scholarly journals Preconditioned cues have no value

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Melissa J Sharpe ◽  
Hannah M Batchelor ◽  
Geoffrey Schoenbaum
Keyword(s):  
No Value ◽  
Conditioned Reinforcement ◽  
Dopamine Neurons ◽  
Mediated Learning ◽  
Sensory Preconditioning ◽  
Phasic Response ◽  
Model Based ◽  
Model Free ◽  
Midbrain Dopamine ◽  
Conditioned Cues

Sensory preconditioning has been used to implicate midbrain dopamine in model-based learning, contradicting the view that dopamine transients reflect model-free value. However, it has been suggested that model-free value might accrue directly to the preconditioned cue through mediated learning. Here, building on previous work (Sadacca et al., 2016), we address this question by testing whether a preconditioned cue will support conditioned reinforcement in rats. We found that while both directly conditioned and second-order conditioned cues supported robust conditioned reinforcement, a preconditioned cue did not. These data show that the preconditioned cue in our procedure does not directly accrue model-free value and further suggest that the cue may not necessarily access value even indirectly in a model-based manner. If so, then phasic response of dopamine neurons to cues in this setting cannot be described as signaling errors in predicting value.

scholarly journals Higher-Order Conditioning and Dopamine: Charting a Path Forward

2021 ◽  
Vol 15 ◽  
Author(s):  
Benjamin M. Seitz ◽  
Aaron P. Blaisdell ◽  
Melissa J. Sharpe
Keyword(s):  
Causal Reasoning ◽  
Higher Order ◽  
Second Order ◽  
Dopamine Neurons ◽  
Sensory Preconditioning ◽  
Order Conditioning ◽  
Sports Team ◽  
Midbrain Dopamine ◽  
Complex Forms ◽  
Learning And Cognition

Higher-order conditioning involves learning causal links between multiple events, which then allows one to make novel inferences. For example, observing a correlation between two events (e.g., a neighbor wearing a particular sports jersey), later helps one make new predictions based on this knowledge (e.g., the neighbor’s wife’s favorite sports team). This type of learning is important because it allows one to benefit maximally from previous experiences and perform adaptively in complex environments where many things are ambiguous or uncertain. Two procedures in the lab are often used to probe this kind of learning, second-order conditioning (SOC) and sensory preconditioning (SPC). In second-order conditioning (SOC), we first teach subjects that there is a relationship between a stimulus and an outcome (e.g., a tone that predicts food). Then, an additional stimulus is taught to precede the predictive stimulus (e.g., a light leads to the food-predictive tone). In sensory preconditioning (SPC), this order of training is reversed. Specifically, the two neutral stimuli (i.e., light and tone) are first paired together and then the tone is paired separately with food. Interestingly, in both SPC and SOC, humans, rodents, and even insects, and other invertebrates will later predict that both the light and tone are likely to lead to food, even though they only experienced the tone directly paired with food. While these processes are procedurally similar, a wealth of research suggests they are associatively and neurobiologically distinct. However, midbrain dopamine, a neurotransmitter long thought to facilitate basic Pavlovian conditioning in a relatively simplistic manner, appears critical for both SOC and SPC. These findings suggest dopamine may contribute to learning in ways that transcend differences in associative and neurological structure. We discuss how research demonstrating that dopamine is critical to both SOC and SPC places it at the center of more complex forms of cognition (e.g., spatial navigation and causal reasoning). Further, we suggest that these more sophisticated learning procedures, coupled with recent advances in recording and manipulating dopamine neurons, represent a new path forward in understanding dopamine’s contribution to learning and cognition.

scholarly journals Phasic Activation of Ventral Tegmental, but not Substantia Nigra, Dopamine Neurons Promotes Model-Based Pavlovian Reward Learning

2017 ◽  
Author(s):  
R. Keiflin ◽  
H.J. Pribut ◽  
N.B. Shah ◽  
P.H. Janak
Keyword(s):  
Substantia Nigra ◽  
Learning Strategy ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Contrast Model ◽  
Model Based ◽  
Model Free ◽  
Predictive Learning ◽  
Neuron Activation ◽  
Ventral Tegmental

ABSTRACTDopamine (DA) neurons in the ventral tegmental area (VTA) and substantia nigra (SNc) encode reward prediction errors (RPEs) and are proposed to mediate error-driven learning. However the learning strategy engaged by DA-RPEs remains controversial. Model-free associations imbue cue/actions with pure value, independently of representations of their associated outcome. In contrast, model-based associations support detailed representation of anticipated outcomes. Here we show that although both VTA and SNc DA neuron activation reinforces instrumental responding, only VTA DA neuron activation during consumption of expected sucrose reward restores error-driven learning and promotes formation of a new cue→sucrose association. Critically, expression of VTA DA-dependent Pavlovian associations is abolished following sucrose devaluation, a signature of model-based learning. These findings reveal that activation of VTA-or SNc-DA neurons engages largely dissociable learning processes with VTA-DA neurons capable of participating in model-based predictive learning, while the role of SNc-DA neurons appears limited to reinforcement of instrumental responses.

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 What is dopamine doing in model-based reinforcement learning?

2020 ◽  
Author(s):  
Thomas Akam ◽  
Mark Walton
Keyword(s):  
Reinforcement Learning ◽  
Prediction Error ◽  
Dopamine Neurons ◽  
State Representation ◽  
Dopaminergic Activity ◽  
Model Based ◽  
Model Free ◽  
Reward Prediction ◽  
Predictive State Representation ◽  
Teaching Signal

Experiments have implicated dopamine in model-based reinforcement learning (RL). These findings are unexpected as dopamine is thought to encode a reward prediction error (RPE), which is the key teaching signal in model-free RL. Here we examine two possible accounts for dopamine’s involvement in model-based RL: the first that dopamine neurons carry a prediction error used to update a type of predictive state representation called a successor representation, the second that two well established aspects of dopaminergic activity, RPEs and surprise signals, can together explain dopamine’s involvement in model-based RL.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Model-Based and Model-Free Social Cognition

2019 ◽  
Author(s):  
Leor M Hackel ◽  
Jeffrey Jordan Berg ◽  
Björn Lindström ◽  
David Amodio
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Learning Strategies ◽  
Memory Systems ◽  
Learning Task ◽  
Financial Advisors ◽  
Model Based ◽  
Model Free ◽  
Systems Model ◽  
Task Assessment

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions—computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each independently predicted choice during the learning task and self-reported liking in a post-task assessment. Specifically, participants liked advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Moreover, participants varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition.

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