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Humans tend to discount information that undermines past choices and judgments. This confirmation bias has significant impact on domains ranging from politics to science and education. Little is known about the mechanisms underlying this fundamental characteristic of belief formation. Here we report a mechanism underlying the confirmation bias. Specifically, we provide evidence for a failure to use the strength of others’ disconfirming opinions to alter confidence in judgments, but adequate use when opinions are confirmatory. This bias is related to reduced neural sensitivity to the strength of others’ opinions in the posterior medial prefrontal cortex when opinions are disconfirming. Our results demonstrate that existing judgments alter the neural representation of information strength, leaving the individual less likely to alter opinions in the face of disagreement.

Key Points Most behavioural and computational models of decision making assume that the following five processes are carried out at the time the decision is made: representation, action valuation, action selection, outcome valuation, and learning. On the basis of a sizeable body of animal and human behavioural evidence, several groups have proposed the existence of three different types of valuation systems: Pavlovian, habitual and goal-directed systems. Pavlovian systems assign value to only a small set of 'prepared' behaviours and thus have a limited behavioural repertoire. Nevertheless, they might be the driving force behind behaviours with important economic consequences (for example, overeating). Examples include preparatory behaviours, such as approaching a cue that predicts food, and consummatory behaviours, such as ingesting available food. Habit valuation systems learn to assign values to stimulus–response associations on the basis of previous experience through a process of trial-and-error. Examples of habits include a smoker's desire to have a cigarette at particular times of day (for example, after a meal) and a rat's tendency to forage in a cue-dependent location after sufficient training. Goal-directed systems assign values to actions by computing action–outcome associations and then evaluating the rewards that are associated with the different outcomes. An example of a goal-directed behaviour is the decision what to eat at a new restaurant. An important difference between habitual and goal-directed systems has to do with how they respond to changes in the environment. The goal-directed system updates the value of an action as soon as the value of its outcome changes, whereas the habit system only learns with repeated experience. The values computed by the three systems can be modulated by factors such as the risk that is associated with the decision, the time delay to the outcomes, and social considerations. The quality of the decisions made by an animal depend on how its brain assigns control to the different valuation systems in situations in which it has to make a choice between several potential actions that are assigned conflicting values. The learning properties of the habit system seem to be well-described by simple reinforcement algorithms, such as Q-learning. Some of the key computations that are predicted by these models are instantiated in the dopamine system. The past decade has seen an increasing interest in the mechanisms by which the human brain decides what actions to take. Here, Rangel and colleagues provide a framework for the exploration of the neurobiological and computational basis of value-based decision making. Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.

To sustain or repair cooperation during a social exchange, adaptive creatures must understand social gestures and the consequences when shared expectations about fair exchange are violated by accident or intent. We recruited 55 individuals afflicted with borderline personality disorder (BPD) to play a multiround economic exchange game with healthy partners. Behaviorally, individuals with BPD showed a profound incapacity to maintain cooperation, and were impaired in their ability to repair broken cooperation on the basis of a quantitative measure of coaxing. Neurally, activity in the anterior insula, a region known to respond to norm violations across affective, interoceptive, economic, and social dimensions, strongly differentiated healthy participants from individuals with BPD. Healthy subjects showed a strong linear relation between anterior insula response and both magnitude of monetary offer received from their partner (input) and the amount of money repaid to their partner (output). In stark contrast, activity in the anterior insula of BPD participants was related only to the magnitude of repayment sent back to their partner (output), not to the magnitude of offers received (input). These neural and behavioral data suggest that norms used in perception of social gestures are pathologically perturbed or missing altogether among individuals with BPD. This game-theoretic approach to psychopathology may open doors to new ways of characterizing and studying a range of mental illnesses.

Infant cues, such as smiling or crying facial expressions, are powerful motivators of human maternal behavior, activating dopamine-associated brain reward circuits. Oxytocin, a neurohormone of attachment, promotes maternal care in animals, although its role in human maternal behavior is unclear. We examined 30 first-time new mothers to test whether differences in attachment, based on the Adult Attachment Interview, were related to brain reward and peripheral oxytocin response to infant cues. On viewing their own infant's smiling and crying faces during functional MRI scanning, mothers with secure attachment showed greater activation of brain reward regions, including the ventral striatum, and the oxytocin-associated hypothalamus/pituitary region. Peripheral oxytocin response to infant contact at 7 months was also significantly higher in secure mothers, and was positively correlated with brain activation in both regions. Insecure/dismissing mothers showed greater insular activation in response to their own infant's sad faces. These results suggest that individual differences in maternal attachment may be linked with development of the dopaminergic and oxytocinergic neuroendocrine systems.

