Forming Beliefs: Why Valence Matters

How the discrepancy between expectation and outcome influences expectation change in relation to depressive symptoms - Results from four experimental studies

BACKGROUND

Traditional associative learning models and their clinical applications suggest that the greater the discrepancy between expectations and outcome, the greater the adjustment of expectations. Other frameworks, however, propose a "tipping point", above which the discrepancy is so large that the source of information is considered unreliable, resulting in reduced expectation change.

METHODS

To test these competing predictions, we performed four experiments in which participants were provided with five levels of positivity/negativity in two different contexts: social interactions and performance feedback. We also examined how depressive symptoms were related to changes in expectations in response to different levels of positive/negative information.

RESULTS

The results regarding the processing of unexpectedly positive information (Study 1, N = 212; Study 2, N = 158) did not allow unanimous conclusions, but were more consistent with the tipping point hypothesis, as there was strong expectation change when information was moderately positive and a decline in expectation change when information was even more positive. For negative information (Study 3, N = 252; Study 4, N = 286), the results were more consistent with the linearity hypothesis, indicating that the more negative the information, the greater the change in expectations. Depressive symptoms interacted with the level of positive information in affecting expectation change: Depressive symptoms were associated with lower expectation change in response to mildly positive information, but not in response to highly positive information, particularly in Study 2.

CONCLUSIONS

The results suggest that the relationship between the magnitude of an expectation violation and expectation change may depend on the valence of the expectation violation.

How people with major depression adjust their expectations of future life events in response to other patients' reports of the positive effects of psychotherapy

Research proposed the devaluation of positive information, referred to as cognitive immunisation, as a mechanism underlying the persistence of negative expectations in depression. In a pre-registered experimental study, we tested this hypothesis by enhancing vs. inhibiting the engagement in cognitive immunisation and comparing it with a distraction control condition and a no-instruction control condition. In a between-subjects design, we presented participants with major depression (N = 156) with video recordings of other patients in which they reported on the positive effects of psychotherapy. Cognitive immunisation was manipulated by instructing participants to focus on similarities vs. differences between themselves and the persons from the videos. The results show that the other patients' reports led to a significant increase in participants' expectations of future life events and treatment expectations. However, there were no significant differences between the experimental conditions in expectation change, most likely because the manipulation was not powerful enough according to the manipulation check. Only in men the cognitive immunisation-promoting condition effectively blocked expectation change, according to exploratory analyses. This study shows that watching positive reports from other patients helps people with major depression improve their pre-treatment expectations. However, cognitive immunisation does not modulate this process, unless gender is considered.

"Don't stop believing" - Decoding belief dynamics in the brain: An ALE meta-analysis of neural correlates in belief formation and updating

Understanding how individuals form and update their beliefs is a fundamental question in cognitive and social psychology. Belief formation (BF) refers to the initial development of an individual's belief, while belief updating (BU) pertains to the revision of existing beliefs in response to contradictory evidence. Although these two processes are often interwoven, they might operate through different neural mechanisms. This meta-analysis aims to synthesize the existing functional magnetic resonance imaging (fMRI) literature on BF and BU, with a particular focus on how BF is investigated. Approaches based on Theory of Mind paradigms, such as False Belief tasks, are often opposed to other approaches, emphasizing the role of individual or situational factors in belief formation. Notably, we propose that this differentiation might reflect the engagement of social and non-social dynamics within belief formation. Activation likelihood estimation (ALE) analysis revealed shared involvement of the Precuneus (PCu) in both BF and BU, while BF specifically engaged the bilateral activation of the Temporo-Parietal Junctions (TPJ). Additionally, social and non-social BF exhibited distinct neural correlates: social BF was associated with activity in the right TPJ, whereas non-social BF relied on the left dorsolateral prefrontal cortex (DLPFC). These findings support the hypothesis that BF and BU operate via partially dissociable neural networks and highlights the role of TPJ and PCu as essential hubs to build-up neural templates and enabling shifts in viewpoint necessary to adapt beliefs.

Selective Integration of Social Feedback Promotes a Stable and Positively Biased Self-Concept

Understanding self-concept dynamics is crucial given its generalized impact on our well-being. However, how we integrate information into our self-representations to promote a positively biased, yet progressively stable self-concept is a question that remains unanswered. In a series of four experiments, we refined a belief updating task to investigate how participants integrate social feedback depending on its valence and self-congruence. Experiment 1 indicated that the lack of control of an initial positive bias in participants self-concept might have masked valence and congruence effects in recent works. After implementing methodological adjustments (Experiments 2 and 3) we found that the integration of social feedback was strongly driven by feedback self-congruence and moderately driven by feedback valence. By synthesizing insights from social, personality, and cognitive psychology, this study advances the understanding of self-concept dynamics during social feedback processing. Our conceptual and methodological advancements offer a new lens for reinterpreting previous findings.

Self-utility distance as a computational approach to understanding self-concept clarity

Self-concept stability and cohesion are crucial for psychological functioning and well-being, yet the mechanisms that underpin this fundamental aspect of human cognition remain underexplored. Integrating insights from cognitive and personality psychology with reinforcement learning, we introduce Self-Utility Distance (SUD)-a metric quantifying the dissimilarities between individuals' self-concept attributes and their expected utility value. In Study 1 (n = 155), participants provided self- and expected utility ratings using a set of predefined adjectives. SUD showed a significant negative relationship with Self-Concept Clarity that persisted after accounting for individuals' Self-Esteem. In Study 2 (n = 323), we found that SUD provides incremental predictive accuracy over Ideal-Self and Ought-Self discrepancies in the prediction of Self-Concept Clarity. In Study 3 (n = 85), we investigated the mechanistic principles underlying Self-Utility Distance. Participants conducted a social learning task where they learned about trait utilities from a reference group. We formalized different computational models to investigate the strategies individuals use to adjust trait utility estimates in response to environmental feedback. Through Hierarchical Bayesian Inference, we found evidence that participants utilized their self-concept to modulate trait utility learning, effectively avoiding the maximization of Self-Utility Distance. Our findings provide insights into self-concept dynamics that might help understand the maintenance of adaptive and maladaptive traits.

Cultural Differences in Vicarious Optimism

Prosocial behavior is affected by the beliefs people have about others. The maintenance of these beliefs is biased: people are vicariously optimistic when updating beliefs about the future of others they care about. This vicarious optimism influences prosocial behavior and might be shaped by the culture people live in. We investigated this question by measuring learning from good and bad news for oneself, friends, and strangers in a pre-registered study of Chinese and American participants (N = 963). American participants showed a stronger bias for better-than-expected "good news" over worse-than-expected "bad news" when learning about themselves (optimism bias) and others (vicarious optimism) compared to Chinese participants. Chinese participants showed vicarious optimism only when learning about a friend, while American participants showed the most vicarious optimism when learning about an identifiable stranger. Our results provide evidence that culture might shape social behavior by biasing learning about the future of others.

