scholarly journals "Learning in reverse: Dopamine errors drive excitatory and inhibitory components of backward conditioning in an outcome-specific manner".

2022 ◽  
Author(s):  
Benjamin M Seitz ◽  
Ivy B Hoang ◽  
Aaron P Blaisdell ◽  
Melissa J Sharpe
Keyword(s):  
Reinforcement Learning ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Learning Deficits ◽  
Backward Conditioning ◽  
Future Prediction ◽  
Midbrain Dopamine ◽  
Inhibitory Components ◽  
Complex Features ◽  
Dopamine Signal

For over two decades, midbrain dopamine was considered synonymous with the prediction error in temporal-difference reinforcement learning. Central to this proposal is the notion that reward-predictive stimuli become endowed with the scalar value of predicted rewards. When these cues are subsequently encountered, their predictive value is compared to the value of the actual reward received allowing for the calculation of prediction errors. Phasic firing of dopamine neurons was proposed to reflect this computation, facilitating the backpropagation of value from the predicted reward to the reward-predictive stimulus, thus reducing future prediction errors. There are two critical assumptions of this proposal: 1) that dopamine errors can only facilitate learning about scalar value and not more complex features of predicted rewards, and 2) that the dopamine signal can only be involved in anticipatory learning in which cues or actions precede rewards. Recent work has challenged the first assumption, demonstrating that phasic dopamine signals across species are involved in learning about more complex features of the predicted outcomes, in a manner that transcends this value computation. Here, we tested the validity of the second assumption. Specifically, we examined whether phasic midbrain dopamine activity would be necessary for backward conditioning- when a neutral cue reliably follows a rewarding outcome. Using a specific Pavlovian-to-Instrumental Transfer (PIT) procedure, we show rats learn both excitatory and inhibitory components of a backward association, and that this association entails knowledge of the specific identity of the reward and cue. We demonstrate that brief optogenetic inhibition of ventral tegmental area dopamine (VTA DA) neurons timed to the transition between the reward and cue, reduces both of these components of backward conditioning. These findings suggest VTA DA neurons are capable of facilitating associations between contiguously occurring events, regardless of the content of those events. We conclude that these data are in line with suggestions that the VTA DA error acts as a universal teaching signal. This may provide insight into why dopamine function has been implicated in a myriad of psychological disorders that are characterized by very distinct reinforcement-learning deficits.

scholarly journals Rethinking dopamine as generalized prediction error

2017 ◽  
Author(s):  
Matthew P.H. Gardner ◽  
Geoffrey Schoenbaum ◽  
Samuel J. Gershman
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Midbrain Dopamine ◽  
Sensory Prediction ◽  
Lines Of Evidence ◽  
Midbrain Dopamine Neurons

AbstractMidbrain dopamine neurons are commonly thought to report a reward prediction error, as hypothesized by reinforcement learning theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signaling errors in both sensory and reward predictions, dopamine supports a form of reinforcement learning that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and reward prediction errors, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.

scholarly journals Rethinking dopamine as generalized prediction error

2018 ◽  
Vol 285 (1891) ◽  
pp. 20181645 ◽  
Author(s):  
Matthew P. H. Gardner ◽  
Geoffrey Schoenbaum ◽  
Samuel J. Gershman
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Midbrain Dopamine ◽  
Sensory Prediction ◽  
Lines Of Evidence ◽  
Midbrain Dopamine Neurons

Midbrain dopamine neurons are commonly thought to report a reward prediction error (RPE), as hypothesized by reinforcement learning (RL) theory. While this theory has been highly successful, several lines of evidence suggest that dopamine activity also encodes sensory prediction errors unrelated to reward. Here, we develop a new theory of dopamine function that embraces a broader conceptualization of prediction errors. By signalling errors in both sensory and reward predictions, dopamine supports a form of RL that lies between model-based and model-free algorithms. This account remains consistent with current canon regarding the correspondence between dopamine transients and RPEs, while also accounting for new data suggesting a role for these signals in phenomena such as sensory preconditioning and identity unblocking, which ostensibly draw upon knowledge beyond reward predictions.

scholarly journals Reward prediction error in the ERP following unconditioned aversive stimuli

