Adaptive learning from outcome contingencies in eating-disorder risk groups.

Eating disorders are characterised by altered eating patterns alongside overvaluation of body weight or shape, and have relatively low rates of successful treatment and recovery. Notably, cognitive inflexibility has been implicated in both the development and maintenance of eating disorders, and understanding the reasons for this inflexibility might indicate avenues for treatment development. We therefore investigate one potential cause of this inflexibility: an inability to adjust learning when outcome contingencies change. We recruited (n = 82) three groups of participants: those who had recovered from anorexia nervosa (RA), those who had high levels of eating disorder symptoms but no formal diagnosis (EA), and control participants (HC). They performed a reinforcement learning task (alongside eye-tracking) in which the volatility of wins and losses was independently manipulated. We predicted that both the RA and EA groups would adjust their learning rates less than the control participants. Unexpectedly, the RA group showed elevated adjustment of learning rates for both win and loss outcomes compared to control participants. The RA group also showed increased pupil dilation to stable wins and reduced pupil dilation to stable losses. Their learning rate adjustment was associated with the difference between their pupil dilation to volatile vs. stable wins. In conclusion, we find evidence that learning rate adjustment is unexpectedly higher in those who have recovered from anorexia nervosa, indicating that the relationship between eating disorders and cognitive inflexibility may be complex. Given our findings, investigation of noradrenergic agents may be valuable in the field of eating disorders.

Eating disorder symptoms and control-seeking behavior.

OBJECTIVE: Eating disorders (EDs) are a heterogenous group of disorders characterized by disturbed eating patterns. Links have been made between ED symptoms and control-seeking behaviors, which may cause relief from distress. However, whether direct behavioral measures of control-seeking behavior correlate with ED symptoms has not been directly tested. Additionally, existing paradigms may conflate control-seeking behavior with uncertainty-reducing behavior. METHOD: A general population sample of 183 participants completed part in an online behavioral task, in which participants rolled a die in order to obtain/avoid a set of numbers. Prior to each roll, participants could choose to change arbitrary features of the task (such as the color of their die) or view additional information (such as the current trial number). Selecting these Control Options could cost participants points or not (Cost/No-Cost conditions). Each participant completed all four conditions, each with 15 trials, followed by a series of questionnaires, including the Eating Attitudes Test-26 (EAT-26), the Intolerance of Uncertainty Scale, and the Obsessive-Compulsive Inventory-Revised (OCI-R). RESULTS: A Spearman's rank test indicated no significant correlation between total EAT-26 score and total number of Control Options selected, with only elevated scores on a measure of obsessions and compulsivity (OCI-R) correlating with the total number of Control Options selected (rs  = .155, p = .036). DISCUSSION: In our novel paradigm, we find no relationship between EAT-26 score and control-seeking. However, we do find some evidence that this behavior may be present in other disorders that often coincide with ED diagnosis, which may indicate that transdiagnostic factors such as compulsivity are important to control-seeking.

Editorial: What is computational psychopathology, and why do we need it?

Computational Psychopathology is an emerging discipline, which is based around the theoretical and mechanistic focus of explanatory psychopathology and computational psychiatry, and reflects the general move in psychiatric research away from whole disorders to component symptoms or transdiagnostic processes. In this Editorial, we present a brief summary of these disciplines and how they combine together to form a 'Computational Psychopathology', and present a brief possible taxonomy. We highlight the papers that form part of this Special Issue, along with their place in our putative taxonomy. We conclude this Editorial by highlighting the benefits of a Computational Psychopathology for research into mental health.

Catastrophizing and Risk-Taking.

Background: Catastrophizing, when an individual overestimates the probability of a severe negative outcome, is related to various aspects of mental ill-health. Here, we further characterize catastrophizing by investigating the extent to which self-reported catastrophizing is associated with risk-taking, using an online behavioural task and computational modelling. Methods: We performed two online studies: a pilot study (n = 69) and a main study (n = 263). In the pilot study, participants performed the Balloon Analogue Risk Task (BART), alongside two other tasks (reported in the Supplement), and completed mental health questionnaires. Based on the findings from the pilot, we explored risk-taking in more detail in the main study using two versions of the Balloon Analogue Risk task (BART), with either a high or low cost for bursting the balloon. Results: In the main study, there was a significant negative relationship between self-report catastrophizing scores and risk-taking in the low (but not high) cost version of the BART. Computational modelling of the BART task revealed no relationship between any parameter and Catastrophizing scores in either version of the task. Conclusions: We show that increased self-reported catastrophizing may be associated with reduced behavioural measures of risk-taking, but were unable to identify a computational correlate of this effect.

10 Simple Rules for a Supportive Lab Environment.

The transition to principal investigator (PI), or lab leader, can be challenging, partially due to the need to fulfil new managerial and leadership responsibilities. One key aspect of this role, which is often not explicitly discussed, is creating a supportive lab environment. Here, we present ten simple rules to guide the new PI in the development of their own positive and thriving lab atmosphere. These rules were written and voted on collaboratively, by the students and mentees of Professor Mark Stokes, who inspired this piece.

