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Hitchcock PF, Frank MJ. The challenge of learning adaptive mental behavior. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2024; 133:413-426. [PMID: 38815082 PMCID: PMC11229419 DOI: 10.1037/abn0000924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
Many psychotherapies aim to help people replace maladaptive mental behaviors (such as those leading to unproductive worry) with more adaptive ones (such as those leading to active problem solving). Yet, little is known empirically about how challenging it is to learn adaptive mental behaviors. Mental behaviors entail taking mental operations and thus may be more challenging to perform than motor actions; this challenge may enhance or impair learning. In particular, challenge when learning is often desirable because it improves retention. Yet, it is also plausible that the necessity of carrying out mental operations interferes with learning the expected values of mental actions by impeding credit assignment: the process of updating an action's value after reinforcement. Then, it may be more challenging not only to perform-but also to learn the consequences of-mental (vs. motor) behaviors. We designed a task to assess learning to take adaptive mental versus motor actions via matched probabilistic feedback. In two experiments (N = 300), most participants found it more difficult to learn to select optimal mental (vs. motor) actions, as evident in worse accuracy not only in a learning but also test (retention) phase. Computational modeling traced this impairment to an indicator of worse credit assignment (impaired construction and maintenance of expected values) when learning mental actions, accounting for worse accuracy in the learning and retention phases. The results suggest that people have particular difficulty learning adaptive mental behavior and pave the way for novel interventions to scaffold credit assignment and promote adaptive thinking. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Peter F. Hitchcock
- Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI
- Department of Psychology, Emory University, Atlanta GA
| | - Michael J. Frank
- Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI
- Carney Institute for Brain Science, Brown University, Providence, RI
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Cheng X, Wang L, Lv Q, Wu H, Huang X, Yuan J, Sun X, Zhao X, Yan C, Yi Z. Reduced learning bias towards the reward context in medication-naive first-episode schizophrenia patients. BMC Psychiatry 2022; 22:123. [PMID: 35172748 PMCID: PMC8851841 DOI: 10.1186/s12888-021-03682-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 12/28/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Reinforcement learning has been proposed to contribute to the development of amotivation in individuals with schizophrenia (SZ). Accumulating evidence suggests dysfunctional learning in individuals with SZ in Go/NoGo learning and expected value representation. However, previous findings might have been confounded by the effects of antipsychotic exposure. Moreover, reinforcement learning also rely on the learning context. Few studies have examined the learning performance in reward and loss-avoidance context separately in medication-naïve individuals with first-episode SZ. This study aimed to explore the behaviour profile of reinforcement learning performance in medication-naïve individuals with first-episode SZ, including the contextual performance, the Go/NoGo learning and the expected value representation performance. METHODS Twenty-nine medication-naïve individuals with first-episode SZ and 40 healthy controls (HCs) who have no significant difference in age and gender, completed the Gain and Loss Avoidance Task, a reinforcement learning task involving stimulus pairs presented in both the reward and loss-avoidance context. We assessed the group difference in accuracy in the reward and loss-avoidance context, the Go/NoGo learning and the expected value representation. The correlations between learning performance and the negative symptom severity were examined. RESULTS Individuals with SZ showed significantly lower accuracy when learning under the reward than the loss-avoidance context as compared to HCs. The accuracies under the reward context (90%win- 10%win) in the Acquisition phase was significantly and negatively correlated with the Scale for the Assessment of Negative Symptoms (SANS) avolition scores in individuals with SZ. On the other hand, individuals with SZ showed spared ability of Go/NoGo learning and expected value representation. CONCLUSIONS Despite our small sample size and relatively modest findings, our results suggest possible reduced learning bias towards reward context among medication-naïve individuals with first-episode SZ. The reward learning performance was correlated with amotivation symptoms. This finding may facilitate our understanding of the underlying mechanism of negative symptoms. Reinforcement learning performance under the reward context may be important to better predict and prevent the development of schizophrenia patients' negative symptom, especially amotivation.
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Affiliation(s)
- Xiaoyan Cheng
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China ,grid.24516.340000000123704535Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Lingling Wang
- grid.9227.e0000000119573309Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China ,grid.410726.60000 0004 1797 8419Department of Psychology, University of Chinese Academy of Sciences, Beijing, China ,grid.22069.3f0000 0004 0369 6365Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062 China
| | - Qinyu Lv
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China
| | - Haisu Wu
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China
| | - Xinxin Huang
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China
| | - Jie Yuan
- grid.24516.340000000123704535Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Xirong Sun
- grid.24516.340000000123704535Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Xudong Zhao
- grid.24516.340000000123704535Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China
| | - Chao Yan
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China.
| | - Zhenghui Yi
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Shanghai, China.
