Dataset Information

Dysfunctional representation of expected value is associated with reinforcement-based decision-making deficits in adolescents with conduct problems.

ABSTRACT:

Background

Previous work has shown that patients with conduct problems (CP) show impairments in reinforcement-based decision-making. However, studies with patients have not previously demonstrated any relationships between impairment in any of the neurocomputations underpinning reinforcement-based decision-making and specific symptom sets [e.g. level of CP and/or callous-unemotional (CU) traits].

Methods

Seventy-two youths [20 female, mean age = 13.81 (SD = 2.14), mean IQ = 102.34 (SD = 10.99)] from a residential treatment program and the community completed a passive avoidance task while undergoing functional MRI.

Results

Greater levels of CP were associated with poorer task performance. Reduced representation of expected values (EV) when making avoidance responses within bilateral anterior insula cortex/inferior frontal gyrus (AIC/iFG) and striatum was associated with greater levels of CP but not CU traits.

Conclusions

The current data indicate that difficulties in the use of value information to motivate decisions to avoid suboptimal choices are associated with increased levels of CP (though not severity of CU traits). Moreover, they account for the behavioral deficits observed during reinforcement-based decision-making in youth with CP. In short, an individual's relative failure to utilize value information within AIC/iFG to avoid bad choices is associated with elevated levels of CP.

SUBMITTER: White SF

PROVIDER: S-EPMC4958524 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Publications

Dysfunctional representation of expected value is associated with reinforcement-based decision-making deficits in adolescents with conduct problems.

White Stuart F SF Tyler Patrick M PM Erway Anna K AK Botkin Mary L ML Kolli Venkata V Meffert Harma H Pope Kayla K Blair James R JR

Journal of child psychology and psychiatry, and allied disciplines 20160408 8

<h4>Background</h4>Previous work has shown that patients with conduct problems (CP) show impairments in reinforcement-based decision-making. However, studies with patients have not previously demonstrated any relationships between impairment in any of the neurocomputations underpinning reinforcement-based decision-making and specific symptom sets [e.g. level of CP and/or callous-unemotional (CU) traits].<h4>Methods</h4>Seventy-two youths [20 female, mean age = 13.81 (SD = 2.14), mean IQ = 102.34 ...[more]

PMID: 27062170

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Project description:BackgroundSurgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.MethodsCochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.ResultsRequirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.ConclusionTraditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Dataset Information

Dysfunctional representation of expected value is associated with reinforcement-based decision-making deficits in adolescents with conduct problems.

Background

Methods

Results

Conclusions

Publications

Dysfunctional representation of expected value is associated with reinforcement-based decision-making deficits in adolescents with conduct problems.

Similar Datasets

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