The debate over dopamine’s role in reward: the case for incentive salience

Berridge, Kent

doi:10.1007/s00213-006-0578-x

Cited by 2,013 publications

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“…One line of evidence against a pleasure-causing role is that mesolimbic dopamine neurons may not reliably be activated by pleasure per se but instead by predictive, motivational, or attentional properties rather than hedonic properties of reward stimuli (Carelli 2004;Cheer et al 2007;Redgrave and Gurney 2006;Salamone et al 2007;Schultz et al 1997). Another line of evidence is that, when 'liking' versus 'wanting' are teased apart by brain manipulations, specific manipulation of dopamine signaling either up or down simply fail to shift 'liking' reactions to pleasure reliably in either animals or humans (Berridge 2007;Brauer and De Wit 1997;Cannon and Palmiter 2003;Evans et al 2006;Leyton 2008;Leyton et al 2002;Leyton et al 2005;Peciña et al 2003;Robinson et al 2005;Tindell et al 2005;Volkow et al 2002;Volkow et al 2006). A third line of evidence is that dopamine systems may also be activated by aversive or frankly non-rewarding stimuli, at least tonic dopamine release pulses that last on the order of a few minutes (Ferrari et al 2003;Horvitz 2000;Salamone 1994;Scott et al 2006).…”

Section: Controversial Subcortical Pleasure Generators? Dopamine and mentioning

confidence: 99%

“…A third line of evidence is that dopamine systems may also be activated by aversive or frankly non-rewarding stimuli, at least tonic dopamine release pulses that last on the order of a few minutes (Ferrari et al 2003;Horvitz 2000;Salamone 1994;Scott et al 2006). Overall, the mesolimbic dopamine system often seems surprisingly unable to alter basic hedonic reactions to pleasure directly, in contrast to opioid and other true brain hedonic hotspots that generate 'liking' (Berridge 2007). Dopamine roles in reward learning, prediction, and motivation A popular alternative interpretation of mesolimbic dopamine's role in reward has been that it mediates learning and predictions about future reward events.…”

Section: Controversial Subcortical Pleasure Generators? Dopamine and mentioning

confidence: 99%

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Affective neuroscience of pleasure: reward in humans and animals

2008

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Introduction Pleasure and reward are generated by brain circuits that are largely shared between humans and other animals. Discussion Here, we survey some fundamental topics regarding pleasure mechanisms and explicitly compare humans and animals. Conclusion Topics surveyed include liking, wanting, and learning components of reward; brain coding versus brain causing of reward; subjective pleasure versus objective hedonic reactions; roles of orbitofrontal cortex and related cortex regions; subcortical hedonic hotspots for pleasure generation; reappraisals of dopamine and pleasure-electrode controversies; and the relation of pleasure to happiness.

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Section: Controversial Subcortical Pleasure Generators? Dopamine and mentioning

confidence: 99%

Section: Controversial Subcortical Pleasure Generators? Dopamine and mentioning

confidence: 99%

Affective neuroscience of pleasure: reward in humans and animals

2008

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“…This has consequences for learning accounts of addiction as some learning tendencies appear to confer vulnerability towards developing addiction. In this part, we first present the data on individual differences in Pavlovian responding in some detail (mainly reiterating the findings of Flagel et al 2011b), then discuss its interpretation in terms of incentive salience (Berridge and Robinson, 1998;Berridge, 2004Berridge, , 2007Saunders and Robinson, 2012), and finally put forth a hypothesis that proposes a connection between the propensity to assign incentive salience and the propensity to employ model-free learning (McClure et al, 2003a;Huys et al, 2013b;Lesaint et al, 2013;Dayan and Berridge, 2013).…”

