Working for beverages without being thirsty: Human Pavlovian-instrumental transfer despite outcome devaluation

Tommaso, Matteo De; Mastropasqua, Tommaso; Turatto, Massimo

doi:10.1016/j.lmot.2018.01.001

Cited by 18 publications

(21 citation statements)

References 30 publications

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“…The modulating influence of conditioned cues on instrumental behavior (i.e. increasing the vigor with which a behavior is displayed or increasing the likelihood of choosing a certain option) has been termed Pavlovian-toinstrumental transfer (PIT) (Cartoni, Balleine, & Baldassarre, 2016;De Tommaso, Mastropasqua, & Turatto, 2018;Schulreich, Gerhardt, & Heekeren, 2016;Talmi, Seymour, Dayan, & Dolan, 2008). PIT is one of the key effects deepening our understanding of cuecontrolled behaviors (Dickinson & Balleine, 1994;Dickinson Anthony & Balleine Bernard, 2002;Holmes, Marchand, & Coutureau, 2010;Niv, Daw, Joel, & Dayan, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…PIT is one of the key effects deepening our understanding of cuecontrolled behaviors (Dickinson & Balleine, 1994;Dickinson Anthony & Balleine Bernard, 2002;Holmes, Marchand, & Coutureau, 2010;Niv, Daw, Joel, & Dayan, 2007). Interestingly, PIT effects can persist even when the outcome of the instrumental behavior has been devalued (De Tommaso et al, 2018), and a stronger PIT has been associated with heightened impulsivity (Garofalo & Robbins, 2017) and with reduced model-based behavior (Sebold et al, 2016). This is why PIT has gained considerable attention in the field of addiction research.…”

Section: Introductionmentioning

confidence: 99%

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Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

Genauck

Matthis

Andrejević

et al. 2018

Preprint

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Background:Just as substance use disorders (SUD), gambling disorder (GD) is characterized by an increase in cue-dependent decision-making (Pavlovian-to-instrumental transfer, PIT). PIT, as studied in SUDs and healthy subjects, is associated with altered communication between Nucleus Accumbens (NAcc), amygdala, and orbitofrontal cortex (OFC). However, these neural differences are poorly understood. For example, it is unclear whether they are due to the physiological effects of substance abuse, or rather related to learning processes and/or other etiological factors like innate traits associated with addiction. We have thus investigated whether network activation patterns during a PIT task are also altered in GD, an addictive disorder not involving substance abuse. We have specifically studied which neural PIT patterns were best at distinguishing GD from healthy control (HC) subjects, all to improve our understanding of the neural signatures of GD and of addiction-related PIT in general. Methods:30 GD and 30 HC subjects completed an affective decision-making task in a functional magnetic resonance imaging (fMRI) scanner. Gambling associated and other emotional cues were shown in the background during the task, allowing us to record multivariate neural PIT signatures focusing on a network of NAcc, amygdala and OFC. We built and tested a classifier based on these multivariate neural PIT signatures using cross-validated elastic net regression. Results and Discussion:As expected, GD subjects showed stronger PIT than HC subjects because they showed stronger increase in gamble acceptance when gambling cues were presented in the Alexander Genauck 5 background. Classification based on neural PIT signatures yielded a significant AUC-ROC (0.70, p = 0.013). When inspecting the features of the classifier, we observed that GD showed stronger PIT-related functional connectivity between nucleus accumbens (NAcc) and amygdala elicited by gambling background cues, as well as between amygdala and orbitofrontal cortex (OFC) elicited by negative and positive cues. Conclusion:We propose that GD and HC subjects are distinguishable by PIT-related neural signatures including amygdala-NAcc-OFC functional connectivity. Our findings suggest that neural PIT alterations in addictive disorders might not depend on the physiological effect of a substance of abuse, but on related learning processes or even innate neural traits, also found in behavioral addictions.Alexander Genauck 6

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

Genauck

Matthis

Andrejević

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…These effects were independent of reinforcer presentation delay, which was interpreted as a disruption in goal-directed instrumental control by Pavlovian cues [252]. PIT effects in humans have been shown to be insensitive to outcome-devaluation [253] (although see satiety effects in PIT-related NAcc activation in animals [254]), but sensitive to extinction, although this was less effective for reducing PIT in a different context [255]. Moreover, reduced working memory capacity has been reported to attenuate outcome-specific but not general PIT [256].…”

