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Component processes of decision making in persons with substance use disorders

Resource type
Thesis type
(Dissertation) Ph.D.
Date created
The Iowa Gambling Task (IGT) is a widely used measure of decision making ability, but its ecological utility in signifying behaviours associated with adverse, “real world” consequences has not been reliably demonstrated in persons with substance use disorders. Past studies evaluating the ecological validity of the IGT have primarily relied on traditional IGT scores; however, the underlying component processes of decision making derived from computational modeling might be more closely related to engagement in behaviours associated with adverse consequences, especially in more vulnerable populations. This study employed the Prospect Valence Learning (PVL) model to decompose IGT performance into component processes in 294 marginally housed persons with substance use disorders (MHP-SUD). Additionally, we modeled performance of 136 healthy participants to ensure parameter robustness. Application of the PVL model revealed an exclusive focus on gains and a universal lack of sensitivity to losses among MHP-SUD. Further, select associations were detected between component processes and self-reported behaviours that have a high likelihood for adverse outcomes in the MHP-SUD. Specifically, lower attention to losses was modestly associated with more behaviours that are apt to adversely impact health, and lower attention to the magnitude of outcomes was modestly associated with more behaviours that are apt to adversely impact others. Delineation of specific processes that underlie decision making and their ecological associations contributes to a deeper and more nuanced understanding of some of the neurocognitive contributors to decision making in a vulnerable population that faces many personal, social, and economic challenges.
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Copyright is held by the author.
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Thornton, Allen
Member of collection
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etd9771_HBaitz.pdf 1.12 MB

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