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Elucidating the Neural Circuitry Underlying Individual Differences in Response to Reward-associated Cues

Published online by Cambridge University Press:  23 March 2020

Abstract

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Stimuli in the environment that have been associated with reward can gain control over behavior and, in some cases, lead to maladaptive behavior. Reward cues acquire inordinate control when they are attributed with incentive salience or transformed into “motivational magnets” (i.e. incentive stimuli). Individuals vary considerably in the extent to which they attribute incentive motivational value to reward cues, and we can capture this individual variation using an animal model. When rats are exposed to a Pavlovian conditioning paradigm, in which the presentation of a lever-cue is immediately followed by the delivery of a food reward, some rats preferentially approach the lever (sign-trackers, STs) while others approach the food cup (goal-trackers, GTs). Importantly, while the lever is a predictor for both STs and GTs, only for STs does it become an incentive stimulus. Thus, this model allows us to parse the neurobiological mechanisms underlying predictive vs. incentive learning processes. Using this model, we have demonstrated that dopamine is critical for incentive, but not predictive, learning and that the cortico-thalamic-striatal “motive circuit” is engaged only by incentive stimuli. In addition, we have identified the paraventricular nucleus of the thalamus (PVT) as a central node that differentially regulates sign- and goal-tracking behaviors. We have begun to utilize a chemogenetic approach (i.e. DREADDs) in combination with in vivo microdialysis to further elucidate the neural circuitry underlying individual variation in cue-motivated behaviors. Findings suggesting that STs rely primarily on subcortical mechanisms, whereas GTs utilize more “top-down” cortical processes will be presented and discussed.

Disclosure of interest

The author has not supplied his declaration of competing interest.

Type
Symposium: From prediction errors to disorders of compulsivity: A computational framework
Copyright
Copyright © European Psychiatric Association 2017
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