Dataset Information

Effects of Depressive Symptoms, Feelings, and Interoception on Reward-Based Decision-Making: Investigation Using Reinforcement Learning Model.

ABSTRACT: BACKGROUND:In this study, we examined the relationships between reward-based decision-making in terms of learning rate, memory rate, exploration rate, and depression-related subjective emotional experience, in terms of interoception and feelings, to understand how reward-based decision-making is impaired in depression. METHODS:In all, 52 university students were randomly assigned to an experimental group and a control group. To manipulate interoception, the participants in the experimental group were instructed to tune their internal somatic sense to the skin-conductance-response waveform presented on a display. The participants in the control group were only instructed to stay relaxed. Before and after the manipulation, the participants completed a probabilistic reversal-learning task to assess reward-based decision-making using reinforcement learning modeling. Similarly, participants completed a probe-detection task, a heartbeat-detection task, and self-rated scales. RESULTS:The experimental manipulation of interoception was not successful. In the baseline testing, reinforcement learning modeling indicated a marginally-significant correlation between the exploration rate and depressive symptoms. However, the exploration rate was significantly associated with lower interoceptive attention and higher depressive feeling. CONCLUSIONS:The findings suggest that situational characteristics may be closely involved in reward exploration and highlight the clinically-meaningful possibility that intervention for affective processes may impact reward-based decision-making in those with depression.

SUBMITTER: Ogishima H

PROVIDER: S-EPMC7464008 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Effects of Depressive Symptoms, Feelings, and Interoception on Reward-Based Decision-Making: Investigation Using Reinforcement Learning Model.

Ogishima Hiroyoshi H Maeda Shunta S Tanaka Yuki Y Shimada Hironori H

Brain sciences 20200801 8

<h4>Background</h4>In this study, we examined the relationships between reward-based decision-making in terms of learning rate, memory rate, exploration rate, and depression-related subjective emotional experience, in terms of interoception and feelings, to understand how reward-based decision-making is impaired in depression.<h4>Methods</h4>In all, 52 university students were randomly assigned to an experimental group and a control group. To manipulate interoception, the participants in the exp ...[more]

PMID: 32752282

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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.

Project description:BackgroundGeneral practitioners (GPs) provide the most antidepressant prescriptions and psychologist referrals in Australia, yet little is known about how they decide between treatments for depressive symptoms.AimsThis study examined the decision cues that GPs use when deciding how to treat depressive symptoms and the meaning they attribute to these associations.MethodsStructured interviews were conducted with 16 Australian GPs in a "think-aloud" verbal protocol analysis format. The transcripts were analyzed using content coding and thematic analysis, informed by the dual processes model of decision making.ResultsParticipants associated prescribing antidepressants with severe depressive symptoms, and psychologist referrals were the preferred initial treatment provided patients were willing to engage. Four main themes emerged from the thematic analysis: 1) psychologist as default, 2) the risk is just too high, 3) medication as supplement, and 4) drivers of antidepressants. Contrary to previous findings, participants identified a strong heuristic association between depressive symptoms and psychologist referral. Antidepressant prescription was associated with severe symptoms, higher risk, and a cluster of cues that lead them away from psychologist referral.ConclusionsParticipants demonstrated an oversensitivity to depressive symptom severity, associating psychologist referrals with subclinical depressive symptoms, and starting antidepressants for suicidal ideation and significant functional decline.HighlightsPsychologist referrals were the default preference for GPs when treating depressive symptoms, whereas antidepressants were reserved for patients with elevated risk from severe depressive symptoms and suicidal ideation.Participants' conceptualization of depression severity was oversensitive compared with treatment guidelines.Contrary to treatment guidelines, GPs in this study demonstrated their discomfort for referring patients with suicidal ideation to a psychologist without initiating antidepressants.GPs should consider whether patients within their mild to moderate assessment range can be treated exclusively with more cost-effective means than psychological therapy such as e-mental health programs, guided self-help, and lifestyle changes alone.

Dataset Information

Effects of Depressive Symptoms, Feelings, and Interoception on Reward-Based Decision-Making: Investigation Using Reinforcement Learning Model.

Publications

Effects of Depressive Symptoms, Feelings, and Interoception on Reward-Based Decision-Making: Investigation Using Reinforcement Learning Model.

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