Reinforcement Learning in Patients With Mood and Anxiety Disorders vs Control Individuals: A Systematic Review and Meta-analysis.

IMPORTANCE: Computational psychiatry studies have investigated how reinforcement learning may be different in individuals with mood and anxiety disorders compared with control individuals, but results are inconsistent. OBJECTIVE: To assess whether there are consistent differences in reinforcement-learning parameters between patients with depression or anxiety and control individuals. DATA SOURCES: Web of Knowledge, PubMed, Embase, and Google Scholar searches were performed between November 15, 2019, and December 6, 2019, and repeated on December 3, 2020, and February 23, 2021, with keywords (reinforcement learning) AND (computational OR model) AND (depression OR anxiety OR mood). STUDY SELECTION: Studies were included if they fit reinforcement-learning models to human choice data from a cognitive task with rewards or punishments, had a case-control design including participants with mood and/or anxiety disorders and healthy control individuals, and included sufficient information about all parameters in the models. DATA EXTRACTION AND SYNTHESIS: Articles were assessed for inclusion according to MOOSE guidelines. Participant-level parameters were extracted from included articles, and a conventional meta-analysis was performed using a random-effects model. Subsequently, these parameters were used to simulate choice performance for each participant on benchmarking tasks in a simulation meta-analysis. Models were fitted, parameters were extracted using bayesian model averaging, and differences between patients and control individuals were examined. Overall effect sizes across analytic strategies were inspected. MAIN OUTCOMES AND MEASURES: The primary outcomes were estimated reinforcement-learning parameters (learning rate, inverse temperature, reward learning rate, and punishment learning rate). RESULTS: A total of 27 articles were included (3085 participants, 1242 of whom had depression and/or anxiety). In the conventional meta-analysis, patients showed lower inverse temperature than control individuals (standardized mean difference [SMD], -0.215; 95% CI, -0.354 to -0.077), although no parameters were common across all studies, limiting the ability to infer differences. In the simulation meta-analysis, patients showed greater punishment learning rates (SMD, 0.107; 95% CI, 0.107 to 0.108) and slightly lower reward learning rates (SMD, -0.021; 95% CI, -0.022 to -0.020) relative to control individuals. The simulation meta-analysis showed no meaningful difference in inverse temperature between patients and control individuals (SMD, 0.003; 95% CI, 0.002 to 0.004). CONCLUSIONS AND RELEVANCE: The simulation meta-analytic approach introduced in this article for inferring meta-group differences from heterogeneous computational psychiatry studies indicated elevated punishment learning rates in patients compared with control individuals. This difference may promote and uphold negative affective bias symptoms and hence constitute a potential mechanistic treatment target for mood and anxiety disorders.

Threat of shock promotes passive avoidance, but not active avoidance.

Anxiety and stress are adaptive responses to threat that promote harm avoidance. In particular, prior work has shown that anxiety induced in humans using threat of unpredictable shock promotes behavioral inhibition in the face of harm. This is consistent with the idea that anxiety promotes passive avoidance-that is, withholding approach actions that could lead to harm. However, harm can also be avoided through active avoidance, where a (withdrawal) action is taken to avoid harm. Here, we provide the first direct within-study comparison of the effects of threat of shock on active and passive avoidance. We operationalize passive avoidance as withholding a button press response in the face of negative outcomes, and active avoidance as lifting/releasing a button press in the face of negative outcomes. We explore the impact of threat of unpredictable shock on the learning of these behavioral responses (alongside matched responses to rewards) within a single cognitive task. We predicted that threat of shock would promote both active and passive avoidance, and that this would be driven by increased reliance on Pavlovian bias, as parameterized within reinforcement-learning models. Consistent with our predictions, we provide evidence that threat of shock promotes passive avoidance as conceptualized by our task. However, inconsistent with predictions, we found no evidence that threat of shock promoted active avoidance, nor evidence of elevated Pavlovian bias in any condition. One hypothetical framework with which to understand these findings is that anxiety promotes passive over active harm avoidance strategies in order to conserve energy while avoiding harm.

The Importance of Common Currency Tasks in Translational Psychiatry.

PURPOSE OF REVIEW: Common currency tasks are tasks that investigate the same phenomenon in different species. In this review, we discuss how to ensure the translational validity of common currency tasks, summarise their benefits, present recent research in this area and offer future directions and recommendations. RECENT FINDINGS: We discuss the strengths and limitations of three specific examples where common currency tasks have added to our understanding of psychiatric constructs-affective bias, reversal learning and goal-based decision making. SUMMARY: Overall, common currency tasks offer the potential to improve drug discovery in psychiatry. We recommend that researchers prioritise construct validity above face validity when designing common currency tasks and suggest that the evidence for construct validity is summarised in papers presenting research in this area.

The development and psychometric properties of a self-report Catastrophizing Questionnaire.

