{"title":"Decomposing Intolerance of Uncertainty: No Association With Affective Decision Making in a Community Sample.","authors":"Yannik Paul, Anya Pedersen, Kamil Fuławka","doi":"10.5334/cpsy.140","DOIUrl":"10.5334/cpsy.140","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"210-230"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mostafa Abdou, Razia S Sahi, Thomas D Hull, Erik C Nook, Nathaniel D Daw
{"title":"Leveraging Large Language Models to Estimate Clinically Relevant Psychological Constructs in Psychotherapy Transcripts.","authors":"Mostafa Abdou, Razia S Sahi, Thomas D Hull, Erik C Nook, Nathaniel D Daw","doi":"10.5334/cpsy.141","DOIUrl":"10.5334/cpsy.141","url":null,"abstract":"<p><p>Developing precise, innocuous markers of psychopathology and the processes that foster effective treatment would greatly advance the field's ability to detect and intervene on psychopathology. However, a central challenge in this area is that both assessment and treatment are conducted primarily in natural language, a medium that makes quantitative measurement difficult. Although recent advances have been made, much existing research in this area has been limited by reliance on previous-generation psycholinguistic tools. Here we build on previous work that identified a linguistic measure of \"psychological distancing\" (that is, viewing a negative situation as separated from oneself) in client language, which was associated with improved emotion regulation in laboratory settings and treatment progress in real-world therapeutic transcripts (Nook et al., 2017, 2022). However, this formulation was based on context-insensitive word count-based measures of distancing (pronoun person and verb tense), which limits the ability to detect more abstract expressions of psychological distance, such as counterfactual or conditional statements. This approach also leaves open many questions about how therapists' - likely subtler - language can effectively guide clients toward increased psychological distance. We address these gaps by introducing the use of appropriately prompted large language models (LLMs) to measure linguistic distance, and we compare these results to those obtained using traditional word-counting techniques. Our results show that LLMs offer a more nuanced and context-sensitive approach to assessing language, significantly enhancing our ability to model the relations between linguistic distance and symptoms. Moreover, this approach enables us to expand the scope of analysis beyond client language to shed insight into how therapists' language relates to client outcomes. Specifically, the LLM was able to detect ways in which a therapist's language encouraged a client to adopt distanced perspectives-rather than simply detecting the therapist themselves being distanced. This measure also reliably tracked the severity of patient symptoms, highlighting the potential of LLM-powered linguistic analysis to deepen our understanding of therapeutic processes.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"187-209"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12427617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marishka M Mehta, Navid Hakimi, Orestes Pena, Taylor Torres, Carter M Goldman, Claire A Lavalley, Jennifer L Stewart, Hannah Berg, Maria Ironside, Martin P Paulus, Robin Aupperle, Ryan Smith
{"title":"Computational Mechanisms of Approach-Avoidance Conflict Predictively Differentiate Between Affective and Substance Use Disorders.","authors":"Marishka M Mehta, Navid Hakimi, Orestes Pena, Taylor Torres, Carter M Goldman, Claire A Lavalley, Jennifer L Stewart, Hannah Berg, Maria Ironside, Martin P Paulus, Robin Aupperle, Ryan Smith","doi":"10.5334/cpsy.131","DOIUrl":"10.5334/cpsy.131","url":null,"abstract":"<p><p>Psychiatric disorders are highly heterogeneous and often co-morbid, posing specific challenges for effective treatment. Recently, computational modeling has emerged as a promising approach for characterizing sources of this heterogeneity, which could potentially aid in clinical differentiation. In this study, we tested whether computational mechanisms of decision-making under approach-avoidance conflict (AAC) - where behavior is expected to have both positive and negative outcomes - may have utility in this regard. We first carried out a set of pre-registered modeling analyses in a sample of 480 individuals who completed an established AAC task. These analyses aimed to replicate cross-sectional and longitudinal results from a prior dataset (N = 478) - suggesting that mechanisms of decision uncertainty (<i>DU</i>) and emotion conflict (<i>EC</i>) differentiate individuals with depression, anxiety, substance use disorders, and healthy comparisons. We then combined the prior and current datasets and employed a stacked machine learning approach to assess whether these computational measures could successfully perform out-of-sample classification between diagnostic groups. This revealed above-chance differentiation between affective and substance use disorders (balanced accuracy > 0.688), both in the presence and absence of co-morbidities. These results demonstrate the predictive utility of computational measures in characterizing distinct mechanisms of psychopathology and may point to novel treatment targets.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"159-186"},"PeriodicalIF":0.0,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145042345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ilaria Costantini, Axel Montout, Paul Moran, Daphne Kounali, Rebecca M Pearson, Casimir J H Ludwig
