Computational psychiatry (Cambridge, Mass.)最新文献

{"title":"Computational Modelling Reveals Slower Safety Learning and Threat Extinction are Associated With Higher Anxiety Severity in Remote Fear Conditioning.","authors":"Tim Kerr, Kirstin Purves, Thomas McGregor, Michelle G Craske, Tom Barry, Kathryn J Lester, Elena Constantinou, Michael Sun, Oliver J Robinson, Thalia C Eley","doi":"10.5334/cpsy.138","DOIUrl":"10.5334/cpsy.138","url":null,"abstract":"<p><p>Anxiety disorders are chronic, pervasive, and debilitating; characterised by a persistent or exaggerated response to distal or abstract threats. Impaired threat discrimination (distinguishing safe from threatening stimuli) and impaired threat extinction (learning a once threatening stimulus is now safe), are known risk factors in the development and persistence of anxiety disorders. These effects can be experimentally elicited through fear conditioning. First, repeated trials of paired aversive and neutral stimuli are delivered during a fear acquisition phase, followed by repeated trials with no aversive stimuli in a fear extinction phase. The effects are typically measured through comparison of end-phase data points, or simple descriptive or statistical models. Computational modelling, by contrast, can offer a hypothesis-driven, trial-by-trial mechanistic account of fear conditioning. This unmasks within subject task variance by estimating the rate of threat learning, safety learning, and threat extinction, examining individual differences in the cognitive mechanisms behind anxiety. A normative sample (n = 145) underwent a differential fear conditioning task on a bespoke smartphone app, in addition to completing an anxiety severity measure (GAD-7). Computational models fitted to task data estimated learning rates. Whilst the threat learning rate showed no association, the threat extinction and safety learning rates showed small negative associations with anxiety severity (ρ = -0.22, p = 0.01 & ρ = -0.21, p = 0.01 respectively). These findings are in keeping with prior studies using traditional analytical approaches, and indicate that anxious individuals are not quicker to develop fear of a stimulus, but take more time than their non-anxious counterparts to learn that a stimulus is safe. This study strengthens the evidence for impairments in fear extinction in those with anxiety, and the importance of learning rates as an index of anxiety severity, a previously hidden cognitive mechanism underlying anxiety persistence.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"18-35"},"PeriodicalIF":0.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146047554","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":"Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study.","authors":"Brian Zaboski, Sarah Fineberg, Patrick Skosnik, Stephen Kichuk, Madison Fitzpatrick, Christopher Pittenger","doi":"10.5334/cpsy.149","DOIUrl":"10.5334/cpsy.149","url":null,"abstract":"<p><strong>Objective: </strong>Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an affordable and noninvasive approach, but identifying predictive signals in EEG data has met with little success, even with the application of traditional machine learning methods. We explored whether convolutional neural networks (CNNs) applied to EEG time-frequency representations can distinguish individuals with OCD from healthy controls.</p><p><strong>Method: </strong>We collected resting-state EEG data from 20 unmedicated participants (10 with OCD, 10 healthy controls). Four-second EEG segments were transformed into time-frequency representations. We then trained a 2D CNN using a leave-one-subject-out cross-validation framework to perform subject-level classification and compared its performance to a more traditional support vector machine (SVM) approach. Next, using multimodal fusion, we examined whether adding clinical and demographic information improved classification.</p><p><strong>Results: </strong>The CNN classifier achieved high subject-level performance, distinguishing individuals with an accuracy of 85.0% and an area under the curve (AUC) of 0.88. This significantly outperformed the SVM baseline, which performed no better than chance (45.0% accuracy, AUC: 0.47). A subsequent multimodal analysis revealed that clinical and demographic variables did not contribute any additional independent information.</p><p><strong>Conclusion: </strong>CNNs applied to resting-state EEG show promise for identifying OCD, outperforming traditional machine learning methods. These findings highlight the potential of deep learning to uncover complex, diagnostically relevant patterns in neural data. While limited by sample size, this work supports further investigation into multimodal models for psychiatric classification, warranting replication in larger, more diverse samples.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"10 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12829452/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055169","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":"Illness Severity in Psychotic Disorders Amplifies Anterior Insula's Sensitivity to Unreciprocated Smiles.","authors":"Jayson Jeganathan, Megan E J Campbell, Renate Thienel, Nikitas C Koussis, Bryan Paton, Katharina V Wellstein, Michael Breakspear","doi":"10.5334/cpsy.142","DOIUrl":"10.5334/cpsy.142","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"253-267"},"PeriodicalIF":0.