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Harry J. Stewardson ◽  
Thomas D. Sambrook
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Temporal Difference ◽  
Dopamine System ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Midbrain Dopamine ◽  
Human Participants

AbstractReinforcement learning in humans and other animals is driven by reward prediction errors: deviations between the amount of reward or punishment initially expected and that which is obtained. Temporal difference methods of reinforcement learning generate this reward prediction error at the earliest time at which a revision in reward or punishment likelihood is signalled, for example by a conditioned stimulus. Midbrain dopamine neurons, believed to compute reward prediction errors, generate this signal in response to both conditioned and unconditioned stimuli, as predicted by temporal difference learning. Electroencephalographic recordings of human participants have suggested that a component named the feedback-related negativity (FRN) is generated when this signal is carried to the cortex. If this is so, the FRN should be expected to respond equivalently to conditioned and unconditioned stimuli. However, very few studies have attempted to measure the FRN’s response to unconditioned stimuli. The present study attempted to elicit the FRN in response to a primary aversive stimulus (electric shock) using a design that varied reward prediction error while holding physical intensity constant. The FRN was strongly elicited, but earlier and more transiently than typically seen, suggesting that it may incorporate other processes than the midbrain dopamine system.

scholarly journals Midbrain dopamine neurons signal aversion in a reward-context-dependent manner

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Hideyuki Matsumoto ◽  
Ju Tian ◽  
Naoshige Uchida ◽  
Mitsuko Watabe-Uchida
Keyword(s):  
Dopamine Neurons ◽  
Prediction Errors ◽  
Dependent Manner ◽  
Value Prediction ◽  
Signal Value ◽  
Aversive Events ◽  
High Reward ◽  
Midbrain Dopamine ◽  
Dopamine Signaling ◽  
Reward And Punishment

Dopamine is thought to regulate learning from appetitive and aversive events. Here we examined how optogenetically-identified dopamine neurons in the lateral ventral tegmental area of mice respond to aversive events in different conditions. In low reward contexts, most dopamine neurons were exclusively inhibited by aversive events, and expectation reduced dopamine neurons’ responses to reward and punishment. When a single odor predicted both reward and punishment, dopamine neurons’ responses to that odor reflected the integrated value of both outcomes. Thus, in low reward contexts, dopamine neurons signal value prediction errors (VPEs) integrating information about both reward and aversion in a common currency. In contrast, in high reward contexts, dopamine neurons acquired a short-latency excitation to aversive events that masked their VPE signaling. Our results demonstrate the importance of considering the contexts to examine the representation in dopamine neurons and uncover different modes of dopamine signaling, each of which may be adaptive for different environments.

scholarly journals Dopamine transients delivered in learning contexts do not act as model-free prediction errors

2019 ◽  
Author(s):  
Melissa J. Sharpe ◽  
Hannah M. Batchelor ◽  
Lauren E. Mueller ◽  
Chun Yun Chang ◽  
Etienne J.P. Maes ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Associative Learning ◽  
Prediction Error ◽  
Error Term ◽  
Neural Correlates ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Excess Value

AbstractDopamine neurons fire transiently in response to unexpected rewards. These neural correlates are proposed to signal the reward prediction error described in model-free reinforcement learning algorithms. This error term represents the unpredicted or ‘excess’ value of the rewarding event. In model-free reinforcement learning, this value is then stored as part of the learned value of any antecedent cues, contexts or events, making them intrinsically valuable, independent of the specific rewarding event that caused the prediction error. In support of equivalence between dopamine transients and this model-free error term, proponents cite causal optogenetic studies showing that artificially induced dopamine transients cause lasting changes in behavior. Yet none of these studies directly demonstrate the presence of cached value under conditions appropriate for associative learning. To address this gap in our knowledge, we conducted three studies where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquired value and instead entered into value-independent associative relationships with the other cues or rewards. These results show that dopamine transients, constrained within appropriate learning situations, support valueless associative learning.

scholarly journals Dopamine reward prediction error coding

2016 ◽  
Vol 18 (1) ◽  
pp. 23-32 ◽  
Keyword(s):  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Negative Prediction ◽  
Baseline Activity ◽  
Error Coding ◽  
Reward Value ◽  
Dopamine Signal

