Optimizing vs. Matching: Response Strategy in a Probabilistic Learning Task is associated with Negative Symptoms of Schizophrenia
Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be id...
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Published in | Schizophrenia research Vol. 127; no. 1-3; pp. 215 - 222 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Amsterdam
Elsevier B.V
01.04.2011
Elsevier |
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Abstract | Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance. |
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AbstractList | Abstract Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance. Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance. Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance.Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are thought to predict important personal characteristics and individual differences. Typically, relatively small proportion of subjects can be identified as optimizers for effectively exploiting the environment and choosing the more rewarding stimulus nearly all of the time. In contrast, the vast majority of subjects behaves sub-optimally and adopts the matching or super-matching strategy, apportioning their responses in a way that matches or slightly exceeds the probabilities of reinforcement. In the present study, we administered a two-choice probability learning paradigm to 51 individuals with schizophrenia (SZ) and 29 healthy controls (NC) to examine whether there are differences in the proportion of subjects falling into these response strategy classifications, and to determine whether task performance is differentially associated with symptom severity and neuropsychological functioning. Although the sample of SZ patients did not differ from NC in overall rate of learning or end performance, significant clinical differences emerged when patients were divided into optimizing, super-matching and matching subgroups based upon task performance. Patients classified as optimizers, who adopted the most advantageous learning strategy, exhibited higher levels of positive and negative symptoms than their matching and super-matching counterparts. Importantly, when both positive and negative symptoms were considered together, only negative symptom severity was a significant predictor of whether a subject would behave optimally, with each one standard deviation increase in negative symptoms increasing the odds of a patient being an optimizer by as much as 80%. These data provide a rare example of a greater clinical impairment being associated with better behavioral performance. |
Author | Strauss, Gregory P. Frank, Michael J. Gold, James M. Waltz, James A. Kasanova, Zuzana |
AuthorAffiliation | 1 Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228 3 Department of Psychiatry and Human Behavior, Brown University, Providence, RI 2 Department of Cognitive, Linguistic & Psychological Sciences, Providence, RI |
AuthorAffiliation_xml | – name: 1 Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228 – name: 2 Department of Cognitive, Linguistic & Psychological Sciences, Providence, RI – name: 3 Department of Psychiatry and Human Behavior, Brown University, Providence, RI |
Author_xml | – sequence: 1 givenname: Zuzana surname: Kasanova fullname: Kasanova, Zuzana email: zkasanova@mprc.umaryland.edu organization: Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228 – sequence: 2 givenname: James A. surname: Waltz fullname: Waltz, James A. organization: Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228 – sequence: 3 givenname: Gregory P. surname: Strauss fullname: Strauss, Gregory P. organization: Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228 – sequence: 4 givenname: Michael J. surname: Frank fullname: Frank, Michael J. organization: Department of Cognitive, Linguistic & Psychological Sciences, Providence, RI – sequence: 5 givenname: James M. surname: Gold fullname: Gold, James M. organization: Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, PO Box 21247, Baltimore, MD 21228 |
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Keywords | Probability Learning Task Matching Schizophrenia Reinforcement Learning Exploration Negative Symptoms Psychosis Human Acquisition process Negative symptom Reinforcement learning Strategy Probability learning |
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Snippet | Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications that are... Abstract Previous research indicates that behavioral performance in simple probability learning tasks can be organized into response strategy classifications... |
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SubjectTerms | Adult Adult and adolescent clinical studies Behavioral Symptoms - etiology Biological and medical sciences Choice Behavior - physiology Exploration Female Humans Male Matching Medical sciences Middle Aged Negative Symptoms Neuropsychological Tests Probability Learning Probability Learning Task Psychiatric Status Rating Scales Psychiatry Psychology. Psychoanalysis. Psychiatry Psychopathology. Psychiatry Psychoses Reinforcement Learning Schizophrenia Schizophrenia - complications Schizophrenic Psychology |
Title | Optimizing vs. Matching: Response Strategy in a Probabilistic Learning Task is associated with Negative Symptoms of Schizophrenia |
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