Browse
Submit Data
Databases
API
Help

Dataset Information

33 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Reward prediction error does not explain movement selectivity in DMS-projecting dopamine neurons.

ABSTRACT: Although midbrain dopamine (DA) neurons have been thought to primarily encode reward prediction error (RPE), recent studies have also found movement-related DAergic signals. For example, we recently reported that DA neurons in mice projecting to dorsomedial striatum are modulated by choices contralateral to the recording side. Here, we introduce, and ultimately reject, a candidate resolution for the puzzling RPE vs movement dichotomy, by showing how seemingly movement-related activity might be explained by an action-specific RPE. By considering both choice and RPE on a trial-by-trial basis, we find that DA signals are modulated by contralateral choice in a manner that is distinct from RPE, implying that choice encoding is better explained by movement direction. This fundamental separation between RPE and movement encoding may help shed light on the diversity of functions and dysfunctions of the DA system.

SUBMITTER: Lee RS

PROVIDER: S-EPMC6464606 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Reward prediction error does not explain movement selectivity in DMS-projecting dopamine neurons.

Lee Rachel S RS Mattar Marcelo G MG Parker Nathan F NF Witten Ilana B IB Daw Nathaniel D ND

eLife 20190404

Although midbrain dopamine (DA) neurons have been thought to primarily encode reward prediction error (RPE), recent studies have also found movement-related DAergic signals. For example, we recently reported that DA neurons in mice projecting to dorsomedial striatum are modulated by choices contralateral to the recording side. Here, we introduce, and ultimately reject, a candidate resolution for the puzzling RPE vs movement dichotomy, by showing how seemingly movement-related activity might be e ...[more]

PMID: 30946008

Similar Datasets

Dopamine neurons share common response function for reward prediction error.

Project description:Dopamine neurons are thought to signal reward prediction error, or the difference between actual and predicted reward. How dopamine neurons jointly encode this information, however, remains unclear. One possibility is that different neurons specialize in different aspects of prediction error; another is that each neuron calculates prediction error in the same way. We recorded from optogenetically identified dopamine neurons in the lateral ventral tegmental area (VTA) while mice performed classical conditioning tasks. Our tasks allowed us to determine the full prediction error functions of dopamine neurons and compare them to each other. We found marked homogeneity among individual dopamine neurons: their responses to both unexpected and expected rewards followed the same function, just scaled up or down. As a result, we were able to describe both individual and population responses using just two parameters. Such uniformity ensures robust information coding, allowing each dopamine neuron to contribute fully to the prediction error signal.

| S-EPMC4767554 | biostudies-literature

Dopamine reward prediction error responses reflect marginal utility.

Project description:BackgroundOptimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity.ResultsIn two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles.ConclusionsThese data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility).

| S-EPMC4228052 | biostudies-literature

The cost of obtaining rewards enhances the reward prediction error signal of midbrain dopamine neurons.

Project description:Midbrain dopamine neurons are known to encode reward prediction errors (RPE) used to update value predictions. Here, we examine whether RPE signals coded by midbrain dopamine neurons are modulated by the cost paid to obtain rewards, by recording from dopamine neurons in awake behaving monkeys during performance of an effortful saccade task. Dopamine neuron responses to cues predicting reward and to the delivery of rewards were increased after the performance of a costly action compared to a less costly action, suggesting that RPEs are enhanced following the performance of a costly action. At the behavioral level, stimulus-reward associations are learned faster after performing a costly action compared to a less costly action. Thus, information about action cost is processed in the dopamine reward system in a manner that amplifies the following dopamine RPE signal, which in turn promotes more rapid learning under situations of high cost.

| S-EPMC6695452 | biostudies-literature

Dopamine reward prediction-error signalling: a two-component response.

Project description:Environmental stimuli and objects, including rewards, are often processed sequentially in the brain. Recent work suggests that the phasic dopamine reward prediction-error response follows a similar sequential pattern. An initial brief, unselective and highly sensitive increase in activity unspecifically detects a wide range of environmental stimuli, then quickly evolves into the main response component, which reflects subjective reward value and utility. This temporal evolution allows the dopamine reward prediction-error signal to optimally combine speed and accuracy.

| S-EPMC5549862 | biostudies-literature

Glutamate inputs send prediction error of reward but not negative value of aversive stimuli to dopamine neurons.

Project description:Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs) but the mechanisms underlying RPE computation, particularly contributions of different neurotransmitters, remain poorly understood. Here we used a genetically-encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons. We found that glutamate inputs exhibit virtually all of the characteristics of RPE, rather than conveying a specific component of RPE computation such as reward or expectation. Notably, while glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli toward more positive responses, while excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.

| S-EPMC10659341 | biostudies-literature

Dopamine prediction error responses integrate subjective value from different reward dimensions.