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans

2014 ◽  
Vol 26 (3) ◽  
pp. 635-644 ◽  
Author(s):  
Olav E. Krigolson ◽  
Cameron D. Hassall ◽  
Todd C. Handy
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Human Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Neural Basis ◽  
Error Related Negativity ◽  
Reward Positivity ◽  
Reward Prediction ◽  
Feedback Error

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors—discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833–1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129–141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769–776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679–709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

scholarly journals Midbrain dopamine neurons provide teaching signals for goal-directed navigation

2021 ◽  
Author(s):  
Karolina Farrell ◽  
Armin Lak ◽  
Aman B Saleem
Keyword(s):  
Virtual Reality ◽  
Visual Cues ◽  
Task Engagement ◽  
Dopamine Neurons ◽  
List Type ◽  
Prediction Errors ◽  
Learning Stage ◽  
Midbrain Dopamine ◽  
Improved Performance ◽  
Midbrain Dopamine Neurons

SummaryIn naturalistic environments, animals navigate in order to harvest rewards. Successful goal-directed navigation requires learning to accurately estimate location and select optimal state-dependent actions. Midbrain dopamine neurons are known to be involved in reward value learning1–13. They have also been linked to reward location learning, as they play causal roles in place preference14,15 and enhance spatial memory16–21. Dopamine neurons are therefore ideally placed to provide teaching signals for goal-directed navigation. To test this, we imaged dopamine neural activity as mice learned to navigate in a closed-loop virtual reality corridor and lick to report the reward location. Across learning, phasic dopamine responses developed to visual cues and trial outcome that resembled reward prediction errors and indicated the animal’s estimate of the reward location. We also observed the development of pre-reward ramping activity, the slope of which was modulated by both learning stage and task engagement. The slope of the dopamine ramp was correlated with the accuracy of licks in the next trial, suggesting that the ramp sculpted accurate location-specific action during navigation. Our results indicate that midbrain dopamine neurons, through both their phasic and ramping activity, provide teaching signals for improving goal-directed navigation.HighlightsWe investigated midbrain dopamine activity in mice learning a goal-directed navigation task in virtual realityPhasic dopamine signals reflected prediction errors with respect to subjective estimate of reward locationA slow ramp in dopamine activity leading up to reward location developed over learning and was enhanced with task engagementPositive ramp slopes were followed by improved performance on subsequent trials, suggesting a teaching role during goal-directed navigation

scholarly journals A Pallidus-Habenula-Dopamine Pathway Signals Inferred Stimulus Values

2010 ◽  
Vol 104 (2) ◽  
pp. 1068-1076 ◽  
Author(s):  
Ethan S. Bromberg-Martin ◽  
Masayuki Matsumoto ◽  
Simon Hong ◽  
Okihide Hikosaka
Keyword(s):  
Reinforcement Learning ◽  
Dopamine Neurons ◽  
Neural Pathway ◽  
Dopamine System ◽  
Lateral Habenula ◽  
Reward Value ◽  
Saccade Task ◽  
Midbrain Dopamine ◽  
Behavioral Evidence ◽  
Midbrain Dopamine Neurons

The reward value of a stimulus can be learned through two distinct mechanisms: reinforcement learning through repeated stimulus-reward pairings and abstract inference based on knowledge of the task at hand. The reinforcement mechanism is often identified with midbrain dopamine neurons. Here we show that a neural pathway controlling the dopamine system does not rely exclusively on either stimulus-reward pairings or abstract inference but instead uses a combination of the two. We trained monkeys to perform a reward-biased saccade task in which the reward values of two saccade targets were related in a systematic manner. Animals used each trial's reward outcome to learn the values of both targets: the target that had been presented and whose reward outcome had been experienced (experienced value) and the target that had not been presented but whose value could be inferred from the reward statistics of the task (inferred value). We then recorded from three populations of reward-coding neurons: substantia nigra dopamine neurons; a major input to dopamine neurons, the lateral habenula; and neurons that project to the lateral habenula, located in the globus pallidus. All three populations encoded both experienced values and inferred values. In some animals, neurons encoded experienced values more strongly than inferred values, and the animals showed behavioral evidence of learning faster from experience than from inference. Our data indicate that the pallidus-habenula-dopamine pathway signals reward values estimated through both experience and inference.

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.

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