Anhedonia Across and Beyond the Schizophrenia Spectrum

Anhedonia refers to the diminished ability to experience pleasure, and is a core feature of schizophrenia (SCZ). The neurocognitive and neural correlates of anhedonia remain elusive. Based on several influential theoretical models for negative symptoms, this selective review proposed four important neurocognitive domains, which may unveil the neurobiological mechanisms of anhedonia. The authors critically reviewed the current evidence regarding value representation of reward, prospection, emotion-behavior decoupling, and belief updating in the Chinese setting, covering both behavioral and neuroimaging research. We observed a limited application of the transdiagnostic approach in previous studies on the four domains, and the lack of adequate measures to tap into the expressivity deficit in SCZ. Despite many behavioral paradigms for these four domains utilized both social and non-social stimuli, previous studies seldom focused on the social-versus-non-social differentiation. We further advocated several important directions for future research.

From relaxed beliefs under psychedelics (REBUS) to revised beliefs after psychedelics (REBAS)

The Relaxed Beliefs Under pSychedelics (REBUS) model proposes that serotonergic psychedelics decrease the precision weighting of neurobiologically-encoded beliefs. We conducted a preliminary examination of two psychological assumptions of REBUS: (a) psychedelics foster acute relaxation and post-acute revision of confidence in mental-health-relevant beliefs; which (b) facilitate positive therapeutic outcomes and are associated with the entropy of EEG signals. Healthy individuals (N = 11) were administered 1 mg and 25 mg psilocybin 4-weeks apart. Confidence ratings for personally held beliefs were obtained before, during, and 4-weeks post-psilocybin. Acute entropy and subjective experiences were measured, as was well-being (before and 4-weeks post-psilocybin). Confidence in negative self-beliefs decreased following 25 mg psilocybin. Entropy and subjective effects under 25 mg psilocybin correlated with decreases in negative self-belief confidence (acutely and at 4-weeks). Particularly strong evidence was seen for a relationship between decreases in negative self-belief confidence and increases in well-being. We report the first empirical evidence that the relaxation and revision of negative self-belief confidence mediates psilocybin's positive psychological outcomes, and provide tentative evidence for a neuronal mechanism, namely, increased neuronal entropy. Replication within larger and clinical samples is necessary. We also introduce a new measure for examining the robustness of these preliminary findings and the utility of the REBUS model.

"It's safer to believe that others don't like me" - A qualitative study on the paradoxical value of negative core beliefs in depression

Depression is related to difficulty revising established negative self-beliefs in response to novel positive experiences. This propensity is in some way paradoxical because negative beliefs usually have many obvious negative consequences for the individual (e.g., feeling upset). Using a qualitative approach, the present study sought to explore what makes such negative self-beliefs valuable from the patients' perspectives. In 14 patients with major depression, we conducted semi-structured interviews that explored the perceived benefits of retaining an individually specified negative core belief as well as the perceived costs of changing it. In a deductive-inductive approach based on a recent theoretical model of the value of beliefs, we found eight themes that may explain why people with depression uphold negative beliefs (intercoder agreement: κ = .81): expectation management, certainty and control, avoiding cognitive dissonance, adaptivity in the past, protection of higher values, attachment and belonging, saving resources, short-term counterevidence. The two most frequently mentioned themes were that retaining negative beliefs helps patients sustain certainty and keep expectations low to prevent future disappointments. While previous research has advanced the understanding of how (i.e., through which mechanisms) people with depression maintain negative self-beliefs, the present study provides novel insights into why they do so.

The critical role of emotional communication for motivated reasoning

Persuasive appeals frequently prove ineffective or produce unintended outcomes, due to the presence of motivated reasoning. Using the example of electric cars adoption, this research delves into the impact of emotional content, message valence, and the coherence of pre-existing attitudes on biased information evaluation. By conducting a factorial survey (N = 480) and incorporating a computational model of attitude formation, we aim to gain a deeper insight into the cognitive-affective mechanisms driving motivated reasoning. Our experimental findings reveal that motivated reasoning is most pronounced when persuasive appeals employ a combination of emotional and rational elements within a negatively valenced argument. Furthermore, our computational model, which estimates belief and affect adjustments underlying attitude changes, elucidates how message framing influences cognitive-affective processes through emotional coherence. The results provide support for a negative correlation between shifts in coherence in response to new information and the propensity for motivated reasoning. The research contributes to computational models of opinion dynamics and social influence, offering a psychologically realistic framework for exploring the impact of individual reasoning on population-level dynamics, particularly in policy contexts, where it can enhance communication and informed policy discussions.

Striatal-hippocampal functional connectivity contributes to real-life positive anticipatory experiences and subjective well-being

Positive anticipatory experiences are key to daily well-being. However, the brain's functional architecture underlying real-world positive anticipatory experiences and well-being remains unexplored. In the present study, we combined an ecological momentary assessment and resting-state functional neuroimaging to identify the neural predictors of real-world positive anticipatory experiences and explore their relationships with subjective well-being (SWB). With a model-based approach, we quantified participants' accuracy in predicting positive events and the degree to which participants' affective states were influenced by the positive anticipation. We found that individuals with higher accuracy in predicting upcoming positive events showed greater SWB, and this relationship was mediated by greater positive anticipatory feelings. Importantly, functional connectivity of the dorsal and ventral striatal-hippocampal networks significantly predicted the accuracy and positive anticipatory feelings, respectively. These functional networks were further predictive of SWB. Our findings provide novel and ecologically valid evidence that the interaction between neural systems for reward-processing and memory plays an important role in real-life positive anticipatory experiences and everyday SWB.

Beyond the Positivity Bias: The Processing and Integration of Self-Relevant Feedback Is Driven by Its Alignment With Pre-Existing Self-Views

Our self-concept is constantly faced with self-relevant information. Prevailing research suggests that information's valence plays a central role in shaping our self-views. However, the need for stability within the self-concept structure and the inherent alignment of positive feedback with the pre-existing self-views of healthy individuals might mask valence and congruence effects. In this study (N = 30, undergraduates), we orthogonalized feedback valence and self-congruence effects to examine the behavioral and electrophysiological signatures of self-relevant feedback processing and self-concept updating. We found that participants had a preference for integrating self-congruent and dismissing self-incongruent feedback, regardless of its valence. Consistently, electroencephalography results revealed that feedback congruence, but not feedback valence, is rapidly detected during early processing stages. Our findings diverge from the accepted notion that self-concept updating is based on the selective incorporation of positive information. These findings offer novel insights into self-concept dynamics, with implications for the understanding of psychopathological conditions.

