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Intention to learn modulates the impact of reward and punishment on sequence learning.

ABSTRACT: In real-world settings, learning is often characterised as intentional: learners are aware of the goal during the learning process, and the goal of learning is readily dissociable from the awareness of what is learned. Recent evidence has shown that reward and punishment (collectively referred to as valenced feedback) are important factors that influence performance during learning. Presently, however, studies investigating the impact of valenced feedback on skill learning have only considered unintentional learning, and therefore the interaction between intentionality and valenced feedback has not been systematically examined. The present study investigated how reward and punishment impact behavioural performance when participants are instructed to learn in a goal-directed fashion (i.e. intentionally) rather than unintentionally. In Experiment 1, participants performed the serial response time task with reward, punishment, or control feedback and were instructed to ignore the presence of the sequence, i.e., learn unintentionally. Experiment 2 followed the same design, but participants were instructed to intentionally learn the sequence. We found that punishment significantly benefitted performance during learning only when participants learned unintentionally, and we observed no effect of punishment when participants learned intentionally. Thus, the impact of feedback on performance may be influenced by goal of the learner.

SUBMITTER: Steel A

PROVIDER: S-EPMC7264311 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Intention to learn modulates the impact of reward and punishment on sequence learning.

Steel Adam A Baker Chris I CI Stagg Charlotte J CJ

Scientific reports 20200601 1

In real-world settings, learning is often characterised as intentional: learners are aware of the goal during the learning process, and the goal of learning is readily dissociable from the awareness of what is learned. Recent evidence has shown that reward and punishment (collectively referred to as valenced feedback) are important factors that influence performance during learning. Presently, however, studies investigating the impact of valenced feedback on skill learning have only considered u ...[more]

PMID: 32483289

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Project description:In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.

Dataset Information

Intention to learn modulates the impact of reward and punishment on sequence learning.

Publications

Intention to learn modulates the impact of reward and punishment on sequence learning.

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