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Symptom expression in psychiatric conditions is often linked to altered threat perception, however how computational mechanisms that support aversive learning relate to specific psychiatric symptoms remains undetermined. We answer this question using an online game-based aversive learning task together with measures of common psychiatric symptoms in 400 subjects. We show that physiological symptoms of anxiety and a transdiagnostic compulsivity-related factor are associated with enhanced safety learning, as measured using a probabilistic computational model, while trait cognitive anxiety symptoms are associated with enhanced learning from danger. We use data-driven partial least squares regression to identify two separable components across behavioural and questionnaire data: one linking enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linking enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Our findings implicate aversive learning processes in the expression of psychiatric symptoms that transcend diagnostic boundaries. Many psychiatric symptoms are linked to perceptions of danger, particularly when it is uncertain. Here, the authors show that a range of psychiatric symptoms are associated with the way people learn from safe and dangerous outcomes in the context of uncertainty.

Ability in various cognitive domains is often assessed by measuring task performance, such as the accuracy of a perceptual categorization. A similar analysis can be applied to metacognitive reports about a task to quantify the degree to which an individual is aware of his or her success or failure. Here, we review the psychological and neural underpinnings of metacognitive accuracy, drawing on research in memory and decision-making. These data show that metacognitive accuracy is dissociable from task performance and varies across individuals. Convergent evidence indicates that the function of the rostral and dorsal aspect of the lateral prefrontal cortex (PFC) is important for the accuracy of retrospective judgements of performance. In contrast, prospective judgements of performance may depend upon medial PFC. We close with a discussion of how metacognitive processes relate to concepts of cognitive control, and propose a neural synthesis in which dorsolateral and anterior prefrontal cortical subregions interact with interoceptive cortices (cingulate and insula) to promote accurate judgements of performance.

The subjective well-being or happiness of individuals is an important metric for societies. Although happiness is influenced by life circumstances and population demographics such as wealth, we know little about how the cumulative influence of daily life events are aggregated into subjective feelings. Using computational modeling, we show that emotional reactivity in the form of momentary happiness in response to outcomes of a probabilistic reward task is explained not by current task earnings, but by the combined influence of recent reward expectations and prediction errors arising from those expectations. The robustness of this account was evident in a large-scale replication involving 18,420 participants. Using functional MRI, we show that the very same influences account for task-dependent striatal activity in a manner akin to the influences underpinning changes in happiness.

This study reports that people are worse at incorporating negative information when updating their beliefs. Correspondingly, neural activity encodes desirable information updates, but there is weaker encoding of unexpectedly undesirable information. Unrealistic optimism is a pervasive human trait that influences domains ranging from personal relationships to politics and finance. How people maintain unrealistic optimism, despite frequently encountering information that challenges those biased beliefs, is unknown. We examined this question and found a marked asymmetry in belief updating. Participants updated their beliefs more in response to information that was better than expected than to information that was worse. This selectivity was mediated by a relative failure to code for errors that should reduce optimism. Distinct regions of the prefrontal cortex tracked estimation errors when those called for positive update, both in individuals who scored high and low on trait optimism. However, highly optimistic individuals exhibited reduced tracking of estimation errors that called for negative update in right inferior prefrontal gyrus. These findings indicate that optimism is tied to a selective update failure and diminished neural coding of undesirable information regarding the future.

Using a combination of fMRI in humans and computational modeling, the authors show that different striatal nuclei encode different kinds of decision-making information. The anterior caudate nucleus encodes the value of individual steps in a decision tree, whereas the putamen encodes values learnt during extensive training. Investigations of the underlying mechanisms of choice in humans have focused on learning from prediction errors, leaving the computational structure of value based planning comparatively underexplored. Using behavioral and neuroimaging analyses of a minimax decision task, we found that the computational processes underlying forward planning are expressed in the anterior caudate nucleus as values of individual branching steps in a decision tree. In contrast, values represented in the putamen pertain solely to values learned during extensive training. During actual choice, both striatal areas showed a functional coupling to ventromedial prefrontal cortex, consistent with this region acting as a value comparator. Our findings point toward an architecture of choice in which segregated value systems operate in parallel in the striatum for planning and extensively trained choices, with medial prefrontal cortex integrating their outputs.

The very earliest stages of sensory processing have the potential to alter how we perceive and respond to our environment. These initial processing circuits can incorporate subcortical regions, such as the thalamus and brainstem nuclei, which mediate complex interactions with the brain’s cortical processing hierarchy. These subcortical pathways, many of which we share with other animals, are not merely vestigial but appear to function as ‘shortcuts’ that ensure processing efficiency and preservation of vital life-preserving functions, such as harm avoidance, adaptive social interactions and efficient decision-making. Here, we propose that functional interactions between these higher-order and lower-order brain areas contribute to atypical sensory and cognitive processing that characterizes numerous neuropsychiatric disorders.Early processing in subcortical areas has been underemphasized in models of how perception and cognition are altered in psychiatric disorders. Here, McFadyen and colleagues review recent discoveries in how subcortical–cortical dynamics contribute to perception and higher-order cognition.

