Clinical models of decision making in addiction

Abstract

As research on decision making in addiction accumulates, it is increasingly clear that decision-making processes are dysfunctional in addiction and that this dysfunction may be fundamental to the initiation and maintenance of addictive behavior. How drug-dependent individuals value and choose among drug and nondrug rewards is consistently different from non-dependent individuals. The present review focuses on the assessment of decision-making in addiction. We cover the common behavioral tasks that have shown to be fruitful in decision-making research and highlight analytical and graphical considerations, when available, to facilitate comparisons within and among studies. Delay discounting tasks, drug demand tasks, drug choice tasks, the Iowa Gambling Task, and the Balloon Analogue Risk Task are included.

Keywords: Balloon Analogue Risk Task; Decision-making; Delay discounting; Drug choice; Drug demand; Iowa Gambling Task.

Fig. 1

Panels A and B show hypothetical discounting curves for relatively high (i.e., steep) and low (i.e., shallow) discount rates. Panel A depicts discounting curves as displayed on a linearly scaled x-axis, where spacing between delay values is arithmetic. Many of the shorter delays are grouped together in an indistinguishable bunch as shown in the circled area of the x-axis. Panel B depicts discounting curves on a logarithmic x-axis, where the progression of values is exponential. Note that effective delay 50 (ED50) is more easily identifiable and differences in discount rates are manifested in a location shift (i.e., left or right) along the x-axis. Panel C depicts real data from Koffarnus and Bickel (2014) for college-aged participants completing an adjusting amount discounting task showing the range of discounting rates seen with control subjects.

Fig. 2

Typical examples of how differences in demand intensity (Q₀) and elasticity (α) are affected by the normalization procedure proposed by Hursh and Winger (1995). When Q₀ differs (panel A), it can be hard to identify curves with similar elasticities, but normalization of the same data makes this obvious visually because all data with the same elasticity fall on the same curve when normalized (panel B). When both Q₀ and α differ (panel C), distinguishing which curve is most or least elastic is also difficult, but normalization of these data produces parallel curves aligned from highest to lowest elasticity (panel D).