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Substance Use Disorders Affecting Sleep

Substance Use Disorders Affecting Sleep

Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...

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Sequential Learning Models for the Wisconsin Card Sort Task: Assessing Processes in Substance Dependent Individuals.

Anthony J Bishara¹, John K Kruschke, Julie C Stout

¹Department of Psychology, College of Charleston.

Journal of Mathematical Psychology

|May 25, 2010

Summary

Sequential learning models applied to the Wisconsin Card Sort Task (WCST) reveal distinct executive function differences in substance dependence. These models offer improved interpretation of cognitive processes beyond traditional measures.

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Area of Science:

Neuropsychology
Cognitive Psychology
Computational Psychiatry

Background:

The Wisconsin Card Sort Task (WCST) is a standard neuropsychological tool for assessing executive functions.
Traditional WCST measures like perseverative errors can be challenging to interpret for clinical populations.
Individual-level process estimation is needed to supplement existing WCST behavioral metrics.

Purpose of the Study:

To develop and validate sequential learning models for the WCST.
To assess the utility of these models in differentiating substance-dependent individuals from healthy controls.
To determine if model parameters provide incremental validity over traditional WCST measures.

Main Methods:

Development of a family of sequential learning models for the WCST.
Application of models to data from substance-dependent individuals and healthy controls.
Statistical analysis to compare model parameter predictive power against traditional WCST measures.

Main Results:

Model parameters significantly predicted group membership (substance dependence vs. controls).
Substance dependence was characterized by slower attention shifting after negative feedback and reduced decision consistency.
Model parameters demonstrated incremental content and predictive validity.

Conclusions:

Sequential learning models offer a more nuanced understanding of executive functions in the WCST.
These models can enhance the diagnostic and prognostic capabilities of the WCST for substance use disorders.
Individualized cognitive process estimation via modeling is a valuable supplement to traditional neuropsychological testing.