Computational psychiatry (Cambridge, Mass.)最新文献

{"title":"A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model.","authors":"Zhihao Zhang, Saksham Chandra, Andrew Kayser, Ming Hsu, Joshua L Warren","doi":"10.1162/cpsy_a_00028","DOIUrl":"10.1162/cpsy_a_00028","url":null,"abstract":"<p><p>Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations. We introduce a hierarchical Bayesian implementation of a class of strategic learning models, experience-weighted attraction (EWA), that is widely used in behavioral game theory. Importantly, this approach provides a unified framework for capturing between- and within-participant variation, including changes associated with disease progression, comorbidity, and treatment status. We show using simulated data that our hierarchical Bayesian approach outperforms representative agent and individual-level estimation methods that are commonly used in extant literature, with respect to parameter estimation and uncertainty quantification. Furthermore, using an empirical dataset, we demonstrate the value of our approach over competing methods with respect to balancing model fit and complexity. Consistent with the success of hierarchical Bayesian approaches in other areas of behavioral science, our hierarchical Bayesian EWA model represents a powerful and flexible tool to apply to a wide range of behavioral paradigms for studying the interplay between complex human behavior and biological factors.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"4 ","pages":"40-60"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7790055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38803813","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":"Anxiety, avoidance, and sequential evaluation.","authors":"Samuel Zorowitz, Ida Momennejad, Nathaniel D Daw","doi":"10.1162/cpsy_a_00026","DOIUrl":"10.1162/cpsy_a_00026","url":null,"abstract":"<p><p>Anxiety disorders are characterized by a range of aberrations in the processing of and response to threat, but there is little clarity what core pathogenesis might underlie these symptoms. Here we propose that a particular set of unrealistically pessimistic assumptions can distort an agent's behavior and underlie a host of seemingly disparate anxiety symptoms. We formalize this hypothesis in a decision theoretic analysis of maladaptive avoidance and a reinforcement learning model, which shows how a localized bias in beliefs can formally explain a range of phenomena related to anxiety. The core observation, implicit in standard decision theoretic accounts of sequential evaluation, is that the potential for avoidance should be protective: if danger can be avoided later, it poses less threat now. We show how a violation of this assumption - via a pessimistic, false belief that later avoidance will be unsuccessful - leads to a characteristic, excessive propagation of fear and avoidance to situations far antecedent of threat. This single deviation can explain a range of features of anxious behavior, including exaggerated threat appraisals, fear generalization, and persistent avoidance. Simulations of the model reproduce laboratory demonstrations of abnormal decision making in anxiety, including in situations of approach-avoid conflict and planning to avoid losses. The model also ties together a number of other seemingly disjoint phenomena in anxious disorders. For instance, learning under the pessimistic bias captures a hypothesis about the role of anxiety in the later development of depression. The bias itself offers a new formalization of classic insights from the psychiatric literature about the central role of maladaptive beliefs about control and self-efficacy in anxiety. This perspective also extends previous computational accounts of beliefs about control in mood disorders, which neglected the sequential aspects of choice.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"4 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ec/4e/nihms-1694304.PMC8143038.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39032648","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":"Differential Effects of Psychotic Illness on Directed and Random Exploration.","authors":"James A Waltz, Robert C Wilson, Matthew A Albrecht, Michael J Frank, James M Gold","doi":"10.1162/cpsy_a_00027","DOIUrl":"10.1162/cpsy_a_00027","url":null,"abstract":"<p><p>Schizophrenia is associated with a number of deficits in decision-making, but the scope, nature, and cause of these deficits are not completely understood. Here we focus on a particular type of decision, known as the <i>explore/exploit</i> dilemma, in which people must choose between exploiting options that yield relatively known rewards and exploring more ambiguous options of uncertain reward probability or magnitude. Previous work has shown that healthy people use two distinct strategies to decide when to explore: directed exploration, which involves choosing options that would reduce uncertainty about the reward values (information seeking), and random exploration (exploring by chance), which