Section: Behavioral Paradigms and Neural Circuitrymentioning

confidence: 93%

Instrumental and Pavlovian Mechanisms in Alcohol Use Disorder

et al. 2020

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Purpose of Review Current theories of alcohol use disorders (AUD) highlight the importance of Pavlovian and instrumental learning processes mainly based on preclinical animal studies. Here, we summarize available evidence for alterations of those processes in human participants with AUD with a focus on habitual versus goal-directed instrumental learning, Pavlovian conditioning, and Pavlovian-to-instrumental transfer (PIT) paradigms. Recent Findings The balance between habitual and goal-directed control in AUD participants has been studied using outcome devaluation or sequential decision-making procedures, which have found some evidence of reduced goal-directed/model-based control, but little evidence for stronger habitual responding. The employed Pavlovian learning and PIT paradigms have shown considerable differences regarding experimental procedures, e.g., alcohol-related or conventional reinforcers or stimuli. Summary While studies of basic learning processes in human participants with AUD support a role of Pavlovian and instrumental learning mechanisms in the development and maintenance of drug addiction, current studies are characterized by large variability regarding methodology, sample characteristics, and results, and translation from animal paradigms to human research remains challenging. Longitudinal approaches with reliable and ecologically valid paradigms of Pavlovian and instrumental processes, including alcohol-related cues and outcomes, are warranted and should be combined with state-of-the-art imaging techniques, computational approaches, and ecological momentary assessment methods.

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“…Estimating subject-specific parameters from behavioral choice data 13 We modeled each subject's behavioral data by submitting dichotomized choices (somewhat no, 14 no: 0; somewhat yes, yes: 1) into logistic regressions. We dichotomized choices to increase the 15 precision when estimating behavioral parameters, in line with previous studies using the mixed 16 gambles task 23,35 .…”

Section: Methodsmentioning

confidence: 99%

“…PIT has recently drawn attention in the 4 study of substance use disorders (SUDs) 12 . This is because PIT effects can persist even when 5 the outcome of the instrumental behavior has been devalued 13 , and because increased PIT has 6 been associated with a marker for impulsivity 14 and with decreased model-based behavior 15 . 7…”

Section: Introductionmentioning

confidence: 99%

Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls

Genauck

Andrejević

Brehm

et al. 2019

Preprint

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While an increased impact of cues on decision-making has been associated with substance dependence, it is yet unclear whether this is also a phenotype of non-substance related addictive disorders, such as gambling disorder. To better understand the basic mechanisms of impaired decision-making in addiction, we investigated whether cue-induced changes in decision-making could distinguish gambling disorder (GD) from healthy control (HC) subjects. We expected that cue-induced changes in gamble acceptance and specifically in loss aversion would distinguish GD from HC subjects.30 GD subjects and 30 matched HC subjects completed a mixed gambles task where gambling and other emotional cues were shown in the background. We used machine learning and classification to carve out the importance of cue-dependency of decision-making and of loss aversion for distinguishing GD from HC subjects.Cross-validated classification yielded an area under the receiver operating curve (AUC-ROC) of 68.9% (p=0.002). Applying the classifier to an independent sample yielded an AUC-ROC of 65.0% (p=0.047). As expected, the classifier used cue-induced changes in gamble acceptance to distinguish GD from HC. Especially increased gambling during the presentation of gambling cues was characteristic of GD subjects. However, unexpectedly, cue-induced changes in loss aversion were irrelevant for distinguishing GD from HC subjects. To our knowledge, this is the first study to investigate the classificatory power of addiction-relevant behavioral task parameters when distinguishing GD from HC subjects. The results indicate that cue-induced changes in decision-making are a characteristic feature of addictive disorders, independent of a substance of abuse.

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Working for beverages without being thirsty: Human Pavlovian-instrumental transfer despite outcome devaluation

Cited by 18 publications

References 30 publications

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

Instrumental and Pavlovian Mechanisms in Alcohol Use Disorder

Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls

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