The DOI matches its Crossref record; 284 of the 284 references it cites were found in a registry or an index.
Predictive Reward Signal of Dopamine Neurons
Abstract
The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in neurons in striatum, frontal cortex, and amygdala, which process specific reward information but do not emit a global reward prediction error signal. A cooperation between the different reward signals may assure the use of specific rewards for selectively reinforcing behaviors. Among the other projection systems, noradrenaline neurons predominantly serve attentional mechanisms and nucleus basalis neurons code rewards heterogeneously. Cerebellar climbing fibers signal errors in motor performance or errors in the prediction of aversive events to cerebellar Purkinje cells. Most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal but may reflect the absence of a general enabling function of tonic levels of extracellular dopamine. Thus dopamine systems may have two functions, the phasic transmission of reward information and the tonic enabling of postsynaptic neurons.
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In the citing paper
“Moreover, AL is, according to all definitions, “predictive.” Because the CS (e.g., the sound of a buzzer) repeatedly precedes the US (e.g., the smell of food), it comes to “predict” the response (salivation). The extent to which the organism anticipates the reinforcement when the CS occurs influences the strength of learning. Only unanticipated events lead to learning. The greater the difference”
From The Transition to Minimal Consciousness through the Evolution of Associative Learning (2016) · cites this paper as Schultz, 1998
In the citing paper
“Previous literature suggests that dopaminergic neuron firing (Schultz, 1998) and dopamine release in the medial NAc core (Kutlu et al., 2022) in response to novel sensory events habituates as the stimulus becomes familiar.”
From Ventral striatum dopamine release encodes unique properties of visual stimuli in mice (2023) · cites this paper as Schultz, 1998
In the citing paper
“Although it was originally postulated that the DA released from the VTA only mediates the hedonic or pleasurable effects of natural reinforcers, it is now clear that changes in DAergic activity can also signal punishment, aversion, or lack of expected reward [53–58].”
From Mechanistic insights into nicotine withdrawal (2011) · cites this paper as [53]
In the citing paper
“With repeated exposure, the DA neurons stop firing when receiving the food and fire instead when exposed to the stimulus that predicts food delivery (33).”
From The Addictive Dimensionality of Obesity (2013) · cites this paper as [33]
In the citing paper
“These responses constitute biological implementations of the crucial error term for reinforcement learning according to the Rescorla–Wagner model and temporal difference reinforcement models73; such a signal is appropriate for mediating learning and updating of reward predictions for approach behaviour and economic decisions4.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [4]
In the citing paper
“Experiments with monkeys and humans have shown that these neurons encode a prediction error, the difference between the reinforcer expected and the reinforcer obtained within a trial (O'Doherty et al., 2004; Schultz, 1998).”
From DYNAMIC RESPONSE-BY-RESPONSE MODELS OF MATCHING BEHAVIOR IN RHESUS MONKEYS (2005) · cites this paper as Schultz, 1998
In the citing paper
“A seminal finding was that phasic activity of dopamine neurons in the ventral tegmental area (VTA) encodes a reward prediction error (the difference between the expected and actual reward) (Schultz et al., 1997; Schultz, 1998; Roesch et al., 2007).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Schultz, 1998
In the citing paper
“Fast phasic events occur in response to reward-related events, which may serve as important teaching signals for error detection and modulate behavioral changes in response to the environment (Schultz 1998).”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Schultz 1998
In the citing paper
“Slow changes in tonic levels of DA may be a preparatory mechanism for an organism to respond to environmental cues associated with reward (Schultz 1998).”
From Developmental imaging genetics: Linking dopamine function to adolescent behavior (2014) · cites this paper as Schultz 1998
In the citing paper
“Most dopaminergic neurons within the core reward circuitry, particularly in the VS, show short bursts of phasic activation in response to reward and, after learning, in response to conditioned cues that signal a potential reward [13].”
From Social “wanting” dysfunction in autism: neurobiological underpinnings and treatment implications (2012) · cites this paper as [13]
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Metadata
- Authors
- Schultz, W.
- Affiliations
- University of Fribourg (CH)
- Corresponding author
- Schultz, Wolfram
- ORCID
- Schultz, Wolfram
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 1998-07-01
- Volume
- 80
- Issue
- 1
- Pages
- 1-27
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2109059823
- Cited by
- 4,564
- References
- 284
- Citations by year
- 2026: 115 · 2025: 132 · 2024: 156 · 2023: 136 · 2022: 164 · 2021: 165 · 2020: 231 · 2019: 167 · 2018: 168 · 2017: 162 · 2016: 223 · 2015: 208 · 2014: 228 · 2013: 205 · 2012: 223
- FWCI
- 20.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- dopaminergic neurons, reward prediction error, midbrain dopamine system, reinforcement learning, approach behavior, tonic dopamine, norepinephrine, nucleus basalis of Meynert, climbing fibers, aversive stimuli
- MeSH terms
- Animals, Brain, Dopamine, Humans, Learning, Models, Neurological, Neurons, Reward, Signal Transduction
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