describes behavioral variability that is not goal directed. We administered a recently developed gambling task designed to quantify both directed and random exploration to 108 patients with schizophrenia (PSZ) and 33 healthy volunteers (HVs). We found that PSZ patients show reduced directed exploration relative to HVs, but no difference in random exploration. Moreover, patients' directed exploration behavior clusters into two qualitatively different behavioral phenotypes. In the first phenotype, which accounts for the majority of the patients (79%) and is consistent with previously reported behavior, directed exploration is only marginally (but significantly) reduced, suggesting that these patients can use directed exploration, but at a slightly lower level than community controls. In contrast, the second phenotype, comprising 21% of patients, exhibit a form of \"extreme ambiguity aversion,\" in which they almost never choose more informative options, even when they are clearly of higher value. Moreover, in PSZ, deficits in directed exploration were related to measures of intellectual function, whereas random exploration was related to positive symptoms. Taken together, our results suggest that schizophrenia has differential effects on directed and random exploration and that investigating the explore/exploit dilemma in psychosis patients may reveal subgroups of patients with qualitatively different patterns of exploration.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"4 ","pages":"18-39"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25531383","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":"A Robot Model of OC-Spectrum Disorders: Design Framework, Implementation, and First Experiments","authors":"Matthew Lewis, N. Fineberg, Lola Cañamero","doi":"10.1162/cpsy_a_00025","DOIUrl":"https://doi.org/10.1162/cpsy_a_00025","url":null,"abstract":"Computational psychiatry is increasingly establishing itself as a valuable discipline for understanding human mental disorders. However, robot models and their potential for investigating embodied and contextual aspects of mental health have been, to date, largely unexplored. In this article, we present an initial robot model of obsessive-compulsive (OC) spectrum disorders based on an embodied motivation-based control architecture for decision-making in autonomous robots. The OC family of conditions is chiefly characterized by obsessions (recurrent, invasive thoughts) and/or compulsions (an urge to carry out certain repetitive or ritualized behaviors). The design of our robot model follows and illustrates a general design framework that we have proposed to ground research in robot models of mental disorders and to link it with existing methodologies in psychiatry, notably in the design of animal models. To test and validate our model, we present and discuss initial experiments, results, and quantitative and qualitative analyses regarding the compulsive and obsessive elements of OC-spectrum disorders. While this initial stage of development only models basic elements of such disorders, our results already shed light on aspects of the underlying theoretical model that are not obvious simply from consideration of the model.","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"25 1","pages":"40-75"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/cpsy_a_00025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64512008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost Evaluation During Decision-Making in Patients at Early Stages of Psychosis.","authors":"Anna O Ermakova, Nimrod Gileadi, Franziska Knolle, Azucena Justicia, Rachel Anderson, Paul C Fletcher, Michael Moutoussis, Graham K Murray","doi":"10.1162/cpsy_a_00020","DOIUrl":"10.1162/cpsy_a_00020","url":null,"abstract":"<p><p>Jumping to conclusions during probabilistic reasoning is a cognitive bias reliably observed in psychosis and linked to delusion formation. Although the reasons for this cognitive bias are unknown, one suggestion is that psychosis patients may view sampling information as more costly. However, previous computational modeling has provided evidence that patients with chronic schizophrenia jump to conclusions because of noisy decision-making. We developed a novel version of the classical beads task, systematically manipulating the cost of information gathering in four blocks. For 31 individuals with early symptoms of psychosis and 31 healthy volunteers, we examined the numbers of \"draws to decision\" when information sampling had no, a fixed, or an escalating cost. Computational modeling involved estimating a cost of information sampling parameter and a cognitive noise parameter. Overall, patients sampled less information than controls. However, group differences in numbers of draws became less prominent at higher cost trials, where less information was sampled. The attenuation of group difference was not due to floor effects, as in the most costly block, participants sampled more information than an ideal Bayesian agent. Computational modeling showed