Journal
JOURNAL OF MATHEMATICAL PSYCHOLOGY
Volume 54, Issue 1, Pages 28-38Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmp.2009.10.002
Keywords
Decision-making; Cannabis; Iowa Gambling Task; Cognitive modeling
Categories
Funding
- National Institute on Drug Abuse [R01 DA 014119]
- NIMH [T32 MH17146]
Ask authors/readers for more resources
Chronic cannabis users are known to be impaired on a test of decision-making, the Iowa Gambling Task (IGT). Computational models of the psychological processes underlying this impairment have the potential to provide a rich description of the psychological characteristics of poor performers within particular clinical groups. We used two computational models of IGT performance, the Expectancy Valence Learning model (EVL) and the Prospect Valence Learning model (PVL), to assess motivational, memory, and response processes in 17 chronic cannabis abusers and 15 control participants. Model comparison and simulation methods revealed that the PVL model explained the observed data better than the EVL model. Results indicated that cannabis abusers tended to be under-influenced by loss magnitude, treating each loss as a constant and minor negative outcome regardless of the size of the loss. In addition, they were more influenced by gains, and made decisions that were less consistent with their expectancies relative to non-using controls. (C) 2009 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available