Using Computational Models to Help Explain Decision Making Processes of Substance Abusers

The purpose of this chapter is to formulate a computational model that synthesizes two separate explanations for decision making deficits in substance abusers. One is based on poor planning caused by discounting of future consequences, and the other is based on poor learning caused by insensitivity to punishments or hypersensitivity to rewards. First we review empirical research on differences between substance abusers and non abusers with respect to discounting of future consequences. Second, we review empirical research that has revealed hypersensitivity to rewards or insensitivity to punishments by drug abusers as compared to non abusers on the Bechara-Damasio simulated gambling task. Third we present a computational model of multistage decision making that includes planning for future consequences, and we apply this model to both the delay discounting paradigm and the simulated gambling paradigm. Finally, we make linkages between this computational model and the neurophysiological underpinnings of the model, and we discuss the implications of these linkages for understanding the development of substance abuse habits. Models of Substance Abuse 3 Substance abuse is a complex problem that needs to be approached from many different scientific directions, with each providing a necessarily limited perspective and contribution. A reasonably complete understanding of the issues requires an interdisciplinary integration of the sociological, psychological, neurological, and genetic causes underlying this personal and social dilemma (West, 2001, provides a broad overview of theories). Even within a single disciplinary point of view, such as psychology, an overwhelming number of factors must be considered including psychopathology, personality, cognitive, and motivational processes (see Finn, 2002; Jentsch & Taylor, 1999, Tiffany, 1990, for alternative views). This chapter focuses on a specific, yet vital, topic concerning substance abuse – the decision making process (cf. Skog, 2000). The purpose of this chapter is to formulate a computational model that synthesizes a number of separate lines of research directed at understanding decision making processes of substance abusers. Examining the issue from this restricted point of view allows us to delve more deeply into a few of the basic psychological mechanisms underlying substance abuse. Furthermore, we link these decision making processes to their neuro-physiological underpinnings. 1. A Prototypic Drug Abuse Decision To begin our formal analysis, we ask, what are the basic theoretical ingredients that go into making decisions in a substance abuse setting? Figure 1 is a “decision tree” 1 Marr (1982) proposed three levels of theorizing. At the top, computational level, theories are designed to explain the abstract goals that a cognitive system is trying to achieve (e.g., maximize expected utility). At the next, algorithmic level, theories are designed to explain the dynamics used to achieve the top level goals (e.g., learning processes). The final, implementation level, describes the neuro-physiological basis of the second level (e.g.,nerual synaptic modifications). Here we discuss all three levels. Models of Substance Abuse 4 diagram of a typical ‘self-control’ decision problem (based on Kanfer & Karoly, 1972), and one that is frequently faced by drug abusers. This is an oversimplification of the problem, but it is sufficient to introduce the basic theoretical concepts. Imagine an undergraduate student who is currently at home on Sunday morning, and he is invited to go out that Sunday night with some wild and rowdy friends, but he also has to study for a calculus test that is scheduled for early Monday morning. At the current decision (the square node labeled D1), he must decide whether to stay alone at home and study, or go out with his friends. If he decides to stay at home and study (take the downward branch), then he will miss all the immediate fun, but he is likely to do pass the exam the next day (the two branches of the circular chance node E1 represent the different possible outcomes of the exam after this choice). If he decides to go out with his friends, then he will face a second decision – that is, he may return soon after dinner and go home and study, or he may stay out too late, abusing alcohol and possibly other drugs. In the former case, he gets to enjoy being with his friends for a short while, and he still has a very good chance of passing the exam. In the latter case, he gets to have fun all night, but he will suffer the next day -feeling ill and tired and likely to fail the exam. At the very end of each path of the tree are the terminal nodes, or final consequences, that follow each particular sequence of actions and events. 2 More abstractly, the first stage can be viewed as a choice between experimenting with drugs or not. If the decision in the first stage is to experiment, then the second stage is a choice between stopping after a short time, or continuing and overindulging in drug use. Models of Substance Abuse 5 Figure 1: Self – Control Decision Problem This simple decision tree entails most of the basic ingredients discussed by decision theories (see, e.g., Clemen, 1996). First of all, the decision involves planning, because the student needs to consider the future decision at node D2 before choosing at the current decision node D1. Second, the decision involves risk or uncertainty about the chance events (E1, E2, or E3) that follow each action. Third, the decision involves the evaluation of the rewards and punishments produced by the consequences (C1 – C6) of each decision. Decision theorists use backward induction to determine the optimal strategy for decision trees such as the one shown in Figure 1 (see Clemen, 1996). Working backwards from decision node D2, the optimal choice is to return home early, because this maximizes the chances of passing the important calculus exam. Given that the student plans to return home early, the optimal choice at node D1 is to go out with his friends, because this maximizes the enjoyment with friends while at the same time produces the Go out with friends Stay home and study Stay out Late Return Early D1 D2 C1