Overestimating the intensity of negative feelings in autobiographical memory: evidence from the 9/11 attack and COVID-19 pandemic.

When recalling autobiographical events, people not only retrieve event details but also the feelings they experienced. The current study examined whether people are able to consistently recall the intensity of past feelings associated with two consequential and negatively valenced events, i.e. the 9/11 attack (N = 769) and the COVID-19 pandemic (N = 726). By comparing experienced and recalled intensities of negative feelings, we discovered that people systematically recall a higher intensity of negative feelings than initially reported - overestimating the intensity of past negative emotional experiences. The COVID-19 dataset also revealed that individuals who experienced greater improvement in emotional well-being displayed smaller biases in recalling their feelings. Across both datasets, the intensity of remembered feelings was correlated with initial feelings and current feelings, but the impact of the current feelings was stronger in the COVID-19 dataset than in the 9/11 dataset. Our results demonstrate that when recalling negative autobiographical events, people tend to overestimate the intensity of prior negative emotional experiences with their degree of bias influenced by current feelings and well-being.

Causal Inference Gates Corticostriatal Learning.

Attributing outcomes to your own actions or to external causes is essential for appropriately learning which actions lead to reward and which actions do not. Our previous work showed that this type of credit assignment is best explained by a Bayesian reinforcement learning model which posits that beliefs about the causal structure of the environment modulate reward prediction errors (RPEs) during action value updating. In this study, we investigated the brain networks underlying reinforcement learning that are influenced by causal beliefs using functional magnetic resonance imaging while human participants (n = 31; 13 males, 18 females) completed a behavioral task that manipulated beliefs about causal structure. We found evidence that RPEs modulated by causal beliefs are represented in dorsal striatum, while standard (unmodulated) RPEs are represented in ventral striatum. Further analyses revealed that beliefs about causal structure are represented in anterior insula and inferior frontal gyrus. Finally, structural equation modeling revealed effective connectivity from anterior insula to dorsal striatum. Together, these results are consistent with a possible neural architecture in which causal beliefs in anterior insula are integrated with prediction error signals in dorsal striatum to update action values.SIGNIFICANCE STATEMENT Learning which actions lead to reward-a process known as reinforcement learning-is essential for survival. Inferring the causes of observed outcomes-a process known as causal inference-is crucial for appropriately assigning credit to one's own actions and restricting learning to effective action-outcome contingencies. Previous studies have linked reinforcement learning to the striatum, and causal inference to prefrontal regions, yet how these neural processes interact to guide adaptive behavior remains poorly understood. Here, we found evidence that causal beliefs represented in the prefrontal cortex modulate action value updating in posterior striatum, separately from the unmodulated action value update in ventral striatum posited by standard reinforcement learning models.

The rational use of causal inference to guide reinforcement learning strengthens with age.

Beliefs about the controllability of positive or negative events in the environment can shape learning throughout the lifespan. Previous research has shown that adults' learning is modulated by beliefs about the causal structure of the environment such that they update their value estimates to a lesser extent when the outcomes can be attributed to hidden causes. This study examined whether external causes similarly influenced outcome attributions and learning across development. Ninety participants, ages 7 to 25 years, completed a reinforcement learning task in which they chose between two options with fixed reward probabilities. Choices were made in three distinct environments in which different hidden agents occasionally intervened to generate positive, negative, or random outcomes. Participants' beliefs about hidden-agent intervention aligned with the true probabilities of the positive, negative, or random outcome manipulation in each of the three environments. Computational modeling of the learning data revealed that while the choices made by both adults (ages 18-25) and adolescents (ages 13-17) were best fit by Bayesian reinforcement learning models that incorporate beliefs about hidden-agent intervention, those of children (ages 7-12) were best fit by a one learning rate model that updates value estimates based on choice outcomes alone. Together, these results suggest that while children demonstrate explicit awareness of the causal structure of the task environment, they do not implicitly use beliefs about the causal structure of the environment to guide reinforcement learning in the same manner as adolescents and adults.

Publisher Correction: Controllability governs the balance between Pavlovian and instrumental action selection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Controllability governs the balance between Pavlovian and instrumental action selection.

A Pavlovian bias to approach reward-predictive cues and avoid punishment-predictive cues can conflict with instrumentally-optimal actions. Here, we propose that the brain arbitrates between Pavlovian and instrumental control by inferring which is a better predictor of reward. The instrumental predictor is more flexible; it can learn values that depend on both stimuli and actions, whereas the Pavlovian predictor learns values that depend only on stimuli. The arbitration theory predicts that the Pavlovian predictor will be favored when rewards are relatively uncontrollable, because the additional flexibility of the instrumental predictor is not useful. Consistent with this hypothesis, we find that the Pavlovian approach bias is stronger under low control compared to high control contexts.

Causal Inference About Good and Bad Outcomes.

