Intolerance of Uncertainty (IU) is a transdiagnostic factor in psychological disorders, yet its underlying psychological mechanisms remain unclear. To close this gap, we first identify three potential mechanisms from existing definitions of IU: (1) negativity overweighting, (2) probability distortion, and (3) information deficit aversion. Second, we demonstrate how these mechanisms map onto well-established preference patterns in decision making under uncertainty as captured by Cumulative Prospect Theory: (1) loss aversion, (2) nonlinear probability weighting, and (3) the description–experience (DE) gap. Third, we conduct an affective decision-making experiment to investigate the relationship between self-reported IU and these preference patterns, as measured with individually estimated parameters of cumulative prospect theory. In the study, 100 participants made 120 choices between hypothetical painkillers with different probabilistic side effects. Half of the choices were made in a description condition, where all information was provided upfront; the other half in an experience condition, where participants acquired information through sampling. Trait IU was measured with a questionnaire. Participants overweighed side effects relative to treatment benefits (loss aversion), overestimated the probability of unlikely negative outcomes (increased nonlinear probability weighting), and their probability weighting patterns differed between the experimental conditions (DE gap). However, their preference patterns did not correlate with IU scores. Possible explanations are that the task did not effectively establish an affective context with real consequences for behavior, or that disorder-specific processes were not captured in our community sample. These findings highlight the need for a precise definition of IU and suggest avenues for designing tasks that enable a better understanding of IU.

The optimism bias is a cognitive bias where individuals overestimate the likelihood of good outcomes and underestimate the likelihood of bad outcomes. Associated with improved quality of life, optimism bias is considered to be adaptive and is a promising avenue of research for mental health interventions in conditions where individuals lack optimism such as major depressive disorder. Here we lay the groundwork for future research on optimism as an intervention by introducing a domain general formal model of optimism bias, which can be applied in different task settings. Employing the active inference framework, we propose a model of the optimism bias as high precision likelihood biased towards positive outcomes. First, we simulate how optimism may be lost during development by exposure to negative events. We then ground our model in the empirical literature by showing how the developmentally acquired differences in optimism are expressed in a belief updating task typically used to assess optimism bias. Finally, we show how optimism affects action in a modified two-armed bandit task. Our model and the simulations it affords provide a computational basis for understanding how optimism bias may emerge, how it may be expressed in standard tasks used to assess optimism, and how it affects agents’ decision-making and actions; in combination, this provides a basis for future research on optimism as a mental health intervention.

When we smile, we expect that others will smile back. When one’s smile is not reciprocated, these expectations are violated, producing prediction error signals in the brain. Prediction error signals may be experienced as aversive, disincentivizing smiling. Social smiling is impaired in psychotic disorders suggesting increased sensitivity to unreciprocated smiles.We developed the Incongruent Facial Emotion task to probe responses to unreciprocated smiles. Healthy controls and persons with schizophrenia or schizoaffective disorder voluntarily smiled, after which they viewed a stimulus face with a happy or angry expression. Brain activations were quantified with functional magnetic resonance imaging.Greater illness severity was associated with reduced smile amplitude. Across both groups, viewing an incongruent stimulus after initiating a smile activated the bilateral anterior insulae and right supplementary motor cortex. Brain activations in the left middle occipital and left superior frontal gyri were greater in the clinical group. The anterior insula response to incongruent facial reactions was significantly greater in more severely ill clinical participants. Dynamic causal modelling suggests that incongruent stimuli reduce tonic self-inhibition in the anterior insula, and that this disinhibition is enhanced by illness severity.The results suggest that the anterior insula processes affective prediction errors and sends feedback to supplementary motor areas to alter behavioural responses. The underlying brain circuits are enhanced in clinical participants with severe illness, suggesting new avenues to understand affective blunting in psychotic disorders.

In value-based decision-making there is wide behavioural variability in how individuals respond to uncertainty. Maladaptive responses to uncertainty have been linked to a vulnerability to mental illness, for example, between risk aversion and affective disorders. Here, we examine individual differences in risk sensitivity when subjects confront options drawn from different value distributions, where these embody the same or different means and variances. In simulations, we show that a model that learns a distribution using Bayes’ rule and reads out different parts of the distribution under the influence of a risk-sensitive parameter (Conditional Value at Risk, CVaR) predicts how likely an agent is to prefer a broader over a narrow distribution (pro-variance bias/risk-seeking) under the same overall means. Using empirical data, we show that CVaR estimates correlate with participants’ pro-variance biases better than a range of alternative parameters derived from other models. Importantly, across two independent samples, CVaR estimates and participants’ pro-variance bias negatively correlated with trait rumination, a common trait in depression and anxiety. We conclude that a Bayesian-CVaR model captures individual differences in sensitivity to variance in value distributions and task-independent trait dispositions linked to affective disorders.

Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modeling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability. Using Sequenced Treatment Alternatives to Relieve Depression (STAR*D) and Combining Medications to Enhance Depression Outcomes (CO-MED) trial data, we employed deep neural networks to predict remission after feature selection. Treatments included were citalopram, escitalopram, bupropion SR plus escitalopram, and venlafaxine plus mirtazapine. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using two approaches: (1) using predictions generated directly from the model (the predicted improvement approach) and (2) using bootstrapping for sample generation and then estimating population remission rate for patients who actually received the drug predicted by the model compared to the general population (the actual improvement approach). Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an area under the curve of 0.69 using 17 input features. Our actual improvement analysis showed a statistically significant 2.48% absolute improvement (corresponding to a 7.2% relative improvement) in population remission rate ( p = 0.01, CI 2.48% ± 0.5%). Our model serves as proof-of-concept that deep learning approaches, with further refinement and work to address concerns about differences between studies when multiple datasets are used for training, may have utility in differential prediction of antidepressant response when selecting from a number of treatment options.

One of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals into patient and control groups are generally based on a diagnostic system, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). Several flaws in these conventional diagnostic-based approaches have been recognized. The flaws are primarily due to the complexity in the relation between the pathogenetic factors (causes) and disorders: The current diagnostic categories may not reflect the underlying etiological mechanisms. To overcome this difficulty, the National Institute of Mental Health initiated a novel research strategy called Research Domain Criteria (RDoC), which encourages studies to focus on the neurobiological mechanisms and core aspects of behavior rather than to rely on traditional diagnostic categories. However, how RDoC can improve research in psychiatry remains a matter of debate. In this article, we propose a theoretical framework for evaluating psychiatric research strategies, including the conventional diagnostic category-based approaches and the RDoC approach. The proposed framework is based on the statistical modeling of the processes of how the disorder arises from pathogenetic factors. This framework provides the statistical power to quantify how likely relevant pathogenetic factors are to be detected under various research strategies. On the basis of the proposed framework, we can discuss which approach performs better in different types of situations. We present several theoretical and numerical results that highlight the advantages and disadvantages of the strategies. We also demonstrate how a computational model is incorporated into the proposed framework as a generative model of behavioral observations. This demonstration highlights how the computational models contribute to designing psychiatric studies.

We build on a neuroanatomical model of how empathic states can motivate caregiving behavior, via empathy circuit-driven activation of regions in the hypothalamus and amygdala, which in turn stimulate a mesolimbic–ventral pallidum pathway, by integrating findings related to the perception of pain in self and others. On this basis, we propose a network to capture states of personal distress and (weak and strong forms of) empathic concern, which are particularly relevant for psychotherapists conducting attachment-based interventions. This model is then extended for the case of self-attachment therapy, in which conceptualized components of the self serve as both the source of and target for empathic resonance. In particular, we consider how states of empathic concern involving an other that is perceived as being closely related to the self might enhance the motivation for self-directed bonding (which in turn is proposed to lead the individual toward more compassionate states) in terms of medial prefrontal cortex–mediated activation of these caregiving pathways. We simulate our model computationally and discuss the interplay between the bonding and empathy protocols of the therapy.

Reinforcement learning models provide an excellent example of how a computational process approach can help organize ideas and understanding of underlying neurobiology. In a strong sense, this is the assumption behind computational neuroscience. Computational psychiatry, as a translational arm of computational neuroscience, can also profit from the computational process approach but applied at many levels ranging from low-level neurobiology through characterization of mental states and even up to the level of multiple interacting humans. Here, we review some of the early evidence for why reinforcement learning in its modern versions moves well beyond behaviorist accounts and provides an excellent “computational paradigm” for framing value-dependent decision-making; something that goes awry in a number of psychiatry conditions. We focus in particular on how social exchange between humans can engage reward systems and can be used as a computational device good for parsing subjects into categories that relate in interesting ways to traditional depictions of psychopathology.

The stabilization of human cognition via feedback from embedding social and cultural contexts is a dynamic process deeply intertwined with it, constituting, in a sense, the riverbanks directing the flow of a stream of generalized consciousness at different scales: cultural norms and social interaction are synergistic with individual and group cognition and their disorders. A canonical failure mode in atomistic cultures is found to be a "ground state" collapse well-represented by atomistic models of economic interaction that are increasingly characterized as divorced from reality by heterodox economists. That is, high rates of psychopathic and antisocial personality disorder and obsessive-compulsive disorder emerge as culture-bound syndromes particular to Western or Westernizing societies, or to those undergoing social disintegration.