Section: Individual Variabilitymentioning

confidence: 99%

The role of learning-related dopamine signals in addiction vulnerability

Huys

Tobler

Hasler

et al. 2014

Progress in Brain Research

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Psychostimulants such as methylphenidate (MPH) and antidepressants such as fluoxetine (FLX) are widely used in the treatment of various mental disorders or as cognitive enhancers. These medications are often combined, for example, to treat comorbid disorders. There is a considerable body of evidence from animal models indicating that individually these psychotropic medications can have detrimental effects on the brain and behavior, especially when given during sensitive periods of brain development. However, almost no studies investigate possible interactions between these drugs. This is surprising given that their combined neurochemical effects (enhanced dopamine and serotonin neurotransmission) mimic some effects of illicit drugs such as cocaine and amphetamine. Here, we summarize recent studies in juvenile rats on the molecular effects in the mid-and forebrain and associated behavioral changes, after such combination treatments. Our findings indicate that these combined MPH + FLX treatments can produce similar molecular changes as seen after cocaine exposure while inducing behavioral changes indicative of dysregulated mood and motivation, effects that often endure into adulthood. Tables: 2 References: 254 ABSTRACT Dopaminergic signals play a mathematically precise role in reward-related learning, and variations in dopaminergic signalling have been implicated in vulnerability to addiction. Here, we provide a detailed overview of the relationship between theoretical, mathematical and experimental accounts of phasic dopamine signalling, with implications for the role of learning-related dopamine signalling in addiction and related disorders. We describe the theoretical and behavioural characteristics of model-free learning based on errors in the prediction of reward, including step-by-step explanations of the underlying equations. We then use recent insights from an animal model that highlights individual variation in learning during a Pavlovian conditioning paradigm to describe overlapping aspects of incentive salience attribution and model-free learning. We argue that this provides a computationally coherent account of some features of addiction. CONTENTS

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“…In reward-based decision making in particular, two largely separate traditions have studied the reinforcement learning and the incentive theories of dopamine (Berridge, 2007). Despite solid evidence for both theories, theoretical and empirical studies tend to favor and focus on one or the other interpretation, with little attempt to unify them or to study their interaction.…”

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Opponent actor learning (OpAL): Modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive.

Collins¹,

Frank²

2014

Psychological Review

378

455

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The striatal dopaminergic system has been implicated in reinforcement learning (RL), motor performance, and incentive motivation. Various computational models have been proposed to account for each of these effects individually, but a formal analysis of their interactions is lacking. Here we present a novel algorithmic model expanding the classical actor-critic architecture to include fundamental interactive properties of neural circuit models, incorporating both incentive and learning effects into a single theoretical framework. The standard actor is replaced by a dual opponent actor system representing distinct striatal populations, which come to differentially specialize in discriminating positive and negative action values. Dopamine modulates the degree to which each actor component contributes to both learning and choice discriminations. In contrast to standard frameworks, this model simultaneously captures documented effects of dopamine on both learning and choice incentive-and their interactions-across a variety of studies, including probabilistic RL, effort-based choice, and motor skill learning.Keywords: dopamine, striatum, reinforcement learning, choice incentive, computational model Dopamine plays a crucial role in human and animal cognition, substantially influencing a diversity of processes including reinforcement learning, motivation, incentive, working memory, and effort. Dopaminergic neurons in the substantial nigra and ventral tegmental area project to a very wide set of subcortical and cortical areas, with strongest innervation in the basal ganglia (BG), specifically in the ventral and dorsal striatum. Dysregulation of dopamine is present in a wide array of mental illnesses such as Parkinson's disease, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and Tourette's syndrome and is a central pharmaceutical target used to treat symptoms across these and numerous other pathologies.Although considerable progress has been made in our understanding of its various distinct roles, there remain fundamental debates concerning its precise mechanisms and functions, especially regarding their integration and interactions. In reward-based decision making in particular, two largely separate traditions have studied the reinforcement learning and the incentive theories of dopamine (Berridge, 2007). Despite solid evidence for both theories, theoretical and empirical studies tend to favor and focus on one or the other interpretation, with little attempt to unify them or to study their interaction. Here we develop an explicit computational analysis of the dual role of striatal dopamine in modulation of incentive motivation (affecting choice), reinforcement learning, and how these processes interact. This endeavor allows us not only to account for both types of findings alone but also those that could not be explained by either theory in isolation. RL Theory of DopamineOne widely accepted theory of dopamine function relates to its role in model-free reinforcement learning (RL). Specifically, phasic f...

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The debate over dopamine’s role in reward: the case for incentive salience

Abstract: In short, dopamine's contribution appears to be chiefly to cause 'wanting' for hedonic rewards, more than 'liking' or learning for those rewards.

Cited by 2,013 publications

References 241 publications

Affective neuroscience of pleasure: reward in humans and animals

Affective neuroscience of pleasure: reward in humans and animals

The role of learning-related dopamine signals in addiction vulnerability

Opponent actor learning (OpAL): Modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive.

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