that, in the condition with no objective cost to information sampling, patients attributed higher costs to information sampling than controls did, Mann-Whitney <i>U</i> = 289, <i>p</i> = 0.007, with marginal evidence of differences in noise parameter estimates, <i>t</i>(60) = 1.86, <i>p</i> = 0.07. In patients, individual differences in severity of psychotic symptoms were statistically significantly associated with higher cost of information sampling, ρ = 0.6, <i>p</i> = 0.001, but not with more cognitive noise, ρ = 0.27, <i>p</i> = 0.14; in controls, cognitive noise predicted aspects of schizotypy (preoccupation and distress associated with delusion-like ideation on the Peters Delusion Inventory). Using a psychological manipulation and computational modeling, we provide evidence that early-psychosis patients jump to conclusions because of attributing higher costs to sampling information, not because of being primarily noisy decision makers.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"3 ","pages":"18-39"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6436576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37107909","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":"Multiple Dissociations Between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence From Computationally Informed EEG.","authors":"James F Cavanagh,&nbsp;Andrew W Bismark,&nbsp;Michael J Frank,&nbsp;John J B Allen","doi":"10.1162/cpsy_a_00024","DOIUrl":"10.1162/cpsy_a_00024","url":null,"abstract":"<p><p>In this report, we provide the first evidence that mood and anxiety dimensions are associated with unique aspects of EEG responses to reward and punishment, respectively. We reanalyzed data from our prior publication of a categorical depiction of depression to address more sophisticated dimensional hypotheses. Highly symptomatic depressed individuals (<i>N</i> = 46) completed a probabilistic learning task with concurrent EEG. Measures of anxiety and depression symptomatology were significantly correlated with each other; however, only anxiety predicted better avoidance learning due to a tighter coupling of negative prediction error signaling with punishment-specific EEG features. In contrast, depression predicted a smaller reward-related EEG feature, but this did not affect prediction error coupling or the ability to learn from reward. We suggest that this reward-related alteration reflects motivational or hedonic aspects of reward and not a diminishment in the ability to represent the information content of reinforcements. These findings compel further research into the domain-specific neural systems underlying dimensional aspects of psychiatric disease.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"3 ","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/cpsy_a_00024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37295718","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":"A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision.","authors":"Hayato Idei,&nbsp;Shingo Murata,&nbsp;Yiwen Chen,&nbsp;Yuichi Yamashita,&nbsp;Jun Tani,&nbsp;Tetsuya Ogata","doi":"10.1162/cpsy_a_00019","DOIUrl":"https://doi.org/10.1162/cpsy_a_00019","url":null,"abstract":"Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thoroughly investigated. Using a humanoid robot controlled by a neural network using a precision-weighted prediction error minimization mechanism, it is suggested that both increased and decreased sensory precision could induce the behavioral rigidity characterized by resistance to change that is characteristic of autistic behavior. Specifically, decreased sensory precision caused any error signals to be disregarded, leading to invariability of the robot’s intention, while increased sensory precision caused an excessive response to error signals, leading to fluctuations and subsequent fixation of intention. The results may provide a system-level explanation of mechanisms underlying different types of behavioral rigidity in autism spectrum and other psychiatric disorders. In addition, our findings suggest that symptoms caused by decreased and increased sensory precision could be distinguishable by examining the internal experience of patients and neural activity coding prediction error signals in the biological brain.","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"2 ","pages":"164-182"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/cpsy_a_00019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9166819","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":"A Dynamical Bifurcation Model of Bipolar Disorder Based on Learned Expectation and Asymmetry in Mood Sensitivity.","authors":"Shyr-Shea Chang,&nbsp;Tom Chou","doi":"10.1162/cpsy_a_00021","DOIUrl":"https://doi.org/10.1162/cpsy_a_00021","url":null,"abstract":"<p><p>Bipolar disorder is a common psychiatric dysfunction characterized by recurring episodes of mania and depression. Despite its prevalence, the causes and mechanisms of bipolar disorder remain largely unknown. Recently, theories focusing on the interaction between