Cognitive mechanisms underlying risky decision-making in chronic cannabis users

Abstract

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 underinfluenced 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.Keywords decision-making; cannabis; Iowa Gambling Task; cognitive modeling Substance abusers often are impaired on laboratory measures of decision-making (Bechara et al., 2001;Petry, 2003;Petry, Bickel, & Arnett, 1998;Rogers et al., 1999). For example, in a laboratory decision-making task known as the Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994), substance abusers often make choices that lead to small, immediate gains at the cost of larger losses over time (S. Grant, Contoreggi, & London, 2000). Cannabis (marijuana) users, like other substance-using populations, perform more poorly than non-using controls on the IGT (Lamers, Bechara, Rizzo, & Ramaekers, 2006;Whitlow et al., 2004), even after prolonged abstinence from the drug (Bolla, Eldreth, Matochik, & Cadet, 2005). This impairment may be due to underlying deficits or differences in psychological processes (e.g., memory impairments, loss insensitivity, etc.), but pinpointing such processes can be difficult with traditional behavioral measures from the IGT. Recent work Address correspondence to: Julie C. Stout, School of Psychology, Psychiatry, & Psychological Medicine, Room 534, Building 17 Clayton Campus, Monash University, Victoria 3800 AUSTRALIA, Tel: +61 3 99053987, Fax: +61 3 99053948, julie.stout@med.monash.edu.au. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript has attempted to disentangle component processes of the IGT by means of computational cognitive models (Buse...

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