Expectations shape our perception, profoundly influencing how we interpret the world. Positive expectations about sensory stimuli can alleviate distress and reduce pain (e.g., placebo effect), while negative expectations may heighten anxiety and exacerbate pain (e.g., nocebo effect). To investigate the impact of the (an)hedonic aspect of expectations on subjective experiences, we measured neurobehavioral responses to the taste of hot sauce among participants with heterogeneous taste preferences. By identifying participants who “liked” versus those who strongly “disliked” spicy flavors and by providing contextual cues about the spiciness of the sauce to be tasted, we dissociated the effects of positive and negative expectations from sensory stimuli (i.e., visual and gustatory stimuli), which were the same across all participants. Our results indicate that positive expectations lead to modulations in the intensity of subjective experience. These modulations were accompanied by increased activity in brain regions previously linked to information integration and the placebo effect, including the anterior insula, dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex, as well as a predefined “pleasure signature.” In contrast, negative expectations decreased hedonic experience and increased neural activity in the previously validated “Neurological Pain Signature” network. These findings demonstrate that hedonic aspects of one’s expectations asymmetrically shape how the brain processes sensory input and associated behavioral reports of one’s subjective experiences of intensity, pleasure, and pain. Our results suggest a dissociable impact of hedonic information: positive expectations facilitate higher-level information integration and reward processing, while negative expectations prime lower-level nociceptive and affective processes. This study demonstrates the powerful role of hedonic expectations in shaping subjective reality and suggests potential avenues for consumer and therapeutic interventions targeting expectation-driven neural processes.

The evolution of human cognitive function is reliant on complex social interactions which form the behavioural foundation of who we are. These social capacities are subject to dramatic change in disease and injury; yet their supporting neural substrates remain poorly understood. Hyperscanning employs functional neuroimaging to simultaneously assess brain activity in two individuals and offers the best means to understand the neural basis of social interaction. However, present technologies are limited, either by poor performance (low spatial/temporal precision) or an unnatural scanning environment (claustrophobic scanners, with interactions via video). Here, we describe hyperscanning using wearable magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs). We demonstrate our approach by simultaneously measuring brain activity in two subjects undertaking two separate tasks—an interactive touching task and a ball game. Despite large and unpredictable subject motion, sensorimotor brain activity was delineated clearly, and the correlation of the envelope of neuronal oscillations between the two subjects was demonstrated. Our results show that unlike existing modalities, OPM-MEG combines high-fidelity data acquisition and a naturalistic setting and thus presents significant potential to investigate neural correlates of social interaction.

In the mammalian brain, dopamine is a critical neuromodulator whose actions underlie learning, decision-making, and behavioral control. Degeneration of dopamine neurons causes Parkinson’s disease, whereas dysregulation of dopamine signaling is believed to contribute to psychiatric conditions such as schizophrenia, addiction, and depression. Experiments in animal models suggest the hypothesis that dopamine release in human striatum encodes reward prediction errors (RPEs) (the difference between actual and expected outcomes) during ongoing decision-making. Blood oxygen level-dependent (BOLD) imaging experiments in humans support the idea that RPEs are tracked in the striatum; however, BOLD measurements cannot be used to infer the action of any one specific neurotransmitter. We monitored dopamine levels with subsecond temporal resolution in humans (n = 17) with Parkinson’s disease while they executed a sequential decision-making task. Participants placed bets and experienced monetary gains or losses. Dopamine fluctuations in the striatum fail to encode RPEs, as anticipated by a large body of work in model organisms. Instead, subsecond dopamine fluctuations encode an integration of RPEs with counterfactual prediction errors, the latter defined by how much better or worse the experienced outcome could have been. How dopamine fluctuations combine the actual and counterfactual is unknown. One possibility is that this process is the normal behavior of reward processing dopamine neurons, which previously had not been tested by experiments in animal models. Alternatively, this superposition of error terms may result from an additional yet-to-be-identified subclass of dopamine neurons.

Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that \"could-have-been-experienced\" if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n = 54), we report that fictive learning signals strongly predict changes in subjects' investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice.

Cooperation and competition between human players in repeated microeconomic games offer a window onto social phenomena such as the establishment, breakdown and repair of trust. However, although a suitable starting point for the quantitative analysis of such games exists, namely the Interactive Partially Observable Markov Decision Process (I-POMDP), computational considerations and structural limitations have limited its application, and left unmodelled critical features of behavior in a canonical trust task. Here, we provide the first analysis of two central phenomena: a form of social risk-aversion exhibited by the player who is in control of the interaction in the game; and irritation or anger, potentially exhibited by both players. Irritation arises when partners apparently defect, and it potentially causes a precipitate breakdown in cooperation. Failing to model one's partner's propensity for it leads to substantial economic inefficiency. We illustrate these behaviours using evidence drawn from the play of large cohorts of healthy volunteers and patients. We show that for both cohorts, a particular subtype of player is largely responsible for the breakdown of trust, a finding which sheds new light on borderline personality disorder.

Generalising information from ourselves to others, and others to ourselves allows for both a dependable source of navigation and adaptability in interpersonal exchange. Disturbances to social development in sensitive periods can cause enduring and distressing damage to lasting healthy relationships. However, identifying the mechanisms of healthy exchange has been difficult. We introduce a theory of self-other generalisation tested with data from a three-phase social value orientation task – the Intentions Game. We involved humans with ( n =50) and without ( n =53) a diagnosis of borderline personality disorder and assessed whether infractions to self-other generalisation may explain prior findings of disrupted social learning and instability. Healthy controls initially used their preferences to predict others and were influenced by their partners, leading to self-other convergence. In contrast, individuals with borderline personality disorder maintained distinct self-other representations when learning about others. This allowed for equal predictive performance compared to controls despite reduced updating sensitivity. Furthermore, we explored theory-driven individual differences underpinning contagion. Overall, the findings provide a clear explanation of how self-other generalisation constrains and assists learning, and how childhood adversity is associated with separation of internalised beliefs. Our model makes clear predictions about the mechanisms of social information generalisation concerning both joint and individual reward.