Pavlovian to instrumental transfer: A neurobehavioural perspective

Holmes, Nathan M.; Marchand, Alain R.; Coutureau, Étienne

doi:10.1016/j.neubiorev.2010.03.007

Cited by 287 publications

(317 citation statements)

References 160 publications

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“…It describes the capacity of an appetitive pavlovian stimulus (CS+) to elicit or increase the vigor of an ongoing behavior in an instrumental conditioning procedure. Two main types of PIT have been distinguished: general and specific (Balleine and Killcross, 2006;Holmes et al, 2010). In general PIT, the CS+ enhances any appetitive instrumental response, even when the instrumental response is associated with a different outcome, whereas in specific PIT, the CS+ associated with a certain outcome only enhances an instrumental response associated with the same outcome (Fig.…”

Section: Pavlovian-to-instrumental Transfer (Pit)mentioning

confidence: 99%

“…3C). Specific PIT can be interpreted as activation of the learned behavioral sequence associated with the outcome predicted by the presented CS+ (the behavioral chaining account, Holmes et al, 2010;Van den Bos et al, 2004). General PIT can be thought of as an expression of the transfer of incentive salience and the associated energy expenditure to obtain reward, from the CS+ to the ongoing behavior (Berridge, 2007;Holmes et al, 2010), which is accompanied by a change in the motivational state (Holland and Gallagher, 2003).…”

Section: Pavlovian-to-instrumental Transfer (Pit)mentioning

confidence: 99%

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Influence of reward motivation on human declarative memory

Miendlarzewska

Bavelier

Schwartz

2016

Neuroscience & Biobehavioral Reviews

150

134

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a b s t r a c tMotivational relevance can prioritize information for memory encoding and consolidation based on reward value. In this review, we pinpoint the possible psychological and neural mechanisms by which reward promotes learning, from guiding attention to enhancing memory consolidation. We then discuss how reward value can spill-over from one conditioned stimulus to a non-conditioned stimulus. Such generalization can occur across perceptually similar items or through more complex relations, such as associative or logical inferences. Existing evidence suggests that the neurotransmitter dopamine boosts the formation of declarative memory for rewarded information and may also control the generalization of reward values. In particular, temporally-correlated activity in the hippocampus and in regions of the dopaminergic circuit may mediate value-based decisions and facilitate cross-item integration. Given the importance of generalization in learning, our review points to the need to study not only how reward affects later memory but how learned reward values may generalize to related representations and ultimately alter memory structure.

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Section: Pavlovian-to-instrumental Transfer (Pit)mentioning

confidence: 99%

Section: Pavlovian-to-instrumental Transfer (Pit)mentioning

confidence: 99%

Influence of reward motivation on human declarative memory

Miendlarzewska

Bavelier

Schwartz

2016

Neuroscience & Biobehavioral Reviews

150

134

View full text Add to dashboard Cite

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“…This is demonstrated in two types of Pavlovian-instrumental transfer (PIT), general and outcome-specific PIT. In both types of PIT, appetitive CSs enhance and aversive CSs suppress instrumental behaviours for other outcomes (Estes and Skinner, 1941;Rescorla and Solomon, 1967;Lovibond, 1983;Cardinal et al, 2002;Niv et al, 2007;Holmes et al, 2010;Talmi et al, 2008;Huys et al, 2011). In general PIT, a stimulus that has been paired in a Pavlovian manner with one type of outcome (e.g.…”

Section: Pavlovian-instrumental Transfermentioning

confidence: 99%

“…This does require representation of the actual outcome, not just the value. Thus, while general PIT requires only the information carried by V MF (s), outcome-specific PIT requires additional information and likely relies on V MB (s) from model-based processes (Corbit and Balleine, 2005;Schoenbaum et al, 2009;Holmes et al, 2010;McDannald et al, 2011;Prévost et al, 2012Prévost et al, , 2013.…”

Section: Pavlovian-instrumental Transfermentioning

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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“…As such, cues can guide goal-directed 24 behavior, but not necessarily in an adaptive way (Holmes, Marchand, & Coutureau, 2010 a cue predicting pain increased pain-related 18 fear and avoidance tendencies, whereas cues predicting reward decreased these tendencies (Claes, Vlaeyen, 19 & Crombez, 2016). In the current experiment, we therefore focus specifically on the impact of Pavlovian 20 cues on the avoidance of painful movements.…”

mentioning

confidence: 99%