Cognitive biases, including implicit memory associations are thought to play an important role in the development of addictive behaviors. The aim of the present study was to investigate implicit affective memory associations in heavy cannabis users. Implicit positive-arousal, sedation, and negative associations toward cannabis were measured with three Single Category Implicit Association Tests (SC-IAT’s) and compared between 59 heavy cannabis users and 89 controls. Moreover, we investigated the relationship between these implicit affective associations and explicit expectancies, subjective craving, cannabis use, and cannabis related problems. Results show that heavy cannabis users had stronger implicit positive-arousal associations but weaker implicit negative associations toward cannabis compared to controls. Moreover, heavy cannabis users had stronger sedation but weaker negative explicit expectancies toward cannabis compared to controls. Within heavy cannabis users, more cannabis use was associated with stronger implicit negative associations whereas more cannabis use related problems was associated with stronger explicit negative expectancies, decreasing the overall difference on negative associations between cannabis users and controls. No other associations were observed between implicit associations, explicit expectancies, measures of cannabis use, cannabis use related problems, or subjective craving. These findings indicate that, in contrast to other substances of abuse like alcohol and tobacco, the relationship between implicit associations and cannabis use appears to be weak in heavy cannabis users.
Implicit Associations and Explicit Expectancies toward Cannabis in Heavy Cannabis Users and Controls
Esther M Beraha,J. Cousijn,Elisa Hermanides,A. Goudriaan,R. Wiers
Published 2013 in Frontiers in Psychiatry
ABSTRACT
PUBLICATION RECORD
- Publication year
2013
- Venue
Frontiers in Psychiatry
- Publication date
2013-06-21
- Fields of study
Medicine, Psychology
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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