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12758102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901602","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":"Reinforcement Learning and Decision Making in Depression in Adolescents and Young Adults: Insights from a New Model of the Probabilistic Reward Task.","authors":"Ziwei Cheng, Amelia D Moser, Jenna Jones, Christopher D Schneck, David J Miklowitz, Daniel G Dillon, Roselinde H Kaiser","doi":"10.5334/cpsy.147","DOIUrl":"10.5334/cpsy.147","url":null,"abstract":"<p><p>Depression is a prevalent psychiatric condition that commonly emerges in adolescence and young adulthood and is associated with reward processing abnormalities. The Probabilistic Reward Task (PRT) is widely used to investigate the impact of depression on reward processing, but prior studies have not comprehensively addressed the reinforcement learning and decision-making mechanisms involved in the task. In 726 adolescents and young adults with varying levels of depression, we collected PRT data and applied a novel computational model with response-outcome learning and evidence accumulation processes to provide new insights into the cognitive processes implicated in depression. Compared to participants with no history of psychopathology, those with depressive disorders showed reduced impact of learned response values on decision bias toward the more frequently rewarded action. In addition, higher levels of anhedonia were associated with slower evidence accumulation during decision-making. Together, these findings improved our understanding of the reinforcement learning and decision-making mechanisms assessed by the PRT and their associations with depression.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"268-283"},"PeriodicalIF":0.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12758101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901559","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":"Improving the Reliability of the Pavlovian Go/No-Go Task for Computational Psychiatry Research.","authors":"Samuel Zorowitz, Gili Karni, Natalie Paredes, Nathaniel Daw, Yael Niv","doi":"10.5334/cpsy.127","DOIUrl":"10.5334/cpsy.127","url":null,"abstract":"<p><strong>Background: </strong>The Pavlovian go/no-go task is commonly used to measure individual differences in Pavlovian biases and their interaction with instrumental learning. The task has also been widely used in computational psychiatry research, to correlate Pavlovian biases with mental health symptoms. However, prior research has reported unacceptable reliability for computational model-based performance measures for this task, limiting its usefulness in individual-differences research. Here, we apply several strategies previously shown to enhance task-measure reliability (e.g., task gamification, hierarchical Bayesian modeling for model estimation) to the Pavlovian go/no-go task, to improve the reliability of the task as a tool for future research.</p><p><strong>Methods: </strong>In two experiments, two independent samples of adult participants (N = 103, N = 110) completed a novel, gamified version of the Pavlovian go/no-go task multiple times over several weeks. We used hierarchical Bayesian modeling to derive reinforcement learning model-based indices of participants' task performance, and to estimate the reliability of these measures.</p><p><strong>Results: </strong>In Experiment 1, we observed considerable practice effects, with most participants reaching near-ceiling levels of performance with repeat testing. Consequently, the test-retest reliability of some model parameters was unacceptable (as low as 0.379). In Experiment 2, participants completed a modified version of the task designed to lessen these practice effects. We observed greatly reduced practice effects and improved estimates of the test-retest reliability (range: 0.696-0.989).</p><p><strong>Conclusion: </strong>The results demonstrate that model-based measures of performance on our modified Pavlovian go/no-go task can reach levels of reliability sufficient for use in individual-differences research. We therefore provide the task code for use by the computational psychiatry community (as well as other researchers). Additional investigation is necessary to validate the modified version of the task in other populations and settings.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"9 1","pages":"231-252"},"PeriodicalIF":0.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12716264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806618","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":"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}

{"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}

{"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}

{"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}