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Dam VH, Stenbæk DS, Köhler-Forsberg K, Ip C, Ozenne B, Sahakian BJ, Knudsen GM, Jørgensen MB, Frokjaer VG. Hot and cold cognitive disturbances in antidepressant-free patients with major depressive disorder: a NeuroPharm study. Psychol Med 2021; 51:2347-2356. [PMID: 32317043 PMCID: PMC8506354 DOI: 10.1017/s0033291720000938] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 02/05/2020] [Accepted: 03/24/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND Cognitive disturbances are common and disabling features of major depressive disorder (MDD). Previous studies provide limited insight into the co-occurrence of hot (emotion-dependent) and cold (emotion-independent) cognitive disturbances in MDD. Therefore, we here map both hot and cold cognition in depressed patients compared to healthy individuals. METHODS We collected neuropsychological data from 92 antidepressant-free MDD patients and 103 healthy controls. All participants completed a comprehensive neuropsychological test battery assessing hot cognition including emotion processing, affective verbal memory and social cognition as well as cold cognition including verbal and working memory and reaction time. RESULTS The depressed patients showed small to moderate negative affective biases on emotion processing outcomes, moderate increases in ratings of guilt and shame and moderate deficits in verbal and working memory as well as moderately slowed reaction time compared to healthy controls. We observed no correlations between individual cognitive tasks and depression severity in the depressed patients. Lastly, an exploratory cluster analysis suggested the presence of three cognitive profiles in MDD: one characterised predominantly by disturbed hot cognitive functions, one characterised predominantly by disturbed cold cognitive functions and one characterised by global impairment across all cognitive domains. Notably, the three cognitive profiles differed in depression severity. CONCLUSION We identified a pattern of small to moderate disturbances in both hot and cold cognition in MDD. While none of the individual cognitive outcomes mapped onto depression severity, cognitive profile clusters did. Overall cognition-based stratification tools may be useful in precision medicine approaches to MDD.
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Affiliation(s)
- V. H. Dam
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - D. S. Stenbæk
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
| | - K. Köhler-Forsberg
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Psychiatric Center Copenhagen, Copenhagen University Hospital Rigshospitalet, Denmark
| | - C. Ip
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark
| | - B. Ozenne
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
- Department of Public Health, Section of Biostatistics, University of Copenhagen, Denmark
| | - B. J. Sahakian
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Behavioral and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - G. M. Knudsen
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - M. B. Jørgensen
- Psychiatric Center Copenhagen, Copenhagen University Hospital Rigshospitalet, Denmark
| | - V. G. Frokjaer
- Neurobiology Research Unit, the Neuroscience Centre, Copenhagen University Hospital Rigshospitalet, Denmark
- Psychiatric Center Copenhagen, Copenhagen University Hospital Rigshospitalet, Denmark
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Saleh Y, Jarratt-Barnham I, Fernandez-Egea E, Husain M. Mechanisms Underlying Motivational Dysfunction in Schizophrenia. Front Behav Neurosci 2021; 15:709753. [PMID: 34566594 PMCID: PMC8460905 DOI: 10.3389/fnbeh.2021.709753] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/20/2021] [Indexed: 12/24/2022] Open
Abstract
Negative symptoms are a debilitating feature of schizophrenia which are often resistant to pharmacological intervention. The mechanisms underlying them remain poorly understood, and diagnostic methods rely on phenotyping through validated questionnaires. Deeper endo-phenotyping is likely to be necessary in order to improve current understanding. In the last decade, valuable behavioural insights have been gained through the use of effort-based decision making (EBDM) tasks. These have highlighted impairments in reward-related processing in schizophrenia, particularly associated with negative symptom severity. Neuroimaging investigations have related these changes to dysfunction within specific brain networks including the ventral striatum (VS) and frontal brain regions. Here, we review the behavioural and neural evidence associated with negative symptoms, shedding light on potential underlying mechanisms and future therapeutic possibilities. Findings in the literature suggest that schizophrenia is characterised by impaired reward based learning and action selection, despite preserved hedonic responses. Associations between amotivation and reward-processing deficits have not always been clear, and may be mediated by factors including cognitive dysfunction or dysfunctional or self-defeatist beliefs. Successful endo-phenotyping of negative symptoms as a function of objective behavioural and neural measurements is crucial in advancing our understanding of this complex syndrome. Additionally, transdiagnostic research–leveraging findings from other brain disorders, including neurological ones–can shed valuable light on the possible common origins of motivation disorders across diseases and has important implications for future treatment development.