Significance Concern for the welfare of others is a key component of moral decision making and is disturbed in antisocial and criminal behavior. However, little is known about how people evaluate the costs of others’ suffering. Past studies have examined people’s judgments in hypothetical scenarios, but there is evidence that hypothetical judgments cannot accurately predict actual behavior. Here we addressed this issue by measuring how much money people will sacrifice to reduce the number of painful electric shocks delivered to either themselves or an anonymous stranger. Surprisingly, most people sacrifice more money to reduce a stranger’s pain than their own pain. This finding may help us better understand how people resolve moral dilemmas that commonly arise in medical, legal, and political decision making. Concern for the suffering of others is central to moral decision making. How humans evaluate others’ suffering, relative to their own suffering, is unknown. We investigated this question by inviting subjects to trade off profits for themselves against pain experienced either by themselves or an anonymous other person. Subjects made choices between different amounts of money and different numbers of painful electric shocks. We independently varied the recipient of the shocks (self vs. other) and whether the choice involved paying to decrease pain or profiting by increasing pain. We built computational models to quantify the relative values subjects ascribed to pain for themselves and others in this setting. In two studies we show that most people valued others’ pain more than their own pain. This was evident in a willingness to pay more to reduce others’ pain than their own and a requirement for more compensation to increase others’ pain relative to their own. This ”hyperaltruistic” valuation of others’ pain was linked to slower responding when making decisions that affected others, consistent with an engagement of deliberative processes in moral decision making. Subclinical psychopathic traits correlated negatively with aversion to pain for both self and others, in line with reports of aversive processing deficits in psychopathy. Our results provide evidence for a circumstance in which people care more for others than themselves. Determining the precise boundaries of this surprisingly prosocial disposition has implications for understanding human moral decision making and its disturbance in antisocial behavior.

The hippocampal–entorhinal system encodes a map of space that guides spatial navigation. Goal-directed behaviour outside of spatial navigation similarly requires a representation of abstract forms of relational knowledge. This information relies on the same neural system, but it is not known whether the organisational principles governing continuous maps may extend to the implicit encoding of discrete, non-spatial graphs. Here, we show that the human hippocampal–entorhinal system can represent relationships between objects using a metric that depends on associative strength. We reconstruct a map-like knowledge structure directly from a hippocampal–entorhinal functional magnetic resonance imaging adaptation signal in a situation where relationships are non-spatial rather than spatial, discrete rather than continuous, and unavailable to conscious awareness. Notably, the measure that best predicted a behavioural signature of implicit knowledge and blood oxygen level-dependent adaptation was a weighted sum of future states, akin to the successor representation that has been proposed to account for place and grid-cell firing patterns. To help us navigate, the brain encodes information about the positions of landmarks in space in a series of maps. These maps are housed by two neighbouring brain regions called the hippocampus and entorhinal cortex. These regions also encode information about non-spatial relationships, for example, between two events that often occur close together in time. However, it was not known whether such non-spatial relationships may also be encoded as a map. To address this question, Garvert et al. showed volunteers a series of objects on a screen. Unbeknown to the volunteers, the order of the objects was not entirely random. Instead, each object could only follow certain others. The objects were thus connected to one another by a network of non-spatial relationships, broadly comparable to the spatial relationships that connect physical locations in the environment. The next day, the volunteers viewed some of the objects again, this time while lying inside a brain scanner. Although the volunteers still believed that the objects had been presented at random, the activity of their hippocampus and entorhinal cortex reflected the non-spatial relationships volunteers had experienced between the objects. The relationships were organised in an abstract map. This suggests that the brain organises knowledge about abstract non-spatial relationships into maps comparable to those used to represent spatial relationships. The brain can use these maps of non-spatial relationships to guide our behaviour, even though we have no conscious awareness of the information they contain. The maps may also enable us to make new inferences, just as we can use our spatial maps to find short cuts or navigate around obstacles. Future studies should investigate the mechanisms underlying our ability to create maps of non-spatial relationships and how we use them to guide decision making.

Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.

The ability to introspect about self-performance is key to human subjective experience, but the neuroanatomical basis of this ability is unknown. Such accurate introspection requires discriminating correct decisions from incorrect ones, a capacity that varies substantially across individuals. We dissociated variation in introspective ability from objective performance in a simple perceptualdecision task, allowing us to determine whether this interindividual variability was associated with a distinct neural basis. We show that introspective ability is correlated with gray matter volume in the anterior prefrontal cortex, a region that shows marked evolutionary development in humans. Moreover, interindividual variation in introspective ability is also correlated with white-matter microstructure connected with this area of the prefrontal cortex. Our findings point to a focal neuroanatomical substrate for introspective ability, a substrate distinct from that supporting primary perception.