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Opposite environmental gating of the experienced utility (‘liking’) and decision utility (‘wanting’) of heroin versus cocaine in animals and humans: implications for computational neuroscience

BACKGROUND: In this paper, we reviewed translational studies concerned with environmental influences on the rewarding effects of heroin versus cocaine in rats and humans with substance use disorder. These studies show that both experienced utility (‘liking’) and decision utility (‘wanting’) of heroi...

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Detalles Bibliográficos
Autores principales: Badiani, Aldo, Caprioli, Daniele, De Pirro, Silvana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6695361/
https://www.ncbi.nlm.nih.gov/pubmed/31289884
http://dx.doi.org/10.1007/s00213-019-05318-9
Descripción
Sumario:BACKGROUND: In this paper, we reviewed translational studies concerned with environmental influences on the rewarding effects of heroin versus cocaine in rats and humans with substance use disorder. These studies show that both experienced utility (‘liking’) and decision utility (‘wanting’) of heroin and cocaine shift in opposite directions as a function of the setting in which these drugs were used. Briefly, rats and humans prefer using heroin at home but cocaine outside the home. These findings appear to challenge prevailing theories of drug reward, which focus on the notion of shared substrate of action for drug of abuse, and in particular on their shared ability to facilitate dopaminergic transmission. AIMS: Thus, in the second part of the paper, we verified whether our findings could be accounted for by available computational models of reward. To account for our findings, a model must include a component that could mediate the substance-specific influence of setting on drug reward RESULTS: It appears of the extant models that none is fully compatible with the results of our studies. CONCLUSIONS: We hope that this paper will serve as stimulus to design computational models more attuned to the complex mechanisms responsible for the rewarding effects of drugs in real-world contexts.