Project description:Prediction error signals enable us to learn through experience. These experiences include economic choices between different rewards that vary along multiple dimensions. Therefore, an ideal way to reinforce economic choice is to encode a prediction error that reflects the subjective value integrated across these reward dimensions. Previous studies demonstrated that dopamine prediction error responses reflect the value of singular reward attributes that include magnitude, probability, and delay. Obviously, preferences between rewards that vary along one dimension are completely determined by the manipulated variable. However, it is unknown whether dopamine prediction error responses reflect the subjective value integrated from different reward dimensions. Here, we measured the preferences between rewards that varied along multiple dimensions, and as such could not be ranked according to objective metrics. Monkeys chose between rewards that differed in amount, risk, and type. Because their choices were complete and transitive, the monkeys chose "as if" they integrated different rewards and attributes into a common scale of value. The prediction error responses of single dopamine neurons reflected the integrated subjective value inferred from the choices, rather than the singular reward attributes. Specifically, amount, risk, and reward type modulated dopamine responses exactly to the extent that they influenced economic choices, even when rewards were vastly different, such as liquid and food. This prediction error response could provide a direct updating signal for economic values.

| S-EPMC3926061 | biostudies-literature

Striatal Dopamine and Reward Prediction Error Signaling in Unmedicated Schizophrenia Patients.

Project description:Increased striatal dopamine synthesis capacity has consistently been reported in patients with schizophrenia. However, the mechanism translating this into behavior and symptoms remains unclear. It has been proposed that heightened striatal dopamine may blunt dopaminergic reward prediction error signaling during reinforcement learning. In this study, we investigated striatal dopamine synthesis capacity, reward prediction errors, and their association in unmedicated schizophrenia patients (n = 19) and healthy controls (n = 23). They took part in FDOPA-PET and underwent functional magnetic resonance imaging (fMRI) scanning, where they performed a reversal-learning paradigm. The groups were compared regarding dopamine synthesis capacity (Kicer), fMRI neural prediction error signals, and the correlation of both. Patients did not differ from controls with respect to striatal Kicer. Taking into account, comorbid alcohol abuse revealed that patients without such abuse showed elevated Kicer in the associative striatum, while those with abuse did not differ from controls. Comparing all patients to controls, patients performed worse during reversal learning and displayed reduced prediction error signaling in the ventral striatum. In controls, Kicer in the limbic striatum correlated with higher reward prediction error signaling, while there was no significant association in patients. Kicer in the associative striatum correlated with higher positive symptoms and blunted reward prediction error signaling was associated with negative symptoms. Our results suggest a dissociation between striatal subregions and symptom domains, with elevated dopamine synthesis capacity in the associative striatum contributing to positive symptoms while blunted prediction error signaling in the ventral striatum related to negative symptoms.

| S-EPMC7751190 | biostudies-literature

Dopamine reward prediction error signal codes the temporal evaluation of a perceptual decision report.

Project description:Learning to associate unambiguous sensory cues with rewarded choices is known to be mediated by dopamine (DA) neurons. However, little is known about how these neurons behave when choices rely on uncertain reward-predicting stimuli. To study this issue we reanalyzed DA recordings from monkeys engaged in the detection of weak tactile stimuli delivered at random times and formulated a reinforcement learning model based on belief states. Specifically, we investigated how the firing activity of DA neurons should behave if they were coding the error in the prediction of the total future reward when animals made decisions relying on uncertain sensory and temporal information. Our results show that the same signal that codes for reward prediction errors also codes the animal's certainty about the presence of the stimulus and the temporal expectation of sensory cues.

| S-EPMC5715768 | biostudies-literature

Brief optogenetic inhibition of dopamine neurons mimics endogenous negative reward prediction errors.

Project description:Correlative studies have strongly linked phasic changes in dopamine activity with reward prediction error signaling. But causal evidence that these brief changes in firing actually serve as error signals to drive associative learning is more tenuous. Although there is direct evidence that brief increases can substitute for positive prediction errors, there is no comparable evidence that similarly brief pauses can substitute for negative prediction errors. In the absence of such evidence, the effect of increases in firing could reflect novelty or salience, variables also correlated with dopamine activity. Here we provide evidence in support of the proposed linkage, showing in a modified Pavlovian over-expectation task that brief pauses in the firing of dopamine neurons in rat ventral tegmental area at the time of reward are sufficient to mimic the effects of endogenous negative prediction errors. These results support the proposal that brief changes in the firing of dopamine neurons serve as full-fledged bidirectional prediction error signals.

| S-EPMC4696902 | biostudies-literature

Neural Circuitry of Reward Prediction Error.

Project description:Dopamine neurons facilitate learning by calculating reward prediction error, or the difference between expected and actual reward. Despite two decades of research, it remains unclear how dopamine neurons make this calculation. Here we review studies that tackle this problem from a diverse set of approaches, from anatomy to electrophysiology to computational modeling and behavior. Several patterns emerge from this synthesis: that dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from upstream regions; that they combine multiple separate and redundant inputs, which are themselves interconnected in a dense recurrent network; and that despite the complexity of inputs, the output from dopamine neurons is remarkably homogeneous and robust. The more we study this simple arithmetic computation, the knottier it appears to be, suggesting a daunting (but stimulating) path ahead for neuroscience more generally.

| S-EPMC6721851 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data