A computational model of craving and obsession

A. David Redish, Adam Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

If addictions and problematic behaviors arise from interactions between drugs, reward sequences, and natural learning sytems, then an explanation of clinically problematic conditions (such as the self-administration of drugs or problem gambling) requires an understanding of the neural systems that have evolved to allow an agent to make decisions. We hypothesize a unified decision-making system consisting of three components - a situation recognition system, a flexible, planning-capable system, and an inflexible, habit-like system. In this article, we present a model of the planning-capable system based on a planning process arising from experimentally observed look-ahead dynamics in the hippocampus enabling a forward search of possibilities and an evaluation process in the nucleus accumbens. Based on evidence that opioid signaling can provide hedonic evalutation of an achieved outcome, we hypothesize that similar opioid-signaling processes evaluate the value of expected outcomes. This leads to a model of craving, based on the recognition of a path to a high-value outcome, and obsession, based on a value-induced limitation of the search process. This theory can explain why opioid antagonists reduce both hedonic responses and craving.

Original languageEnglish (US)
Title of host publicationReward and Decision Making in Corticobasal Ganglia Networks
PublisherBlackwell Publishing Inc
Pages324-339
Number of pages16
ISBN (Print)1573316741, 9781573316743
DOIs
StatePublished - Jul 2007

Publication series

NameAnnals of the New York Academy of Sciences
Volume1104
ISSN (Print)0077-8923
ISSN (Electronic)1749-6632

Keywords

  • Addiction
  • Craving
  • Dopamine
  • Hippocampus
  • Nucleus accumbens
  • Obsession
  • Opiates
  • Opioid signaling

Fingerprint Dive into the research topics of 'A computational model of craving and obsession'. Together they form a unique fingerprint.

Cite this