective value.

Decisions are based on the subjective values of choice options. However, subjective value is a theoretical construct and not directly observable. Strikingly, distinct theoretical models competing to explain how subjective values are assigned to choice options often make very similar behavioral predictions, which poses a major difficulty for establishing a mechanistic, biologically plausible explanation of decision-making based on behavior alone. Here, we demonstrate that model comparison at the neural level provides insights into model implementation during subjective value computation even though the distinct models parametrically identify common brain regions as computing subjective value. We show that frontal cortical regions implement a model based on the statistical distributions of available rewards, whereas intraparietal cortex and striatum compute subjective value signals according to a model based on distortions in the representations of probabilities. Thus, better mechanistic understanding of how cognitive processes are implemented arises from model comparisons at the neural level, over and above the traditional approach of comparing models at the behavioral level alone.

Intra-individual variability in task performance after cognitive training is associated with long-term outcomes in children.

The potential benefits and mechanistic effects of working memory training (WMT) in children are the subject of much research and debate. We show that after five weeks of school-based, adaptive WMT 6-9 year-old primary school children had greater activity in prefrontal and striatal brain regions, higher task accuracy, and reduced intra-individual variability in response times compared to controls. Using a sequential sampling decision model, we demonstrate that this reduction in intra-individual variability can be explained by changes to the evidence accumulation rates and thresholds. Critically, intra-individual variability is useful in quantifying the immediate impact of cognitive training interventions, being a better predictor of academic skills and well-being 6-12 months after the end of training than task accuracy. Taken together, our results suggest that attention control is the initial mechanism that leads to the long-run benefits from adaptive WMT. Selective and sustained attention abilities may serve as a scaffold for subsequent changes in higher cognitive processes, academic skills, and general well-being. Furthermore, these results highlight that the selection of outcome measures and the timing of the assessments play a crucial role in detecting training efficacy. Thus, evaluating intra-individual variability, during or directly after training could allow for the early tailoring of training interventions in terms of duration or content to maximise their impact.

Changing cultural attitudes towards female genital cutting.

As globalization brings people with incompatible attitudes into contact, cultural conflicts inevitably arise. Little is known about how to mitigate conflict and about how the conflicts that occur can shape the cultural evolution of the groups involved. Female genital cutting is a prominent example. Governments and international agencies have promoted the abandonment of cutting for decades, but the practice remains widespread with associated health risks for millions of girls and women. In their efforts to end cutting, international agents have often adopted the view that cutting is locally pervasive and entrenched. This implies the need to introduce values and expectations from outside the local culture. Members of the target society may view such interventions as unwelcome intrusions, and campaigns promoting abandonment have sometimes led to backlash as they struggle to reconcile cultural tolerance with the conviction that cutting violates universal human rights. Cutting, however, is not necessarily locally pervasive and entrenched. We designed experiments on cultural change that exploited the existence of conflicting attitudes within cutting societies. We produced four entertaining movies that served as experimental treatments in two experiments in Sudan, and we developed an implicit association test to unobtrusively measure attitudes about cutting. The movies depart from the view that cutting is locally pervasive by dramatizing members of an extended family as they confront each other with divergent views about whether the family should continue cutting. The movies significantly improved attitudes towards girls who remain uncut, with one in particular having a relatively persistent effect. These results show that using entertainment to dramatize locally discordant views can provide a basis for applied cultural evolution without accentuating intercultural divisions.

Business culture and dishonesty in the banking industry.

Trust in others' honesty is a key component of the long-term performance of firms, industries, and even whole countries. However, in recent years, numerous scandals involving fraud have undermined confidence in the financial industry. Contemporary commentators have attributed these scandals to the financial sector's business culture, but no scientific evidence supports this claim. Here we show that employees of a large, international bank behave, on average, honestly in a control condition. However, when their professional identity as bank employees is rendered salient, a significant proportion of them become dishonest. This effect is specific to bank employees because control experiments with employees from other industries and with students show that they do not become more dishonest when their professional identity or bank-related items are rendered salient. Our results thus suggest that the prevailing business culture in the banking industry weakens and undermines the honesty norm, implying that measures to re-establish an honest culture are very important.

The neurobiology of rewards and values in social decision making.

How does our brain choose the best course of action? Choices between material goods are thought to be steered by neural value signals that encode the rewarding properties of the choice options. Social decisions, by contrast, are traditionally thought to rely on neural representations of the self and others. However, recent studies show that many types of social decisions may also involve neural value computations. This suggests a unified mechanism for motivational control of behaviour that may incorporate both social and non-social factors. In this Review, we outline a theoretical framework that may help to identify possible overlaps and differences between the neural processes that guide social and non-social decision making.

Spatial gradient in value representation along the medial prefrontal cortex reflects individual differences in prosociality.

Despite the importance of valuing another person's welfare for prosocial behavior, currently we have only a limited understanding of how these values are represented in the brain and, more importantly, how they give rise to individual variability in prosociality. In the present study, participants underwent functional magnetic resonance imaging while performing a prosocial learning task in which they could choose to benefit themselves and/or another person. Choice behavior indicated that participants valued the welfare of another person, although less so than they valued their own welfare. Neural data revealed a spatial gradient in activity within the medial prefrontal cortex (MPFC), such that ventral parts predominantly represented self-regarding values and dorsal parts predominantly represented other-regarding values. Importantly, compared with selfish individuals, prosocial individuals showed a more gradual transition from self-regarding to other-regarding value signals in the MPFC and stronger MPFC-striatum coupling when they made choices for another person rather than for themselves. The present study provides evidence of neural markers reflecting individual differences in human prosociality.

Assessing statistical significance in multivariable genome wide association analysis.