Catastrophizing is a cognitive process that can be defined as predicting the worst possible outcome. It has been shown to be related to psychiatric diagnoses such as depression and anxiety, yet there are no self-report questionnaires specifically measuring it outside the context of pain research. Here, we therefore develop a novel, comprehensive self-report measure of general catastrophizing. We performed five online studies (total n = 734), in which we created and refined a Catastrophizing Questionnaire, and used a factor analytic approach to understand its underlying structure. We also assessed convergent and discriminant validity, and analysed test-retest reliability. Furthermore, we tested the ability of Catastrophizing Questionnaire scores to predict relevant clinical variables over and above other questionnaires. Finally, we also developed a four-item short version of this questionnaire. We found that our questionnaire is best fit by a single underlying factor, and shows convergent and discriminant validity. Exploratory factor analyses indicated that catastrophizing is independent from other related constructs, including anxiety and worry. Moreover, we demonstrate incremental validity for this questionnaire in predicting diagnostic and medication status. Finally, we demonstrate that our Catastrophizing Questionnaire has good test-retest reliability (intraclass correlation coefficient = 0.77, p < 0.001). Critically, we can now, for the first time, obtain detailed self-report data on catastrophizing.

Attentional Control in Subclinical Anxiety and Depression: Depression Symptoms Are Associated With Deficits in Target Facilitation, Not Distractor Inhibition.

Mood and anxiety disorders are associated with deficits in attentional control involving emotive and non-emotive stimuli. Current theories focus on impaired attentional inhibition of distracting stimuli in producing these deficits. However, standard attention tasks struggle to separate distractor inhibition from target facilitation. Here, we investigate whether distractor inhibition underlies these deficits using neutral stimuli in a behavioral task specifically designed to tease apart these two attentional processes. Healthy participants performed a four-location Posner cueing paradigm and completed self-report questionnaires measuring depressive symptoms and trait anxiety. Using regression analyses, we found no relationship between distractor inhibition and mood symptoms or trait anxiety. However, we find a relationship between target facilitation and depression. Specifically, higher depressive symptoms were associated with reduced target facilitation in a task-version in which the target location repeated over a block of trials. We suggest this may relate to findings previously linking depression with deficits in predictive coding in clinical populations.

The translational neural circuitry of anxiety.

Anxiety is an adaptive response that promotes harm avoidance, but at the same time excessive anxiety constitutes the most common psychiatric complaint. Moreover, current treatments for anxiety-both psychological and pharmacological-hover at around 50% recovery rates. Improving treatment outcomes is nevertheless difficult, in part because contemporary interventions were developed without an understanding of the underlying neurobiological mechanisms that they modulate. Recent advances in experimental models of anxiety in humans, such as threat of unpredictable shock, have, however, enabled us to start translating the wealth of mechanistic animal work on defensive behaviour into humans. In this article, we discuss the distinction between fear and anxiety, before reviewing translational research on the neural circuitry of anxiety in animal models and how it relates to human neuroimaging studies across both healthy and clinical populations. We highlight the roles of subcortical regions (and their subunits) such as the bed nucleus of the stria terminalis, the amgydala, and the hippocampus, as well as their connectivity to cortical regions such as dorsal medial and lateral prefrontal/cingulate cortex and insula in maintaining anxiety responding. We discuss how this circuitry might be modulated by current treatments before finally highlighting areas for future research that might ultimately improve treatment outcomes for this common and debilitating transdiagnostic symptom.

Deep Brain Stimulation in Anorexia Nervosa: Hope for the Hopeless or Exploitation of the Vulnerable? The Oxford Neuroethics Gold Standard Framework.

Neurosurgical interventions for psychiatric disorders have a long and troubled history (1, 2) but have become much more refined in the last few decades due to the rapid development of neuroimaging and robotic technologies (2). These advances have enabled the design of less invasive techniques, which are more focused, such as deep brain stimulation (DBS) (3). DBS involves electrode insertion into specific neural targets implicated in pathological behavior, which are then repeatedly stimulated at adjustable frequencies. DBS has been used for Parkinson's disease and movement disorders since the 1960s (4-6) and over the last decade has been applied to treatment-refractory psychiatric disorders, with some evidence of benefit in obsessive-compulsive disorder (OCD), major depressive disorder, and addictions (7). Recent consensus guidelines on best practice in psychiatric neurosurgery (8) stress, however, that DBS for psychiatric disorders remains at an experimental and exploratory stage. The ethics of DBS-in particular for psychiatric conditions-is debated (1, 8-10). Much of this discourse surrounds the philosophical implications of competence, authenticity, personality, or identity change following neurosurgical interventions, but there is a paucity of applied guidance on neuroethical best practice in psychiatric DBS, and health-care professionals have expressed that they require more (11). This paper aims to redress this balance by providing a practical, applied neuroethical gold standard framework to guide research ethics committees, researchers, and institutional sponsors. We will describe this as applied to our protocol for a particular research trial of DBS in severe and enduring anorexia nervosa (SE-AN) (https://clinicaltrials.gov/ct2/show/NCT01924598, unique identifier NCT01924598), but believe it may have wider application to DBS in other psychiatric disorders.