Factors influencing the update of beliefs regarding controversial political issues

Selectively integrating new information contributes to belief polarization and compromises public discourse. To better understand factors that underlie biased belief updating, I conducted three pre-registered studies covering different controversial political issues. The main hypothesis was that cognitively devaluing new information hinders belief updating. Support for this hypothesis was found in only one of the three issues. The only factor that consistently influenced belief updating across issues was the discrepancy between prior beliefs and new information. These results suggest that usually people do use evidence to correct their beliefs, but may refuse to do so if doubts about its generalizability arise.

Cardiac-Sympathetic Contractility and Neural Alpha-Band Power: Cross-Modal Collaboration during Approach-Avoidance Conflict

As evidence mounts that the cardiac-sympathetic nervous system reacts to challenging cognitive settings, we ask if these responses are epiphenomenal companions or if there is evidence suggesting a more intertwined role of this system with cognitive function. Healthy male and female human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous electroencephalographic and cardiac-sympathetic signals. Participants were reward sensitive but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters suggested that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics were consistent with widening decision boundaries serving to combat reward sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically mediated positive inotropy). We also saw evidence of conflict-specific "collaboration" between the neural and cardiac-sympathetic signals. In states of high conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signal's singular association. Cross-trial coherence analyses provided additional evidence that the autonomic systems controlling cardiac-sympathetics might influence the assessment of information streams during conflict by disrupting or overriding reward processing. We conclude that cardiac-sympathetic control might play a critical role, in collaboration with cognitive processes, during the approach-avoidance conflict in humans.

Nonlinear age-related differences in probabilistic learning in mice: A 5-armed bandit task study

This study explores the impact of aging on reinforcement learning in mice, focusing on changes in learning rates and behavioral strategies. A 5-armed bandit task (5-ABT) and a computational Q-learning model were used to evaluate the positive and negative learning rates and the inverse temperature across three age groups (3, 12, and 18 months). Results showed a significant decline in the negative learning rate of 18-month-old mice, which was not observed for the positive learning rate. This suggests that older mice maintain the ability to learn from successful experiences while decreasing the ability to learn from negative outcomes. We also observed a significant age-dependent variation in inverse temperature, reflecting a shift in action selection policy. Middle-aged mice (12 months) exhibited higher inverse temperature, indicating a higher reliance on previous rewarding experiences and reduced exploratory behaviors, when compared to both younger and older mice. This study provides new insights into aging research by demonstrating that there are age-related differences in specific components of reinforcement learning, which exhibit a non-linear pattern.

Tracking politically motivated reasoning in the brain: the role of mentalizing, value-encoding, and error detection networks

Susceptibility to misinformation and belief polarization often reflects people's tendency to incorporate information in a biased way. Despite the presence of competing theoretical models, the underlying neurocognitive mechanisms of motivated reasoning remain elusive as previous empirical work did not properly track the belief formation process. To address this problem, we employed a design that identifies motivated reasoning as directional deviations from a Bayesian benchmark of unbiased belief updating. We asked the members of a proimmigration or an anti-immigration group regarding the extent to which they endorse factual messages on foreign criminality, a polarizing political topic. Both groups exhibited a desirability bias by overendorsing attitude-consistent messages and underendorsing attitude-discrepant messages and an identity bias by overendorsing messages from in-group members and underendorsing messages from out-group members. In both groups, neural responses to the messages predicted subsequent expression of desirability and identity biases, suggesting a common neural basis of motivated reasoning across ideologically opposing groups. Specifically, brain regions implicated in encoding value, error detection, and mentalizing tracked the degree of desirability bias. Less extensive activation in the mentalizing network tracked the degree of identity bias. These findings illustrate the distinct neurocognitive architecture of desirability and identity biases and inform existing cognitive models of politically motivated reasoning.

Hooking the Self Onto the Past: How Positive Autobiographical Memory Retrieval Benefits People With Social Anxiety

Do people with social anxiety (SA) benefit from positive memory retrieval that heightens self-relevant meaning? In this preregistered study, an analog sample of 255 participants with self-reported clinically significant symptoms of SA were randomly assigned to retrieve and process a positive social-autobiographical memory by focusing on either its self-relevant meaning (deep processing) or its perceptual features (superficial processing). Participants were then socially excluded and instructed to reimagine their positive memory. Analyses revealed that participants assigned to the deep processing condition experienced significantly greater improvements than participants in the superficial processing condition in positive affect, social safeness, and positive beliefs about others during initial memory retrieval and in negative and positive beliefs about the self following memory reactivation during recovery from exclusion. These novel findings highlight the potential utility of memory-based interventions for SA that work by "hooking" self-meaning onto recollections of positive interpersonal experiences that elicit feelings of social acceptance.

Neurobiology and systems biology of stress resilience

Stress resilience is the phenomenon that some people maintain their mental health despite exposure to adversity or show only temporary impairments followed by quick recovery. Resilience research attempts to unravel the factors and mechanisms that make resilience possible and to harness its insights for the development of preventative interventions in individuals at risk for acquiring stress-related dysfunctions. Biological resilience research has been lagging behind the psychological and social sciences but has seen a massive surge in recent years. At the same time, progress in this field has been hampered by methodological challenges related to finding suitable operationalizations and study designs, replicating findings, and modeling resilience in animals. We embed a review of behavioral, neuroimaging, neurobiological, and systems biological findings in adults in a critical methods discussion. We find preliminary evidence that hippocampus-based pattern separation and prefrontal-based cognitive control functions protect against the development of pathological fears in the aftermath of singular, event-type stressors [as found in fear-related disorders, including simpler forms of posttraumatic stress disorder (PTSD)] by facilitating the perception of safety. Reward system-based pursuit and savoring of positive reinforcers appear to protect against the development of more generalized dysfunctions of the anxious-depressive spectrum resulting from more severe or longer-lasting stressors (as in depression, generalized or comorbid anxiety, or severe PTSD). Links between preserved functioning of these neural systems under stress and neuroplasticity, immunoregulation, gut microbiome composition, and integrity of the gut barrier and the blood-brain barrier are beginning to emerge. On this basis, avenues for biological interventions are pointed out.

