The problems with delay discounting: a critical review of current practices and clinical applications.

Delay discounting paradigms have gained widespread popularity across clinical research. Given the prevalence in the field, researchers have set lofty expectations for the importance of delay discounting as a key transdiagnostic process and a 'core' process underlying specific domains of dysfunction (e.g. addiction). We believe delay discounting has been prematurely reified as, in and of itself, a core process underlying psychological dysfunction, despite significant concerns with the construct validity of discounting rates. Specifically, high delay discounting rates are only modestly related to measures of psychological dysfunction and therefore are not 'core' to these more complex behavioral problems. Furthermore, discounting rates do not appear to be specifically related to any disorder(s) or dimension(s) of psychopathology. This raises fundamental concerns about the utility of discounting, if the measure is only loosely associated with most forms of psychopathology. This stands in striking contrast to claims that discounting can serve as a 'marker' for specific disorders, despite never demonstrating adequate sensitivity or specificity for any disorder that we are aware of. Finally, empirical evidence does not support the generalizability of discounting rates to other decisions made either in the lab or in the real-world, and therefore discounting rates cannot and should not serve as a summary measure of an individual's decision-making patterns. We provide recommendations for improving future delay discounting research, but also strongly encourage researchers to consider whether the empirical evidence supports the field's hyper-focus on discounting.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Hierarchies improve individual assessment of temporal discounting behavior.

Delay discounting behavior has proven useful in assessing impulsivity across a wide range of populations. As such, accurate estimation of the shape of each individual's temporal discounting profile is paramount when drawing conclusions about how impulsivity relates to clinical and health outcomes such as gambling, addiction, and obesity. Here, we identify an estimation problem with current methods of assessing temporal discounting behavior, and propose a simple solution. First, through a simulation study we identify types of temporal discounting profiles that cannot reliably be estimated. Second, we show how imposing constraints through hierarchical modeling ameliorates these recovery problems. Finally, we apply our solution to a large data set from a temporal discounting task, and illustrate the importance of reliable estimation within patient populations. We conclude with a brief discussion on how hierarchical Bayesian methods can aid in model estimation, compensate for small samples, and improve predictions of externalizing psychopathology.

A computational model of the Cambridge gambling task with applications to substance use disorders.

BACKGROUND: Impulsivity is central to all forms of externalizing psychopathology, including problematic substance use. The Cambridge Gambling task (CGT) is a popular neurocognitive task used to assess impulsivity in both clinical and healthy populations. However, the traditional methods of analysis in the CGT do not fully capture the multiple cognitive mechanisms that give rise to impulsive behavior, which can lead to underpowered and difficult-to-interpret behavioral measures. OBJECTIVES: The current study presents the cognitive modeling approach as an alternative to traditional methods and assesses predictive and convergent validity across and between approaches. METHODS: We used hierarchical Bayesian modeling to fit a series of cognitive models to data from healthy controls (N = 124) and individuals with histories of substance use disorders (Heroin: N = 79; Amphetamine: N = 76; Polysubstance: N = 103; final total across groups N = 382). Using Bayesian model comparison, we identified the best fitting model, which was then used to identify differences in cognitive model parameters between groups. RESULTS: The cognitive modeling approach revealed differences in quality of decision making and impulsivity between controls and individuals with substance use disorders that traditional methods alone did not detect. Crucially, convergent validity between traditional measures and cognitive model parameters was strong across all groups. CONCLUSION: The cognitive modeling approach is a viable method of measuring the latent mechanisms that give rise to choice behavior in the CGT, which allows for stronger statistical inferences and a better understanding of impulsive and risk-seeking behavior.