People learn differently from good and bad outcomes. We argue that valence-dependent learning asymmetries are partly driven by beliefs about the causal structure of the environment. If hidden causes can intervene to generate bad (or good) outcomes, then a rational observer will assign blame (or credit) to these hidden causes, rather than to the stable outcome distribution. Thus, a rational observer should learn less from bad outcomes when they are likely to have been generated by a hidden cause, and this pattern should reverse when hidden causes are likely to generate good outcomes. To test this hypothesis, we conducted two experiments ( N = 80, N = 255) in which we explicitly manipulated the behavior of hidden agents. This gave rise to both kinds of learning asymmetries in the same paradigm, as predicted by a novel Bayesian model. These results provide a mechanistic framework for understanding how causal attributions contribute to biased learning.

Neural Computations Underlying Causal Structure Learning.

Behavioral evidence suggests that beliefs about causal structure constrain associative learning, determining which stimuli can enter into association, as well as the functional form of that association. Bayesian learning theory provides one mechanism by which structural beliefs can be acquired from experience, but the neural basis of this mechanism is poorly understood. We studied this question with a combination of behavioral, computational, and neuroimaging techniques. Male and female human subjects learned to predict an outcome based on cue and context stimuli while being scanned using fMRI. Using a model-based analysis of the fMRI data, we show that structure learning signals are encoded in posterior parietal cortex, lateral prefrontal cortex, and the frontal pole. These structure learning signals are distinct from associative learning signals. Moreover, representational similarity analysis and information mapping revealed that the multivariate patterns of activity in posterior parietal cortex and anterior insula encode the full posterior distribution over causal structures. Variability in the encoding of the posterior across subjects predicted variability in their subsequent behavioral performance. These results provide evidence for a neural architecture in which structure learning guides the formation of associations.SIGNIFICANCE STATEMENT Animals are able to infer the hidden structure behind causal relations between stimuli in the environment, allowing them to generalize this knowledge to stimuli they have never experienced before. A recently published computational model based on this idea provided a parsimonious account of a wide range of phenomena reported in the animal learning literature, suggesting a dedicated neural mechanism for learning this hidden structure. Here, we validate this model by measuring brain activity during a task that involves both structure learning and associative learning. We show that a distinct network of regions supports structure learning and that the neural signal corresponding to beliefs about structure predicts future behavioral performance.

Disrupted Prefrontal Regulation of Striatal Subjective Value Signals in Psychopathy.

Psychopathy is a personality disorder with strong links to criminal behavior. While research on psychopathy has focused largely on socio-affective dysfunction, recent data suggest that aberrant decision making may also play an important role. Yet, the circuit-level mechanisms underlying maladaptive decision making in psychopathy remain unclear. Here, we used a multi-modality functional imaging approach to identify these mechanisms in a population of adult male incarcerated offenders. Psychopathy was associated with stronger subjective value-related activity within the nucleus accumbens (NAcc) during inter-temporal choice and with weaker intrinsic functional connectivity between NAcc and ventromedial prefrontal cortex (vmPFC). NAcc-vmPFC connectivity strength was negatively correlated with NAcc subjective value-related activity; however, this putative regulatory pattern was abolished as psychopathy severity increased. Finally, weaker cortico-striatal regulation predicted more frequent criminal convictions. These data suggest that cortico-striatal circuit dysregulation drives maladaptive decision making in psychopathy, supporting the notion that reward system dysfunction comprises an important neurobiological risk factor.

Selective Mapping of Psychopathy and Externalizing to Dissociable Circuits for Inhibitory Self-Control.

Antisociality is commonly conceptualized as a unitary construct, but there is considerable evidence for multidimensionality. In particular, two partially dissociable symptom clusters - psychopathy and externalizing - have divergent associations to clinical and forensic outcomes and are linked to unique patterns executive dysfunction. Here, we used fMRI in a sample of incarcerated offenders to map these dimensions of antisocial behavior to brain circuits underlying two aspects of inhibitory self-control: interference suppression and response inhibition. We found that psychopathy and externalizing are characterized by unique and task-selective patterns of dysfunction. While higher levels of psychopathy predicted increased activity within a distributed fronto-parietal network for interference suppression, externalizing did not predict brain activity during attentional control. By contrast, each dimension had opposite associations to fronto-parietal activity during response inhibition. These findings provide neurobiological evidence supporting the fractionation of antisocial behavior, and identify dissociable mechanisms through which different facets predispose dysfunction and impairment.

Social Behavior: A Penny for Your Shocks.

Antisocial behavior is an enormously costly social problem, but its origins are poorly understood. A new study shows that prosocial and antisocial behaviors arise from individual differences in how we represent the value of others' pain relative to our own potential gain, rather than from variability in the capacity for effortful inhibitory control.

Neurobiological mechanisms for impulsive-aggression: the role of MAOA.

Aggression may be present across a large part of the spectrum of psychopathology, and underlies costly criminal antisocial behaviors. Human aggression is a complex and underspecified construct, confounding scientific discovery. Nevertheless, some biologically tractable subtypes are apparent, and one in particular-impulsive (reactive) aggression-appears to account for many facets of aggression-related dysfunction in psychiatric illness. Impulsive-aggression is significantly heritable, suggesting genetic transmission. However, the specific neurobiological mechanisms that mediate genetic risk for impulsive-aggression remain unclear. Here, we review extant data on the genetics and neurobiology of individual differences in impulsive-aggression, with particular attention to the role of genetic variation in Monoamine Oxidase A (MAOA) and its impact on serotonergic signaling within corticolimbic circuitry.