Large environmental changes reduce valence-dependent belief updating

When updating beliefs, humans tend to integrate more desirable information than undesirable information. In stable environments (low uncertainty and high predictability), this asymmetry favors motivation towards action and perceived self-efficacy. However, in changing environments (high uncertainty and low predictability), this process can lead to risk underestimation and increase unwanted costs. Here, we examine how people (n = 388) integrate threatening information during an abrupt environmental change (mandatory quarantine during the COVID-19 pandemic). Given that anxiety levels are associated with the magnitude of the updating belief asymmetry; we explore its relationship during this particular context. We report a significant reduction in asymmetrical belief updating during a large environmental change as individuals integrated desirable and undesirable information to the same extent. Moreover, this result was supported by computational modeling of the belief update task. However, we found that the reduction in asymmetrical belief updating was not homogeneous among people with different levels of Trait-anxiety. Individuals with higher levels of Trait-anxiety maintained a valence-dependent updating, as it occurs in stable environments. On the other hand, updating behavior was not associated with acute anxiety (State-Anxiety), health concerns (Health-Anxiety), or having positive expectations (Trait-Optimism). These results suggest that highly uncertain environments can generate adaptive changes in information integration. At the same time, it reveals the vulnerabilities of individuals with higher levels of anxiety to adapt the way they learn.

Negative mood and optimism bias: An experimental investigation of sadness and belief updating

BACKGROUND AND OBJECTIVES

Understanding how individuals integrate new information to form beliefs under changing emotional conditions is crucial to describing decision-making processes. Previous research suggests that although most people demonstrate bias toward optimistic appraisals of new information when updating beliefs, individuals with dysphoric psychiatric conditions (e.g., major depression) do not demonstrate this same bias. Despite these findings, limited research has investigated the relationship between affective states and belief updating processes.

METHODS

We induced neutral and sad moods in participants and had them complete a belief-updating paradigm by estimating the likelihood of negative future events happening to them, viewing the actual likelihood, and then re-estimating their perceived likelihood.

RESULTS

We observed that individuals updated their beliefs more after receiving desirable information relative to undesirable information under neutral conditions. Further, we found that individuals did not demonstrate unrealistic optimism under negative affective conditions.

LIMITATIONS

This study incorporated a population of university students under laboratory conditions and would benefit from replication and extension in clinical populations and naturalistic settings.

CONCLUSIONS

These findings suggest that momentary fluctuations in mood affect how individuals integrate information to form beliefs.

Motivated Cognition in Cooperation

Successful cooperation is tightly linked to individuals' beliefs about their interaction partners, the decision setting, and existing norms, perceptions, and values. This article reviews and integrates findings from judgment and decision-making, social and cognitive psychology, political science, and economics, developing a systematic overview of the mechanisms underlying motivated cognition in cooperation. We elaborate on how theories and concepts related to motivated cognition developed in various disciplines define the concept and describe its functionality. We explain why beliefs play such an essential role in cooperation, how they can be distorted, and how this fosters or harms cooperation. We also highlight how individual differences and situational factors change the propensity to engage in motivated cognition. In the form of a construct map, we provide a visualization of the theoretical and empirical knowledge structure regarding the role of motivated cognition, including its many interdependencies, feedback loops, and moderating influences. We conclude with a brief suggestion for a future research agenda based on this compiled evidence.

Dynamics Learning Rate Bias in Pigeons: Insights from Reinforcement Learning and Neural Correlates

Research in reinforcement learning indicates that animals respond differently to positive and negative reward prediction errors, which can be calculated by assuming learning rate bias. Many studies have shown that humans and other animals have learning rate bias during learning, but it is unclear whether and how the bias changes throughout the entire learning process. Here, we recorded the behavior data and the local field potentials (LFPs) in the striatum of five pigeons performing a probabilistic learning task. Reinforcement learning models with and without learning rate biases were used to dynamically fit the pigeons' choice behavior and estimate the option values. Furthemore, the correlation between the striatal LFPs power and the model-estimated option values was explored. We found that the pigeons' learning rate bias shifted from negative to positive during the learning process, and the striatal Gamma (31 to 80 Hz) power correlated with the option values modulated by dynamic learning rate bias. In conclusion, our results support the hypothesis that pigeons employ a dynamic learning strategy in the learning process from both behavioral and neural aspects, providing valuable insights into reinforcement learning mechanisms of non-human animals.

Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study

BACKGROUND

The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation.

METHODS

We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen).

RESULTS

The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores.

CONCLUSIONS

By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

Neurocognitive Model of Schema-Congruent and -Incongruent Learning in Clinical Disorders: Application to Social Anxiety and Beyond

Negative schemas lie at the core of many common and debilitating mental disorders. Thus, intervention scientists and clinicians have long recognized the importance of designing effective interventions that target schema change. Here, we suggest that the optimal development and administration of such interventions can benefit from a framework outlining how schema change occurs in the brain. Guided by basic neuroscientific findings, we provide a memory-based neurocognitive framework for conceptualizing how schemas emerge and change over time and how they can be modified during psychological treatment of clinical disorders. We highlight the critical roles of the hippocampus, ventromedial prefrontal cortex, amygdala, and posterior neocortex in directing schema-congruent and -incongruent learning (SCIL) in the interactive neural network that comprises the autobiographical memory system. We then use this framework, which we call the SCIL model, to derive new insights about the optimal design features of clinical interventions that aim to strengthen or weaken schema-based knowledge through the core processes of episodic mental simulation and prediction error. Finally, we examine clinical applications of the SCIL model to schema-change interventions in psychotherapy and provide cognitive-behavior therapy for social anxiety disorder as an illustrative example.

Signed and unsigned effects of prediction error on memory: Is it a matter of choice?

Adaptive decision-making is governed by at least two types of memory processes. On the one hand, learned predictions through integrating multiple experiences, and on the other hand, one-shot episodic memories. These two processes interact, and predictions - particularly prediction errors - influence how episodic memories are encoded. However, studies using computational models disagree on the exact shape of this relationship, with some findings showing an effect of signed prediction errors and others showing an effect of unsigned prediction errors on episodic memory. We argue that the choice-confirmation bias, which reflects stronger learning from choice-confirming compared to disconfirming outcomes, could explain these seemingly diverging results. Our perspective implies that the influence of prediction errors on episodic encoding critically depends on whether people can freely choose between options (i.e., instrumental learning tasks) or not (Pavlovian learning tasks). The choice-confirmation bias on memory encoding might have evolved to prioritize memory representations that optimize reward-guided decision-making. We conclude by discussing open issues and implications for future studies.

