Cognitive mechanisms underlying risky decision-making in chronic cannabis users☆

https://doi.org/10.1016/j.jmp.2009.10.002Get rights and content

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 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.

Section snippets

Cognitive mechanisms underlying risky decision-making in chronic cannabis users

Substance abusers often are impaired on laboratory measures of decision-making (Bechara et al., 2001, Petry, 2003, Petry et al., 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 (Grant, Contoreggi, & London, 2000). Cannabis (marijuana) users, like other substance-using

Participants

Participants consisted of 17 chronic cannabis users and 15 control subjects (see Table 3). Inclusion in the chronic cannabis group required reported cannabis usage for at least 25 out of every 30 days for at least 5 years. This group reported an average of 13.2±9.0 (M±SD) years of cannabis abuse. The control group included individuals who reported a maximum of 100 lifetime uses of cannabis, with no use in the past year. On average, they reported 19.7±29.4 lifetime uses of cannabis. Thus, the

Analysis of IGT performance

To analyze IGT performance, the 100 card selections were divided into a series of five blocks. Blocks 1 through 4 each consisted of twenty card selections (trials 1–20, 21–40, 41–60, and 61–80, respectively) whereas Block 5 consisted of fifteen card selections (trials 81 through 95). Performance for trials 96 through 100 was not analyzed because many subjects depleted at least one of the 4 decks between the 96th and 100th trials, changing the structure of the task at that point from a choice

Summary of basic findings

The results of the present study suggest that the PVL model provides a more accurate account of decision-making on the IGT than the EVL model, and demonstrate the usefulness of the PVL model in uncovering the cognitive processes that contribute to performance on that task.

Furthermore, the results show that the PVL model may be used to identify specific impairments in those processes among members of a clinical sample (chronic cannabis users). The between-groups comparison of the PVL model

Acknowledgments

The authors acknowledge Michael Wesley and Christopher Whitlow for their assistance with collecting the behavioral data presented in this report.

References (50)

  • L. Nestor et al.

    Increased ventral striatal BOLD activity during non-drug reward anticipation in cannabis users

    Neuroimage

    (2010)
  • N.M. Petry

    Discounting of money, health, and freedom in substance abusers and controls

    Drug and Alcohol Dependence

    (2003)
  • H.G. Pope et al.

    The residual neuropsychological effects of cannabis: The current status of research

    Drug and Alcohol Dependence

    (1995)
  • R.D. Rogers et al.

    Dissociable deficits in the decision-making cognition of chronic amphetamine abusers, opiate abusers, patients with focal damage to prefrontal cortex, and tryptophan-depleted normal volunteers: Evidence for monoaminergic mechanisms

    Neuropsychopharmacology

    (1999)
  • E.-J. Wagenmakers et al.

    Assessing model mimicry using the parametric bootstrap

    Journal of Mathematical Psychology

    (2004)
  • C.T. Whitlow et al.

    Long-term heavy marijuana users make costly decisions on a gambling task

    Drug and Alcohol Dependence

    (2004)
  • W.-Y. Ahn et al.

    Comparison of decision learning models using the generalization criterion method

    Cognitive Science

    (2008)
  • A.T. Beck et al.

    Beck depression inventory

    (1996)
  • A.J. Bishara et al.

    Similar processes despite divergent behavior in two commonly used measures of risky decision-making

    Journal of Behavioral Decision Making

    (2009)
  • M.J. Bohn et al.

    The Alcohol Use Disorders Identification Test (AUDIT): Validation of a screening instrument for use in medical settings

    Journal of Studies on Alcohol

    (1995)
  • A.J. Budney et al.

    The time course and significance of cannabis withdrawal

    Journal of Abnormal Psychology

    (2003)
  • J.R. Busemeyer et al.

    A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task

    Psychological Assessment

    (2002)
  • G.M. Ellis et al.

    Excretion patterns of cannabinoid metabolites after last use in a group of chronic users

    Clinical Pharmacology and Therapeutics

    (1985)
  • W.K. Estes et al.

    Risks of drawing inferences about cognitive processes from model fits to individual versus average performance

    Psychonomic Bulletin and Review

    (2005)
  • I. Erev et al.

    On adaptation, maximization, and reinforcement learning among cognitive strategies

    Psychological Review

    (2005)

    • Cited by (0)

    ☆

    This research was supported in part by National Institute on Drug Abuse grant R01 DA 014119 and NIMH Research Training Grant in Clinical Science T32 MH17146.

    View full text
    Copyright © 2009 Elsevier Inc. All rights reserved.