Trial reportPsychopharmacology2019
Reward and avoidance learning in the context of aversive environments and possible implications for depressive symptoms.
Trial report in Psychopharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Using computational models of learning to advance cognitive behavioral therapy.Communications psychology · 2025Review
- Childhood adversity and mind wandering: the mediating role of cognitive flexibility and habitual tendencies.European journal of psychotraumatology · 2024Article
- Computational perspectives on human fear and anxiety.Neuroscience and biobehavioral reviews · 2023Review
- Model-based prioritization for acquiring protection.PLoS computational biology · 2022Article
- Stronger Prejudices Are Associated With Decreased Model-Based Control.Frontiers in psychology · 2021Article
- Mechanisms Underlying Unconscious Processing and Their Alterations in Post-traumatic Stress Disorder: Neuroimaging of Zero Monetary Outcomes Contextually Framed as "No Losses" vs. "No Gains".Frontiers in neuroscience · 2020Article
- Editorial: bridging the gap with computational and translational psychopharmacology.Psychopharmacology · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
backgroundAversive stimuli in the environment influence human actions. This includes valence-dependent influences on action selection, e.g., increased avoidance but decreased approach behavior. However, it is yet unclear how aversive stimuli interact with complex learning and decision-making in the reward and avoidance domain. Moreover, the underlying computational mechanisms of these decision-making biases are unknown.
methodsTo elucidate these mechanisms, 54 healthy young male subjects performed a two-step sequential decision-making task, which allows to computationally model different aspects of learning, e.g., model-free, habitual, and model-based, goal-directed learning. We used a within-subject design, crossing task valence (reward vs. punishment learning) with emotional context (aversive vs. neutral background stimuli). We analyzed choice data, applied a computational model, and performed simulations.
resultsWhereas model-based learning was not affected, aversive stimuli interacted with model-free learning in a way that depended on task valence. Thus, aversive stimuli increased model-free avoidance learning but decreased model-free reward learning. The computational model confirmed this effect: the parameter lambda that indicates the influence of reward prediction errors on decision values was increased in the punishment condition but decreased in the reward condition when aversive stimuli were present. Further, by using the inferred computational parameters to simulate choice data, our effects were captured. Exploratory analyses revealed that the observed biases were associated with subclinical depressive symptoms.
conclusionOur data show that aversive environmental stimuli affect complex learning and decision-making, which depends on task valence. Further, we provide a model of the underlying computations of this affective modulation. Finally, our finding of increased decision-making biases in subjects reporting subclinical depressive symptoms matches recent reports of amplified Pavlovian influences on action selection in depression and suggests a potential vulnerability factor for mood disorders. We discuss our findings in the light of the involvement of the neuromodulators serotonin and dopamine.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.