Neural correlates of valence-dependent belief and value updating during uncertainty reduction: An fNIRS study

Selective use of new information is crucial for adaptive decision-making. Combining a gamble bidding task with assessing cortical responses using functional near-infrared spectroscopy (fNIRS), we investigated potential effects of information valence on behavioral and neural processes of belief and value updating during uncertainty reduction in young adults. By modeling changes in the participants' expressed subjective values using a Bayesian model, we dissociated processes of (i) updating beliefs about statistical properties of the gamble, (ii) updating values of a gamble based on new information about its winning probabilities, as well as (iii) expectancy violation. The results showed that participants used new information to update their beliefs and values about the gambles in a quasi-optimal manner, as reflected in the selective updating only in situations with reducible uncertainty. Furthermore, their updating was valence-dependent: information indicating an increase in winning probability was underweighted, whereas information about a decrease in winning probability was updated in good agreement with predictions of the Bayesian decision theory. Results of model-based and moderation analyses showed that this valence-dependent asymmetry was associated with a distinct contribution of expectancy violation, besides belief updating, to value updating after experiencing new positive information regarding winning probabilities. In line with the behavioral results, we replicated previous findings showing involvements of frontoparietal brain regions in the different components of updating. Furthermore, this study provided novel results suggesting a valence-dependent recruitment of brain regions. Individuals with stronger oxyhemoglobin responses during value updating was more in line with predictions of the Bayesian model while integrating new information that indicates an increase in winning probability. Taken together, this study provides first results showing expectancy violation as a contributing factor to sub-optimal valence-dependent updating during uncertainty reduction and suggests limitations of normative Bayesian decision theory.

Dissociated modulations of intranasal vasopressin on prosocial learning between reward-seeking and punishment-avoidance

BACKGROUND

As an integral ingredient of human sociality, prosocial behavior requires learning what acts can benefit or harm others. However, it remains unknown how individuals adjust prosocial learning to avoid punishment or to pursue reward. Given that arginine vasopressin (AVP) is a neuropeptide that has been involved in modulating various social behaviors in mammals, it could be a crucial neurochemical facilitator that supports prosocial learning.

METHODS

In 50 placebo controls and 54 participants with AVP administration, we examined the modulation of AVP on the prosocial learning characterized by reward and punishment framework, as well as its underlying neurocomputational mechanisms combining computational modeling, event-related potentials and oscillations.

RESULTS

We found a self-bias that individuals learn to avoid punishment asymmetrically more severely than reward-seeking. Importantly, AVP increased behavioral performances and learning rates when making decisions to avoid losses for others and to obtain gains for self. These behavioral effects were underpinned by larger responses of stimulus-preceding negativity (SPN) to anticipation, as well as higher punishment-related feedback-related negativity (FRN) for prosocial learning and reward-related P300 for proself benefits, while FRN and P300 neural processes were integrated into theta (4-7 Hz) oscillation at the outcome evaluation stage.

CONCLUSIONS

These results suggest that AVP context-dependently up-regulates altruism for concerning others' losses and reward-seeking for self-oriented benefits. Our findings provide insight into the selectively modulatory roles of AVP in prosocial behaviors depending on learning contexts between proself reward-seeking and prosocial punishment-avoidance.

Neural dissociation between reward and salience prediction errors through the lens of optimistic bias

The question of how the brain represents reward prediction errors is central to reinforcement learning and adaptive, goal-directed behavior. Previous studies have revealed prediction error representations in multiple electrophysiological signatures, but it remains elusive whether these electrophysiological correlates underlying prediction errors are sensitive to valence (in a signed form) or to salience (in an unsigned form). One possible reason concerns the loose correspondence between objective probability and subjective prediction resulting from the optimistic bias, that is, the tendency to overestimate the likelihood of encountering positive future events. In the present electroencephalography (EEG) study, we approached this question by directly measuring participants' idiosyncratic, trial-to-trial prediction errors elicited by subjective and objective probabilities across two experiments. We adopted monetary gain and loss feedback in Experiment 1 and positive and negative feedback as communicated by the same zero-value feedback in Experiment 2. We provided electrophysiological evidence in time and time-frequency domains supporting both reward and salience prediction error signals. Moreover, we showed that these electrophysiological signatures were highly flexible and sensitive to an optimistic bias and various forms of salience. Our findings shed new light on multiple presentations of prediction error in the human brain, which differ in format and functional role.

A belief systems analysis of fraud beliefs following the 2020 US election

Beliefs that the US 2020 Presidential election was fraudulent are prevalent despite substantial contradictory evidence. Why are such beliefs often resistant to counter-evidence? Is this resistance rational, and thus subject to evidence-based arguments, or fundamentally irrational? Here we surveyed 1,642 Americans during the 2020 vote count, testing fraud belief updates given hypothetical election outcomes. Participants' fraud beliefs increased when their preferred candidate lost and decreased when he won, and both effects scaled with partisan preferences, demonstrating partisan asymmetry (desirability effects). A Bayesian model of rational updating of a system of beliefs-beliefs in the true vote winner, fraud prevalence and beneficiary of fraud-accurately accounted for this partisan asymmetry, outperforming alternative models of irrational, motivated updating and models lacking the full belief system. Partisan asymmetries may not reflect motivated reasoning, but rather rational attributions over multiple potential causes of evidence. Changing such beliefs may require targeting multiple key beliefs simultaneously rather than direct debunking attempts.

The shadowing effect of initial expectation on learning asymmetry

Evidence for positivity and optimism bias abounds in high-level belief updates. However, no consensus has been reached regarding whether learning asymmetries exist in more elementary forms of updates such as reinforcement learning (RL). In RL, the learning asymmetry concerns the sensitivity difference in incorporating positive and negative prediction errors (PE) into value estimation, namely the asymmetry of learning rates associated with positive and negative PEs. Although RL has been established as a canonical framework in characterizing interactions between agent and environment, the direction of learning asymmetry remains controversial. Here, we propose that part of the controversy stems from the fact that people may have different value expectations before entering the learning environment. Such a default value expectation influences how PEs are calculated and consequently biases subjects' choices. We test this hypothesis in two learning experiments with stable or varying reinforcement probabilities, across monetary gains, losses, and gain-loss mixed environments. Our results consistently support the model incorporating both asymmetric learning rates and the initial value expectation, highlighting the role of initial expectation in value updating and choice preference. Further simulation and model parameter recovery analyses confirm the unique contribution of initial value expectation in accessing learning rate asymmetry.

