Human Substantia Nigra Neurons Encode Unexpected Financial Rewards

Zaghloul, Kareem A.; Blanco, Justin A.; Weidemann, Christoph T.; McGill, Kathryn R.; Jaggi, Jurg L.; Baltuch, Gordon H.; Kahana, Michael J.

doi:10.1126/science.1167342

Cited by 214 publications

(196 citation statements)

References 26 publications

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“…Many studies have confirmed, quantified, and extended reward prediction error coding by dopamine neurons, even in humans (Zaghloul et al, 2009). The dopamine neurons of monkeys that have not learned to predict reward show continued positive and negative prediction errors at the time of reward or reward omission, respectively.…”

Section: Phasic Dopamine Signals Represent Model-free Prediction Errorsmentioning

confidence: 96%

“…In humans, functional MRI (fMRI) studies and even in vivo surgical recordings (Zaghloul et al, 2009) have reported correlates of prediction-error signals in the striatum that resemble those of dopamine neurons recorded in animals, including phasic (event-related) positive and negative prediction error responses (McClure et al, 2003a;O'Doherty et al, 2003;D'Ardenne et al, 2008) that reflect probability (e.g. Abler et al 2006;Burke et al 2010;Spicer et al 2007;Tobler et al 2007) and more specific predictions of formal learning theories Kahnt et al, 2012;Rutledge et al, 2010;Tobler et al, 2007).…”

Section: Phasic Dopamine Signals Represent Model-free Prediction Errorsmentioning

confidence: 99%

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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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Section: Phasic Dopamine Signals Represent Model-free Prediction Errorsmentioning

confidence: 96%

Section: Phasic Dopamine Signals Represent Model-free Prediction Errorsmentioning

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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“…52 Recent experiments have provided fascinating evidence of the link between utility and chemical processes in the brain. See, for example, Zaghloul, Blanco, Weidemann, McGill, Jaggi, Baltuch and Kahana [168]. 53 Attention was drawn to this phenomenon by Brickman, Coates and Janoff-Bulman's [18] study of lottery winners and paraplegics, and has become the subject of a large literature.…”

Section: Adaptive Utility and Relative Consumptionmentioning

confidence: 99%

The Evolutionary Foundations of Preferences

Robson

Samuelson

2011

Handbook of Social Economics

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Abstract. This paper, prepared for the forthcoming The Social Economics Handbook (Jess Benhabib, Alberto Bisin and Matthew Jackson, editors, Elsevier Press), surveys recent work on the evolutionary origins of preferences. We are especially interested in the circumstances under which evolution would push preferences away from the self-interested perfectly-rational expected utility maximization of classical economic theory in order to incorporate environmental or social considerations. * We thank Andrew Postlewaite for helpful discussions and comments.

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“…Across a range of species from rats to humans, dopamine cells burst fire in response to positive 'prediction errors' (events that are better than expected), whereas these same cells pause in response to negative prediction errors (events that are worse than expected) (Schultz et al, 1997;Bayer and Glimcher, 2005;Roesch et al, 2007;Joshua et al, 2006;Pan et al, 2008;Zaghloul et al, 2009). A key assumption of reinforcement learning models is that these bursts and dips act as a 'teaching signal' by modifying synaptic plasticity in target structures Wickens et al, 2003;Frank, 2005).…”

Section: Dissociating Corticostriatal Genetic Components To Learningmentioning

confidence: 99%

Neurogenetics and Pharmacology of Learning, Motivation, and Cognition

Frank

Fossella

2010

Neuropsychopharmacol

171

167

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Many of the individual differences in cognition, motivation, and learningFand the disruption of these processes in neurological conditionsFare influenced by genetic factors. We provide an integrative synthesis across human and animal studies, focusing on a recent spate of evidence implicating a role for genes controlling dopaminergic function in frontostriatal circuitry, including COMT, DARPP-32, DAT1, DRD2, and DRD4. These genetic effects are interpreted within theoretical frameworks developed in the context of the broader cognitive and computational neuroscience literature, constrained by data from pharmacological, neuroimaging, electrophysiological, and patient studies. In this framework, genes modulate the efficacy of particular neural computations, and effects of genetic variation are revealed by assays designed to be maximally sensitive to these computations. We discuss the merits and caveats of this approach and outline a number of novel candidate genes of interest for future study.

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Human Substantia Nigra Neurons Encode Unexpected Financial Rewards

Cited by 214 publications

References 26 publications

The role of learning-related dopamine signals in addiction vulnerability

The role of learning-related dopamine signals in addiction vulnerability

The Evolutionary Foundations of Preferences

Neurogenetics and Pharmacology of Learning, Motivation, and Cognition

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