{"title":"Investigating Learning, Decision-Making, and Mental Health in Pregnancy: Insights From a UK Cohort Study.","authors":"Ilaria Costantini, Axel Montout, Paul Moran, Daphne Kounali, Rebecca M Pearson, Casimir J H Ludwig","doi":"10.5334/cpsy.134","DOIUrl":"10.5334/cpsy.134","url":null,"abstract":"<p><strong>Background: </strong>Parental capacity to learn from infant responses is a fundamental component of early dyadic interactions. However, the precise cognitive processes involved in these interactions and how these processes are influenced by mental health difficulties remain unclear.</p><p><strong>Methods: </strong>We investigated the computational basis of learning and decision-making in males and nulliparous females (Study 1) and pregnant participants enrolled in a cohort study (Study 2), using a two-armed bandit task adapted to simulate playful interactions with an infant. Participants chose between two competing bandits (i.e., two toys) with different underlying nominal probabilities for three outcomes (i.e., infant sad, neutral, and happy facial expressions). In Study 1, we manipulated the baseline emotional context of the task (i.e., the infant started either happy or sad) to investigate its effect on the processing of emotional feedback and decision-making. In both studies, we explored whether individual differences in mental health and personalities difficulties associated with variation in parameters.</p><p><strong>Results: </strong>In Study 1, the emotional context manipulation influenced both learning rates and how neutral outcomes were evaluated. Participants starting with a happy infant exhibited faster learning and a more negative evaluation of neutral outcomes compared to those starting with a sad infant. In Study 2, participants reporting higher levels of personality difficulties and antenatal depressive symptoms showed reduced learning rates. These associations were weaker in Study 1.</p><p><strong>Conclusions: </strong>Our findings provide novel evidence regarding the role of the emotional context in learning and decision-making processes. For parents with depressive symptoms and personality difficulties, dampened responsivity to emotional feedback and inflexibility in updating beliefs about the values of actions may underlie fewer sensitive behaviours when interacting with their infants.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"142-158"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12427613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145066648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Increasing Response Vigour Under Time Pressure as a Transdiagnostic Marker of Eating Disorders.","authors":"Sam Hall-McMaster, Ondrej Zika","doi":"10.5334/cpsy.130","DOIUrl":"10.5334/cpsy.130","url":null,"abstract":"<p><p>Eating disorders (EDs) are characterised by intense concerns about food and weight. These concerns are linked to changes in decision-making, such as persisting with actions that are no longer rewarding. For example, individuals might engage in long exercise sessions or time-consuming body checking practices, despite limited benefits. This study tested whether people with subclinical ED symptoms show increased persistence due to altered decision-making processes. Specifically, we postulated a shift in internal thresholds for making different decisions in EDs, which change the balance between exploitation and exploration. A subclinical group with heightened concerns about eating (sED; N = 44) and a healthy control group (HC; N = 56) completed a foraging task, in which an option on screen was exploited for reward. With each decision to exploit, reward feedback decreased and participants had to decide when to move on to a new option. Each block was time limited to 7.5 minutes. Behavioural persistence was measured as the number of seconds spent exploiting each option. Decision thresholds were measured when deciding to move on, as the counterfactual reward that would have been received for an exploit action. We predicted that the sED group would show increased persistence and decreased decision thresholds (i.e. lower counterfactual reward when deciding to move on) in comparison to the HC group. We found no evidence for these predictions. Instead, exploratory analyses showed that the sED group exhibited progressively faster response times (RTs) when approaching the time limit for each block. This increase in motor vigour was correlated with the severity of eating disorder symptoms from a range of traditional diagnostic categories. Our results point to changing motor vigour as a potential transdiagnostic marker of ED tendencies.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"124-141"},"PeriodicalIF":0.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12352398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"One Small Step Towards Fixing a Broken System.","authors":"Xiaosi Gu, Rick A Adams","doi":"10.5334/cpsy.148","DOIUrl":"https://doi.org/10.5334/cpsy.148","url":null,"abstract":"","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"122-123"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082440/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marta Radzikowska, Alexandra C Pike, Sam Hall-McMaster