Biased belief updating in depression

Cognitive approaches to depression have benefitted from recent research on belief updating, examining how new information is used to alter beliefs. This review presents recent advances in understanding various sources of bias in belief updating in depression. Specifically, research has demonstrated that people with depression have difficulty revising negative beliefs in response to novel positive information, whereas belief updating in depression is not related to an enhanced integration of negative information. In terms of mechanisms underlying the deficient processing of positive information, research has shown that people with depression use defensive cognitive strategies to devalue novel positive information. Furthermore, the disregard of novel positive information can be amplified by the presence of state negative affect, and the resulting persistence of negative beliefs in turn perpetuates chronically low mood, contributing to a self-reinforcing negative feedback loop of beliefs and affect. Synthesising previous research, this review proposes a coherent framework of when belief change is likely to occur, and argues that future research also needs to elucidate why people with depression hesitate to abandon negative beliefs. Recent insights from belief updating have not only improved the understanding of the psychopathology of depression, but also have the potential to improve its cognitive-behavioural treatment.

A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders

Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.

If the discrepancy between expectations and actual information is too large, expectation change decreases - A replication study

BACKGROUND AND OBJECTIVES

Mental disorders have been related to aberrations in using novel positive information to revise established negative beliefs. Previous research used to make a binary distinction between belief-confirming vs. -disconfirming information, but recently it has been examined how varying levels of positive information is used to update beliefs. The present study aimed to replicate a recent finding suggesting that positive information that deviates to a large extent from people's prior expectations raises doubts about the credibility of new information and therefore hardly leads to change in expectations.

METHODS

In a heterogenous sample (N = 144), participants were provided with slightly positive, moderately positive, or extremely positive information in relation to their prior expectations about other people's behaviour.

RESULTS

Replicating previous research, the present study found that expectation change was greatest for moderately positive information. It also provided evidence for a possible mechanism underlying the inverse U-shaped relationship between the positivity of new information and change in expectations: Extremely positive information was devalued through defensive cognitive strategies, referred to as cognitive immunisation.

LIMITATIONS

Unlike previous research, the belief update task used in this study did not show any association with depressive symptoms, so it is questionable how suitable it is to study biased belief updating in depression.

CONCLUSIONS

Unlike traditional learning models, the present results suggest a tipping point above which the discrepancy between expectation and outcome is suspiciously large, so that the degree of expectation change decreases.

Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals' thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.

Origins and consequences of mood flexibility: a computational perspective

A stable and neutral mood (euthymia) is commended by both economic and clinical perspectives, because it enables rational decisions and avoids mental illnesses. Here we suggest, on the contrary, that a flexible mood responsive to life events may be more adaptive for natural selection, because it can help adjust the behavior to fluctuations in the environment. In our model (dubbed MAGNETO), mood represents a global expected value that biases decisions to forage for a particular reward. When flexible, mood is updated every time an action is taken, by aggregating incurred costs and obtained rewards. Model simulations show that, across a large range of parameters, flexible agents outperform cold agents (with stable neutral mood), particularly when rewards and costs are correlated in time, as naturally occurring across seasons. However, with more extreme parameters, simulations generate short manic episodes marked by incessant foraging and lasting depressive episodes marked by persistent inaction. The MAGNETO model therefore accounts for both the function of mood fluctuations and the emergence of mood disorders.

Expectations do not need to be accurate to be maintained: Valence and need for cognitive closure predict expectation update vs. persistence

Expectations about us and our environment serve to successfully anticipate the future, make accurate predictions, and guide behavior and decisions. However, when expectations are not accurate, individuals need to resolve or minimize incongruence. Coping is especially important when expectations affect important domains such as students' academic self-concept. Whether expectations are adjusted after expectation violation (accommodation), maintained by denying the discrepancy (immunization), or whether individuals modify behavior to minimize the likelihood of future expectation violations (assimilation) depends on situational and dispositional predictors. In our experiment, we examined valence of expectation violation (positive vs. negative) as a situational predictor together with need for cognitive closure (NCC) as a dispositional predictor with N = 297 participants in a word riddle study. MANCOVA revealed that students tended to assimilate and accommodate more strongly after worse-than-expected achievement, and also NCC promoted both stronger accommodation and assimilation. NCC interacted with the valence of expectation violation: individuals with high NCC reported more assimilation and accommodation only after worse-than-expected achievement. The results replicate and extend previous findings: individuals do not always strive to have the most accurate expectations possible. Instead, both affective (valence) and cognitive (NCC) predictors appear to affect which coping strategy is preferred by the individual.

Hedonism as a motive for information search: biased information-seeking leads to biased beliefs

Confirmation bias in information-search contributes to the formation of polarized echo-chambers of beliefs. However, the role of valence on information source selection remains poorly understood. In Experiment 1, participants won financial rewards depending on the outcomes of a set of lotteries. They were not shown these outcomes, but instead could choose to view a prediction of each lottery outcome made by one of two sources. Before choosing their favoured source, participants were first shown a series of example predictions made by each. The sources systematically varied in the accuracy and positivity (i.e., how often they predicted a win) of their predictions. Hierarchical Bayesian modeling indicated that both source accuracy and positivity impacted participants' choices. Importantly, those that viewed more positively-biased information believed that they had won more often and had higher confidence in those beliefs. In Experiment 2, we directly assessed the effect of positivity on the perceived credibility of a source. In each trial, participants watched a single source making a series of predictions of lottery outcomes and rated the strength of their beliefs in each source. Interestingly, positively-biased sources were not seen as more credible. Together, these findings suggest that positively-biased information is sought partly due to the desirable emotional state it induces rather than having enhanced perceived credibility. Information sought on this basis nevertheless produced consequential biased beliefs about the world-state, highlighting a potentially key role for hedonic preferences in information selection and subsequent belief formation.

Individual differences in naturalistic learning link negative emotionality to the development of anxiety

Organisms learn from prediction errors (PEs) to predict the future. Laboratory studies using small financial outcomes find that humans use PEs to update expectations and link individual differences in PE-based learning to internalizing disorders. Because of the low-stakes outcomes in most tasks, it is unclear whether PE learning emerges in naturalistic, high-stakes contexts and whether individual differences in PE learning predict psychopathology risk. Using experience sampling to assess 625 college students' expected exam grades, we found evidence of PE-based learning and a general tendency to discount negative PEs, an "optimism bias." However, individuals with elevated negative emotionality, a personality trait linked to the development of anxiety disorders, displayed a global pessimism and learning differences that impeded accurate expectations and predicted future anxiety symptoms. A sensitivity to PEs combined with an aversion to negative PEs may result in a pessimistic and inaccurate model of the world, leading to anxiety.