MOTIVATION: Although Genome Wide Association Studies (GWAS) genotype a very large number of single nucleotide polymorphisms (SNPs), the data are often analyzed one SNP at a time. The low predictive power of single SNPs, coupled with the high significance threshold needed to correct for multiple testing, greatly decreases the power of GWAS. RESULTS: We propose a procedure in which all the SNPs are analyzed in a multiple generalized linear model, and we show its use for extremely high-dimensional datasets. Our method yields P-values for assessing significance of single SNPs or groups of SNPs while controlling for all other SNPs and the family wise error rate (FWER). Thus, our method tests whether or not a SNP carries any additional information about the phenotype beyond that available by all the other SNPs. This rules out spurious correlations between phenotypes and SNPs that can arise from marginal methods because the 'spuriously correlated' SNP merely happens to be correlated with the 'truly causal' SNP. In addition, the method offers a data driven approach to identifying and refining groups of SNPs that jointly contain informative signals about the phenotype. We demonstrate the value of our method by applying it to the seven diseases analyzed by the Wellcome Trust Case Control Consortium (WTCCC). We show, in particular, that our method is also capable of finding significant SNPs that were not identified in the original WTCCC study, but were replicated in other independent studies. AVAILABILITY AND IMPLEMENTATION: Reproducibility of our research is supported by the open-source Bioconductor package hierGWAS. CONTACT: peter.buehlmann@stat.math.ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of Behavioral Control and Associated vmPFC-DLPFC Connectivity Explains Children's Increased Resistance to Temptation in Intertemporal Choice.

Human civilization is based on the successful pursuit of long-term goals, requiring the ability to forego immediate pleasure for the sake of larger future rewards. This ability improves with age, but the precise cognitive and neural mechanisms underlying its development remain elusive. The developmental changes could result either from younger children valuing immediate rewards more strongly or because older children become better at controlling their impulses. By implementing 2 tasks, a choice-independent valuation task and an intertemporal choice task, both behaviorally and using fMRI in twenty 6- to 13-year old children, we show developmental improvements in behavioral control to uniquely account for age-related changes in temporal discounting. We show further that overcoming temptation during childhood occurs as a function of an age-related increase in functional coupling between value signals in the ventromedial prefrontal cortex and brain regions dedicated to behavioral control, such as left dorsolateral prefrontal cortex during choice. These findings can help to devise measures that reduce the substantial costs of impatience to society.

Anticipatory anxiety disrupts neural valuation during risky choice.

Incidental negative emotions unrelated to the current task, such as background anxiety, can strongly influence decisions. This is most evident in psychiatric disorders associated with generalized emotional disturbances. However, the neural mechanisms by which incidental emotions may affect choices remain poorly understood. Here we study the effects of incidental anxiety on human risky decision making, focusing on both behavioral preferences and their underlying neural processes. Although observable choices remained stable across affective contexts with high and low incidental anxiety, we found a clear change in neural valuation signals: during high incidental anxiety, activity in ventromedial prefrontal cortex and ventral striatum showed a marked reduction in (1) neural coding of the expected subjective value (ESV) of risky options, (2) prediction of observed choices, (3) functional coupling with other areas of the valuation system, and (4) baseline activity. At the same time, activity in the anterior insula showed an increase in coding the negative ESV of risky lotteries, and this neural activity predicted whether the risky lotteries would be rejected. This pattern of results suggests that incidental anxiety can shift the focus of neural valuation from possible positive consequences to anticipated negative consequences of choice options. Moreover, our findings show that these changes in neural value coding can occur in the absence of changes in overt behavior. This suggest a possible pathway by which background anxiety may lead to the development of chronic reward desensitization and a maladaptive focus on negative cognitions, as prevalent in affective and anxiety disorders.

Irrational time allocation in decision-making.

Time is an extremely valuable resource but little is known about the efficiency of time allocation in decision-making. Empirical evidence suggests that in many ecologically relevant situations, decision difficulty and the relative reward from making a correct choice, compared to an incorrect one, are inversely linked, implying that it is optimal to use relatively less time for difficult choice problems. This applies, in particular, to value-based choices, in which the relative reward from choosing the higher valued item shrinks as the values of the other options get closer to the best option and are thus more difficult to discriminate. Here, we experimentally show that people behave sub-optimally in such contexts. They do not respond to incentives that favour the allocation of time to choice problems in which the relative reward for choosing the best option is high; instead they spend too much time on problems in which the reward difference between the options is low. We demonstrate this by showing that it is possible to improve subjects' time allocation with a simple intervention that cuts them off when their decisions take too long. Thus, we provide a novel form of evidence that organisms systematically spend their valuable time in an inefficient way, and simultaneously offer a potential solution to the problem.

A Common Mechanism Underlying Food Choice and Social Decisions.

People make numerous decisions every day including perceptual decisions such as walking through a crowd, decisions over primary rewards such as what to eat, and social decisions that require balancing own and others' benefits. The unifying principles behind choices in various domains are, however, still not well understood. Mathematical models that describe choice behavior in specific contexts have provided important insights into the computations that may underlie decision making in the brain. However, a critical and largely unanswered question is whether these models generalize from one choice context to another. Here we show that a model adapted from the perceptual decision-making domain and estimated on choices over food rewards accurately predicts choices and reaction times in four independent sets of subjects making social decisions. The robustness of the model across domains provides behavioral evidence for a common decision-making process in perceptual, primary reward, and social decision making.

Inferring on the intentions of others by hierarchical Bayesian learning.

Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to "player" or "adviser" roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser's incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser's intentions were presently stable. Finally, our model of the players' inference predicted the players' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers' helpfulness and the advisers' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.