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Affiliation(s)
- Youssuf Saleh
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Isaac Jarratt-Barnham
- Department of Psychiatry, Herchel Smith Building for Brain & Mind Sciences, University of Cambridge, Cambridge, United Kingdom.,Cambridge Psychosis Centre, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Emilio Fernandez-Egea
- Department of Psychiatry, Herchel Smith Building for Brain & Mind Sciences, University of Cambridge, Cambridge, United Kingdom.,Cambridge Psychosis Centre, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, United Kingdom
| | - Masud Husain
- Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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Paulus MP, Thompson WK. Computational approaches and machine learning for individual-level treatment predictions. Psychopharmacology (Berl) 2021; 238:1231-1239. [PMID: 31134293 PMCID: PMC6879811 DOI: 10.1007/s00213-019-05282-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 05/17/2019] [Indexed: 12/24/2022]
Abstract
RATIONALE The impact of neuroscience-based approaches for psychiatry on pragmatic clinical decision-making has been limited. Although neuroscience has provided insights into basic mechanisms of neural function, these insights have not improved the ability to generate better assessments, prognoses, diagnoses, or treatment of psychiatric conditions. OBJECTIVES To integrate the emerging findings in machine learning and computational psychiatry to address the question: what measures that are not derived from the patient's self-assessment or the assessment by a trained professional can be used to make more precise predictions about the individual's current state, the individual's future disease trajectory, or the probability to respond to a particular intervention? RESULTS Currently, the ability to use individual differences to predict differential outcomes is very modest possibly related to the fact that the effect sizes of interventions are small. There is emerging evidence of genetic and neuroimaging-based heterogeneity of psychiatric disorders, which contributes to imprecise predictions. Although the use of machine learning tools to generate clinically actionable predictions is still in its infancy, these approaches may identify subgroups enabling more precise predictions. In addition, computational psychiatry might provide explanatory disease models based on faulty updating of internal values or beliefs. CONCLUSIONS There is a need for larger studies, clinical trials using machine learning, or computational psychiatry model parameters predictions as actionable outcomes, comparing alternative explanatory computational models, and using translational approaches that apply similar paradigms and models in humans and animals.
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Affiliation(s)
- Martin P Paulus
- Laureate Institute for Brain Research, 6655 S Ave Tulsa, Yale, OK, 74136-3326, USA.
| | - Wesley K Thompson
- Family Medicine and Public Health, University of California San Diego, San Diego, CA, USA
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Culbreth AJ, Waltz JA, Frank MJ, Gold JM. Retention of Value Representations Across Time in People With Schizophrenia and Healthy Control Subjects. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:420-428. [PMID: 32712211 PMCID: PMC7708393 DOI: 10.1016/j.bpsc.2020.05.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/24/2020] [Accepted: 05/18/2020] [Indexed: 02/01/2023]
Abstract
BACKGROUND The current study aimed to further etiological understanding of the psychological mechanisms underlying negative symptoms in people with schizophrenia. Specifically, we tested whether negative symptom severity is associated with reduced retention of reward-related information over time and thus a degraded ability to utilize such information to guide future action selection. METHODS Forty-four patients with a diagnosis of schizophrenia or schizoaffective disorder and 28 healthy control volunteers performed a probabilistic reinforcement-learning task involving stimulus pairs in which choices resulted in reward or in loss avoidance. Following training, participants indicated their valuation of learned stimuli in a test/transfer phase. The test/transfer phase was administered immediately following training and 1 week later. Percent retention was defined as accuracy at week-long delay divided by accuracy at immediate delay. RESULTS Healthy control subjects and people with schizophrenia showed similarly robust retention of reinforcement learning over a 1-week delay interval. However, in the schizophrenia group, negative symptom severity was associated with reduced retention of information regarding the value of actions across a week-long interval. This pattern was particularly notable for stimuli associated with reward compared with loss avoidance. CONCLUSIONS Our results show that although individuals with schizophrenia may initially learn about rewarding aspects of their environment, such learning decays at a more rapid rate in patients with severe negative symptoms. Thus, previously learned reward-related information may be more difficult to access to guide future decision making and to motivate action selection.
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Affiliation(s)
- Adam J Culbreth
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland, School of Medicine, Baltimore, Maryland.
| | - James A Waltz
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland, School of Medicine, Baltimore, Maryland
| | - Michael J Frank
- Department of Cognitive, Linguistics, and Psychological Sciences, Brown University, Providence, Rhode Island
| | - James M Gold
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland, School of Medicine, Baltimore, Maryland
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Hernaus D, Frank MJ, Brown EC, Brown JK, Gold JM, Waltz JA. Impaired Expected Value Computations in Schizophrenia Are Associated With a Reduced Ability to Integrate Reward Probability and Magnitude of Recent Outcomes. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 4:280-290. [PMID: 30683607 DOI: 10.1016/j.bpsc.2018.11.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/08/2018] [Accepted: 11/27/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Motivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on "valueless" stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits. METHODS Healthy volunteers (n = 38) and PSZ (n = 49) completed a learning paradigm consisting of four stimulus pairs. Reward magnitude (3, 2, 1, 0 points) and probability (90%, 80%, 20%, 10%) determined each stimulus's EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value. RESULTS PSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability × magnitude) between two competing stimuli increased. Using a previously validated computational hybrid model, PSZ relied less on EV ("Q-learning") and more on stimulus-response learning ("actor-critic"), which correlated with Scale for the Assessment of Negative Symptoms motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability. CONCLUSIONS Probability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on valueless stimulus-response associations. Confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work provides a first mechanistic explanation of complex EV-based learning deficits in PSZ with motivational deficits that arise from an inability to combine information from different reward modalities.
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Affiliation(s)
- Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland.
| | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island; Department of Psychiatry and Brown Institute for Brain Science, Brown University, Providence, Rhode Island
| | - Elliot C Brown
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland; Institute for Psychology, University of Lübeck, Lübeck, Germany
| | - Jaime K Brown
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - James M Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - James A Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
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