Computational meaningfulness as the source of beneficial cognitive biases

The human brain has evolved to solve the problems it encounters in multiple environments. In solving these challenges, it forms mental simulations about multidimensional information about the world. These processes produce context-dependent behaviors. The brain as overparameterized modeling organ is an evolutionary solution for producing behavior in a complex world. One of the most essential characteristics of living creatures is that they compute the values of information they receive from external and internal contexts. As a result of this computation, the creature can behave in optimal ways in each environment. Whereas most other living creatures compute almost exclusively biological values (e.g., how to get food), the human as a cultural creature computes meaningfulness from the perspective of one's activity. The computational meaningfulness means the process of the human brain, with the help of which an individual tries to make the respective situation comprehensible to herself to know how to behave optimally. This paper challenges the bias-centric approach of behavioral economics by exploring different possibilities opened up by computational meaningfulness with insight into wider perspectives. We concentrate on confirmation bias and framing effect as behavioral economics examples of cognitive biases. We conclude that from the computational meaningfulness perspective of the brain, the use of these biases are indispensable property of an optimally designed computational system of what the human brain is like. From this perspective, cognitive biases can be rational under some conditions. Whereas the bias-centric approach relies on small-scale interpretable models which include only a few explanatory variables, the computational meaningfulness perspective emphasizes the behavioral models, which allow multiple variables in these models. People are used to working in multidimensional and varying environments. The human brain is at its best in such an environment and scientific study should increasingly take place in such situations simulating the real environment. By using naturalistic stimuli (e.g., videos and VR) we can create more realistic, life-like contexts for research purposes and analyze resulting data using machine learning algorithms. In this manner, we can better explain, understand and predict human behavior and choice in different contexts.

Authority Brings Responsibility: Feedback from Experts Promotes an Overweighting of Health-Related Pseudoscientific Beliefs

The popularity and spread of health-related pseudoscientific practices is a worldwide problem. Despite being counteracted by competent agents of our societies, their prevalence and spread continue to grow. Current research has focused on identifying which characteristics make us more likely to hold pseudoscientific beliefs. However, how we hold these beliefs despite all the available information against them is a question that remains unanswered. Here, we aimed to assess if the development of health-related pseudoscientific beliefs could be driven by a positive bias in belief updating. Additionally, we aimed to explore whether this bias could be exacerbated, depending on source credibility. In this study, participants (N = 116) underwent a belief updating task where they offered their agreement with various health-related pseudoscientific statements before and after receiving supporting and discrediting feedback from (a) experts (doctors), (b) peers, or (c) a random number generator. Our results suggest that when receiving feedback from experts (but not from peers or random feedback), the participants preferentially integrated supporting information relative to discrediting information about health-related pseudoscience. We discuss the implications of this biased belief updating pattern on health-related pseudoscientific research and suggest new strategies for intervention focused on increasing awareness, training, and consensus among healthcare practitioners.

Neurocomputational mechanisms of affected beliefs

The feedback people receive on their behavior shapes the process of belief formation and self-efficacy in mastering a particular task. However, the neural and computational mechanisms of how the subjective value of self-efficacy beliefs, and the corresponding affect, influence the learning process remain unclear. We investigated these mechanisms during self-efficacy belief formation using fMRI, pupillometry, and computational modeling, and by analyzing individual differences in affective experience. Biases in the formation of self-efficacy beliefs were associated with affect, pupil dilation, and neural activity within the anterior insula, amygdala, ventral tegmental area/ substantia nigra, and mPFC. Specifically, neural and pupil responses mapped the valence of the prediction errors in correspondence with individuals’ experienced affective states and learning biases during self-efficacy belief formation. Together with the functional connectivity dynamics of the anterior insula within this network, our results provide evidence for neural and computational mechanisms of how we arrive at affected beliefs.

Evaluation of Early Ketamine Effects on Belief-Updating Biases in Patients With Treatment-Resistant Depression

IMPORTANCE

Clinical research has shown that persistent negative beliefs maintain depression and that subanesthetic ketamine infusions induce rapid antidepressant responses.

OBJECTIVE

To evaluate whether ketamine alters belief updating and how such cognitive effects are associated with the clinical effects of ketamine.

DESIGN, SETTING, AND PARTICIPANTS

This study used an observational case-control protocol with a mixed-effects design that nested 2 groups by 2 testing time points. Observers were not blinded. Patients with treatment-resistant depression (TRD) and healthy volunteer participants aged 34 to 68 years were included. Patients with TRD were diagnosed with major depressive disorder or bipolar depression, had a Montgomery-Åsberg Depression Rating Scale score greater than 20, a Maudsley Staging Method score greater than 7, and failed to respond to at least 2 prior antidepressant trials. Exclusion criteria were any other psychiatric, neurological, or neurosurgical comorbidities, substance use or addictive disorders, and recreational ketamine consumption. Data were collected from January to February 2019 and from May to December 2019, and data were analyzed from January 2020 to July 2021.

EXPOSURES

Patients with TRD were observed 24 hours before single ketamine infusion, 4 hours after the infusion, and 4 hours after the third infusion, which was 1 week after the first infusion. Healthy control participants were observed twice 1 week apart without ketamine exposure.

MAIN OUTCOMES AND MEASURES

Montgomery-Åsberg Depression Rating Scale score and belief updating after belief updating when patients received good news and bad news measured by a cognitive belief-updating task and mathematically formalized by a computational reinforcement learning model.

RESULTS

Of 56 included participants, 29 (52%) were male, and the mean (SEM) age was 52.3 (1.2) years. A total of 26 patients with TRD and 30 control participants were included. A significant group × testing time point × news valence interaction showed that patients with TRD updated their beliefs more after good than bad news following a single ketamine infusion (controlled for age and education: β = -0.91; 95% CI, -1.58 to -0.24; t216 = -2.67; P = .008) than controls. Computational modeling showed that this effect was associated with asymmetrical learning rates (LRs) after ketamine treatment (good news LRs after ketamine, 0.51 [SEM, 0.04]; bad news LRs after ketamine 0.36 [SEM, 0.03], t25 = 3.8; P < .001) and partially mediated early antidepressant responses (path a*b: β = -1.00 [SEM, 0.66]; t26 = -1.53; z = -1.98; P = .04).

CONCLUSIONS AND RELEVANCE

These findings provide novel insights into the cognitive mechanisms of the action of ketamine in patients with TRD, with promising perspectives for augmented psychotherapy for individuals with mood disorders.

Testing Bayesian models of belief updating in the context of depressive symptomatology

OBJECTIVES

Predictive processing approaches to belief updating in depression propose that depression is related to more negative and more precise priors. Also, belief updating is assumed be negatively biased in comparison to normative Bayesian updating. There is a lack of efficient methods to mathematically model belief updating in depression.

METHODS

We validated a novel performance belief updating paradigm in a nonclinical sample (N = 133). Participants repeatedly participated in a non-self-related emotion recognition task and received false feedback. Effects of the feedback manipulation and differences in depressive symptoms on belief updating were analysed in Bayesian multilevel analyses.

