scholarly journals Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum

2019 ◽  
Author(s):  
William Heffley ◽  
Court Hull
Keyword(s):  
Classical Conditioning ◽  
Learning Rule ◽  
Reward Processing ◽  
Climbing Fibers ◽  
Prediction Errors ◽  
Reward Prediction ◽  
Before And After ◽  
Classical Models ◽  
Cerebellar Learning ◽  
Climbing Fiber Responses

AbstractClassical models of cerebellar learning posit that climbing fibers operate according to a supervised learning rule to instruct changes in motor output by signaling the occurrence of movement errors. However, cerebellar output is also associated with non-motor behaviors, and recently with modulating reward association pathways in the VTA. To test how the cerebellum processes reward related signals in the same type of classical conditioning behavior typically studied to evaluate reward processing in the VTA and striatum, we have used calcium imaging to visualize instructional signals carried by climbing fibers across the lateral cerebellum before and after learning. We find distinct climbing fiber responses in three lateral cerebellar regions that can each signal reward prediction, but not reward prediction errors per se. These instructional signals are well suited to guide cerebellar learning based on reward expectation and enable a cerebellar contribution to reward driven behaviors.

scholarly journals Classical conditioning drives learned reward prediction signals in climbing fibers across the lateral cerebellum

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
William Heffley ◽  
Court Hull
Keyword(s):  
Classical Conditioning ◽  
Learning Rule ◽  
Reward Processing ◽  
Climbing Fibers ◽  
Motor Behaviors ◽  
Reward Prediction ◽  
Before And After ◽  
Classical Models ◽  
Cerebellar Learning ◽  
Climbing Fiber Responses

Classical models of cerebellar learning posit that climbing fibers operate according to a supervised learning rule to instruct changes in motor output by signaling the occurrence of movement errors. However, cerebellar output is also associated with non-motor behaviors, and recently with modulating reward association pathways in the VTA. To test how the cerebellum processes reward related signals in the same type of classical conditioning behavior typically studied to evaluate reward processing in the VTA and striatum, we have used calcium imaging to visualize instructional signals carried by climbing fibers across the lateral cerebellum in mice before and after learning. We find distinct climbing fiber responses in three lateral cerebellar regions that can each signal reward prediction. These instructional signals are well suited to guide cerebellar learning based on reward expectation and enable a cerebellar contribution to reward driven behaviors, suggesting a broad role for the lateral cerebellum in reward-based learning.

scholarly journals Dopamine, Inference, and Uncertainty

2017 ◽  
Vol 29 (12) ◽  
pp. 3311-3326 ◽  
Author(s):  
Samuel J. Gershman
Keyword(s):  
Reinforcement Learning ◽  
Learning Rule ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Stimulus Representation ◽  
Reward Prediction ◽  
Probabilistic Beliefs ◽  
Bayesian Reinforcement Learning ◽  
Simple Error ◽  
The Relationship

The hypothesis that the phasic dopamine response reports a reward prediction error has become deeply entrenched. However, dopamine neurons exhibit several notable deviations from this hypothesis. A coherent explanation for these deviations can be obtained by analyzing the dopamine response in terms of Bayesian reinforcement learning. The key idea is that prediction errors are modulated by probabilistic beliefs about the relationship between cues and outcomes, updated through Bayesian inference. This account can explain dopamine responses to inferred value in sensory preconditioning, the effects of cue preexposure (latent inhibition), and adaptive coding of prediction errors when rewards vary across orders of magnitude. We further postulate that orbitofrontal cortex transforms the stimulus representation through recurrent dynamics, such that a simple error-driven learning rule operating on the transformed representation can implement the Bayesian reinforcement learning update.

scholarly journals Dopamine, Inference, and Uncertainty

2017 ◽  
Author(s):  
Samuel J. Gershman
Keyword(s):  
Reinforcement Learning ◽  
Learning Rule ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Stimulus Representation ◽  
Reward Prediction ◽  
Probabilistic Beliefs ◽  
Bayesian Reinforcement Learning ◽  
Simple Error ◽  
The Relationship

AbstractThe hypothesis that the phasic dopamine response reports a reward prediction error has become deeply entrenched. However, dopamine neurons exhibit several notable deviations from this hypothesis. A coherent explanation for these deviations can be obtained by analyzing the dopamine response in terms of Bayesian reinforcement learning. The key idea is that prediction errors are modulated by probabilistic beliefs about the relationship between cues and outcomes, updated through Bayesian inference. This account can explain dopamine responses to inferred value in sensory preconditioning, the effects of cue pre-exposure (latent inhibition) and adaptive coding of prediction errors when rewards vary across orders of magnitude. We further postulate that orbitofrontal cortex transforms the stimulus representation through recurrent dynamics, such that a simple error-driven learning rule operating on the transformed representation can implement the Bayesian reinforcement learning update.

Reward Prediction Errors Drive Declarative Learning Irrespective of Agency

2020 ◽  
Author(s):  
Kate Ergo ◽  
Luna De Vilder ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Learning Theory ◽  
Learning Effect ◽  
Steady Increase ◽  
Prediction Errors ◽  
Experimental Paradigm ◽  
Reward Prediction ◽  
Declarative Learning

Recent years have witnessed a steady increase in the number of studies investigating the role of reward prediction errors (RPEs) in declarative learning. Specifically, in several experimental paradigms RPEs drive declarative learning; with larger and more positive RPEs enhancing declarative learning. However, it is unknown whether this RPE must derive from the participant’s own response, or whether instead any RPE is sufficient to obtain the learning effect. To test this, we generated RPEs in the same experimental paradigm where we combined an agency and a non-agency condition. We observed no interaction between RPE and agency, suggesting that any RPE (irrespective of its source) can drive declarative learning. This result holds implications for declarative learning theory.

Emotion prediction errors guide socially adaptive behavior

2021 ◽  
Author(s):  
Joseph Heffner ◽  
Jae-Young Son ◽  
Oriel FeldmanHall
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Adaptive Behavior ◽  
Emotional Response ◽  
New Method ◽  
Prediction Errors ◽  
Emotional Experiences ◽  
Past Work ◽  
Reward Prediction ◽  
Expected Outcomes

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a new method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.

scholarly journals A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

2020 ◽  
Author(s):  
Pramod Kaushik ◽  
Jérémie Naudé ◽  
Surampudi Bapi Raju ◽  
Frédéric Alexandre
Keyword(s):  
Classical Conditioning ◽  
Computational Model ◽  
Prediction Error ◽  
Ventral Striatum ◽  
Dopamine Neurons ◽  
System Level ◽  
Reward Prediction Error ◽  
Twin Peaks ◽  
Reward Prediction ◽  
Error Computation

AbstractClassical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.

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