emotion and behavior, including those based on dysregulation of the so-called behavioral approach system (BAS), have gained popularity. Mathematical models built on this principle predict bistability in mood and do not invoke intrinsic biological rhythms that may arise from interactions between mood and expectation. Here we develop and analyze a model with clinically meaningful and modifiable parameters that incorporates the interaction between mood and expectation. Our nonlinear model exhibits a transition to limit cycle behavior when a mood-sensitivity parameter exceeds a threshold value, signaling a transition to a bipolar state. The model also predicts that asymmetry in response to positive and negative events can induce unipolar depression/mania, consistent with clinical observations. We analyze the model with asymmetric mood sensitivities and show that large unidirectional mood sensitivity can lead to bipolar disorder. Finally, we show how observed effects of lithium- and antidepressant-induced mania can be explained within the framework of our proposed model.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":" ","pages":"205-222"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/cpsy_a_00021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36851641","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":"Active Inference and Auditory Hallucinations.","authors":"David Benrimoh,&nbsp;Thomas Parr,&nbsp;Peter Vincent,&nbsp;Rick A Adams,&nbsp;Karl Friston","doi":"10.1162/cpsy_a_00022","DOIUrl":"https://doi.org/10.1162/cpsy_a_00022","url":null,"abstract":"<p><p>Auditory verbal hallucinations (AVH) are often distressing symptoms of several neuropsychiatric conditions, including schizophrenia. Using a Markov decision process formulation of active inference, we develop a novel model of AVH as false (positive) inference. Active inference treats perception as a process of hypothesis testing, in which sensory data are used to disambiguate between alternative hypotheses about the world. Crucially, this depends upon a delicate balance between prior beliefs about unobserved (hidden) variables and the sensations they cause. A false inference that a voice is present, even in the absence of auditory sensations, suggests that prior beliefs dominate perceptual inference. Here we consider the computational mechanisms that could cause this imbalance in perception. Through simulation, we show that the content of (and confidence in) prior beliefs depends on beliefs about policies (here sequences of listening and talking) and on beliefs about the reliability of sensory data. We demonstrate several ways in which hallucinatory percepts could occur when an agent expects to hear a voice in the presence of imprecise sensory data. This model expresses, in formal terms, alternative computational mechanisms that underwrite AVH and, speculatively, can be mapped onto neurobiological changes associated with schizophrenia. The interaction of action and perception is important in modeling AVH, given that speech is a fundamentally enactive and interactive process-and that hallucinators often actively engage with their voices.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"2 ","pages":"183-204"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/cpsy_a_00022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9166817","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":"Visual Attention Deficits in Schizophrenia Can Arise From Inhibitory Dysfunction in Thalamus or Cortex.","authors":"Yohan J John,&nbsp;Basilis Zikopoulos,&nbsp;Daniel Bullock,&nbsp;Helen Barbas","doi":"10.1162/cpsy_a_00023","DOIUrl":"10.1162/cpsy_a_00023","url":null,"abstract":"<p><p>Schizophrenia is associated with diverse cognitive deficits, including disorders of attention-related oculomotor behavior. At the structural level, schizophrenia is associated with abnormal inhibitory control in the circuit linking cortex and thalamus. We developed a spiking neural network model that demonstrates how dysfunctional inhibition can degrade attentive gaze control. Our model revealed that perturbations of two functionally distinct classes of cortical inhibitory neurons, or of the inhibitory thalamic reticular nucleus, disrupted processing vital for sustained attention to a stimulus, leading to distractibility. Because perturbation at each circuit node led to comparable but qualitatively distinct disruptions in attentive tracking or fixation, our findings support the search for new eye movement metrics that may index distinct underlying neural defects. Moreover, because the cortico-thalamic circuit is a common motif across sensory, association, and motor systems, the model and extensions can be broadly applied to study normal function and the neural bases of other cognitive deficits in schizophrenia.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":" ","pages":"223-257"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/cpsy_a_00023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36851642","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}