{"title":"Computational Perspectives on Cognition in Anorexia Nervosa: A Systematic Review.","authors":"Marta Radzikowska, Alexandra C Pike, Sam Hall-McMaster","doi":"10.5334/cpsy.128","DOIUrl":"https://doi.org/10.5334/cpsy.128","url":null,"abstract":"<p><p>Anorexia nervosa (AN) is a severe eating disorder, marked by persistent changes in behaviour, cognition and neural activity that result in insufficient body weight. Recently, there has been a growing interest in using computational approaches to understand the cognitive mechanisms that underlie AN symptoms, such as persistent weight loss behaviours, rigid rules around food and preoccupation with body size. Our aim was to systematically review progress in this emerging field. Based on articles selected using systematic and reproducible criteria, we identified five current themes in the computational study of AN: 1) reinforcement learning; 2) value-based decision-making; 3) goal-directed and habitual control over behaviour; 4) cognitive flexibility; and 5) theory-based accounts. In addition to describing and appraising the insights from each of these areas, we highlight methodological considerations for the field and outline promising future directions to establish the clinical relevance of (neuro)computational changes in AN.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"100-121"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11987859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander J Hess, Sandra Iglesias, Laura Köchli, Stephanie Marino, Matthias Müller-Schrader, Lionel Rigoux, Christoph Mathys, Olivia K Harrison, Jakob Heinzle, Stefan Frässle, Klaas Enno Stephan
{"title":"Bayesian Workflow for Generative Modeling in Computational Psychiatry.","authors":"Alexander J Hess, Sandra Iglesias, Laura Köchli, Stephanie Marino, Matthias Müller-Schrader, Lionel Rigoux, Christoph Mathys, Olivia K Harrison, Jakob Heinzle, Stefan Frässle, Klaas Enno Stephan","doi":"10.5334/cpsy.116","DOIUrl":"10.5334/cpsy.116","url":null,"abstract":"<p><p>Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that models harnessing information from two different data streams (binary responses and continuous response times) ensure robust inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants' uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"76-99"},"PeriodicalIF":0.0,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Value of Non-Instrumental Information in Anxiety: Insights from a Resource-Rational Model of Planning.","authors":"Bilal A Bari, Samuel J Gershman","doi":"10.5334/cpsy.124","DOIUrl":"10.5334/cpsy.124","url":null,"abstract":"<p><p>Anxiety is intimately related to the desire for information and, under some accounts, thought to arise from the intolerance of uncertainty. Here, we seek to test this hypothesis by studying the relationship between trait anxiety and the willingness to pay for non-instrumental information (i.e., information that reveals whether an event will happen but cannot be used to change the outcome). We model behavior with a resource-rational model of planning, according to which non-instrumental information is useful for planning ahead, but paying for this information only makes sense if the anticipated benefits of planning outweigh the cognitive and financial costs. We find a bidirectional effect of trait anxiety factors on information seeking behavior: those with high trait somatic anxiety exhibit a stronger preference for non-instrumental information, whereas those with high trait negative affect exhibit a weaker preference. By fitting the resource-rational model, we find that this divergent desire for information arises from the utility of obtaining information for future planning (increased in somatic anxiety, decreased in negative affect). Our findings lend support to the intolerance of uncertainty hypothesis in somatic anxiety and highlight the importance of studying anxiety as a multifactorial construct.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"63-75"},"PeriodicalIF":0.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Signatures of Perseveration and Heuristic-Based Directed Exploration in Two-Step Sequential Decision Task Behaviour.","authors":"Angela Mariele Brands, David Mathar, Jan Peters","doi":"10.5334/cpsy.101","DOIUrl":"10.5334/cpsy.101","url":null,"abstract":"<p><p>Processes formalized in classic Reinforcement Learning (RL) theory, such as model-based (MB) control, habit formation, and exploration have proven fertile in cognitive and computational neuroscience, as well as computational psychiatry. Dysregulations in MB control and exploration and their neurocomputational underpinnings play a key role across several psychiatric disorders. Yet, computational accounts mostly study these processes in isolation. The current study extended standard hybrid models of a widely-used sequential RL-task (two-step task; TST) employed to measure MB control. We implemented and compared different computational model extensions for this task to quantify potential exploration and perseveration mechanisms. In two independent data sets spanning two different variants of the task, an extended hybrid RL model with a higher-order perseveration and heuristic-based exploration mechanism provided the best fit. While a simpler model with complex perseveration only, was equally well equipped to describe the data, we found a robust positive effect of directed exploration on choice probabilities in stage one of the task. Posterior predictive checks further showed that the extended model reproduced choice patterns present in both data sets. Results are discussed with respect to implications for computational psychiatry and the search for neurocognitive endophenotypes.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"39-62"},"PeriodicalIF":0.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11827566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}