RESULTS

Beliefs were successfully manipulated through the feedback provided. Depressive symptoms were associated with more negative updating than normative Bayesian updating but results were influenced by few cases. No evidence of biased change in beliefs or overly precise priors was found. Depressive symptoms were associated with more negative updating of generalised performance beliefs.

CONCLUSIONS

There was cautious support for negatively biased belief updating associated with depressive symptoms, especially for generalised beliefs. The content of the task may not be self-relevant enough to cause strong biases. Further explication of Bayesian models of depression and replication in clinical samples is needed.

Challenging depressive beliefs: Habitual and recollective components of stability or change

BACKGROUND AND OBJECTIVES

Depressed people tend to hold stable negative beliefs that resist challenges. Two experiments investigated the cognitive bases of belief change or resistance to change following the provision of supportive or challenging pseudo-evidence.

METHOD

Students scoring high and low on a measure of depressed state read belief statements, each followed by invented experimental evidence to either verify or discount them. Two days later, they read all the belief statements again, together with new statements, this time rating belief.

RESULTS

The students agreed that the statements described common beliefs and that the evidence was plausible. Discounted statements were believed less than new statements on the test. Also, dysphoric students believed discounted and new statements less than verified statements, but that difference was larger for the nondysphoric students. Parameter estimates of the habitual basis for belief ratings, obtained with process-dissociation procedures, were higher in the dysphoric group, and estimates of evidence recollection were lower. The latter finding was conceptually supported by deficient recognition of the gist of the discounting evidence in the dysphoric group (Experiment 2).

LIMITATIONS

Experiment 2 results replicated the rating effects in Experiment 1, but not the parameter differences, due to low power as a consequence of the university response to the pandemic.

CONCLUSIONS

We interpret these results in the context of other evidence regarding belief change and depressive cognition, such as habitual rumination and deficient cognitive control.

Flexibility in valenced reinforcement learning computations across development

Optimal integration of positive and negative outcomes during learning varies depending on an environment's reward statistics. The present study investigated the extent to which children, adolescents, and adults (N = 142 8-25 year-olds, 55% female, 42% White, 31% Asian, 17% mixed race, and 8% Black; data collected in 2021) adapt their weighting of better-than-expected and worse-than-expected outcomes when learning from reinforcement. Participants made choices across two contexts: one in which weighting positive outcomes more heavily than negative outcomes led to better performance, and one in which the reverse was true. Reinforcement learning modeling revealed that across age, participants shifted their valence biases in accordance with environmental structure. Exploratory analyses revealed strengthening of context-dependent flexibility with increasing age.

Quantifying the heritability of belief formation

Individual differences in behaviour, traits and mental-health are partially heritable. Traditionally, studies have focused on quantifying the heritability of high-order characteristics, such as happiness or education attainment. Here, we quantify the degree of heritability of lower-level mental processes that likely contribute to complex traits and behaviour. In particular, we quantify the degree of heritability of cognitive and affective factors that contribute to the generation of beliefs about risk, which drive behavior in domains ranging from finance to health. Monozygotic and dizygotic twin pairs completed a belief formation task. We first show that beliefs about risk are associated with vividness of imagination, affective evaluation and learning abilities. We then demonstrate that the genetic contribution to individual differences in these processes range between 13.5 and 39%, with affect evaluation showing a particular robust heritability component. These results provide clues to which mental factors may be driving the heritability component of beliefs formation, which in turn contribute to the heritability of complex traits.

Everything is connected: Inference and attractors in delusions

Delusions are, by popular definition, false beliefs that are held with certainty and resistant to contradictory evidence. They seem at odds with the notion that the brain at least approximates Bayesian inference. This is especially the case in schizophrenia, a disorder thought to relate to decreased - rather than increased - certainty in the brain's model of the world. We use an active inference Markov decision process model (a Bayes-optimal decision-making agent) to perform a simple task involving social and non-social inferences. We show that even moderate changes in some model parameters - decreasing confidence in sensory input and increasing confidence in states implied by its own (especially habitual) actions - can lead to delusions as defined above. Incorporating affect in the model increases delusions, specifically in the social domain. The model also reproduces some classic psychological effects, including choice-induced preference change, and an optimism bias in inferences about oneself. A key observation is that no change in a single parameter is both necessary and sufficient for delusions; rather, delusions arise due to conditional dependencies that create 'basins of attraction' which trap Bayesian beliefs. Simulating the effects of antidopaminergic antipsychotics - by reducing the model's confidence in its actions - demonstrates that the model can escape from these attractors, through this synthetic pharmacotherapy.

[Perinatal beliefs: Neurocognitive mechanisms and cultural specificities]

Perinatal beliefs contribute to the experience of pregnancy and the process of parenthood. Many of these perinatal beliefs have been perpetuated and evolved over time and throughout the world, exerting their influence on the behavior of pregnant women in interaction with medical recommendations. These beliefs generally offer explanations for gravidic and puerperal phenomena, helping to reduce the uncertainty of parents faced with the biological, psychological and social transitions of pregnancy. But certain beliefs can also be harmful, and alter the maternal experience of pregnancy and postpartum. In this paper, we provide an overview of the beliefs associated with the perinatal period. We successively detail the beliefs concerning fertility, pregnancy, childbirth, and postpartum, specifying the cultural beliefs from other cultures interacting with medical recommendations. Finally, we propose a neurocognitive model of perinatal beliefs generation, and we show the need to know these beliefs to improve care in midwifery, obstetrics, and fetal medicine.

Rethinking delusions: A selective review of delusion research through a computational lens

Delusions are rigid beliefs held with high certainty despite contradictory evidence. Notwithstanding decades of research, we still have a limited understanding of the computational and neurobiological alterations giving rise to delusions. In this review, we highlight a selection of recent work in computational psychiatry aimed at developing quantitative models of inference and its alterations, with the goal of providing an explanatory account for the form of delusional beliefs in psychosis. First, we assess and evaluate the experimental paradigms most often used to study inferential alterations in delusions. Based on our review of the literature and theoretical considerations, we contend that classic draws-to-decision paradigms are not well-suited to isolate inferential processes, further arguing that the commonly cited 'jumping-to-conclusion' bias may reflect neither delusion-specific nor inferential alterations. Second, we discuss several enhancements to standard paradigms that show promise in more effectively isolating inferential processes and delusion-related alterations therein. We further draw on our recent work to build an argument for a specific failure mode for delusions consisting of prior overweighting in high-level causal inferences about partially observable hidden states. Finally, we assess plausible neurobiological implementations for this candidate failure mode of delusional beliefs and outline promising future directions in this area.