Anxiety Modulates Preference for Immediate Rewards Among Trait-Impulsive Individuals: A Hierarchical Bayesian Analysis

Abstract

Trait impulsivity—defined by strong preference for immediate over delayed rewards and difficulties inhibiting prepotent behaviors—is observed in all externalizing disorders, including substance-use disorders. Many laboratory tasks have been developed to identify decision-making mechanisms and correlates of impulsive behavior, but convergence between task measures and self-reports of impulsivity are consistently low. Long-standing theories of personality and decision-making predict that neurally mediated individual differences in sensitivity to (a) reward cues and (b) punishment cues (frustrative nonreward) interact to affect behavior. Such interactions obscure one-to-one correspondences between single personality traits and task performance. We used hierarchical Bayesian analysis in three samples with differing levels of substance use ( N = 967) to identify interactive dependencies between trait impulsivity and state anxiety on impulsive decision-making. Our findings reveal how anxiety modulates impulsive decision-making and demonstrate benefits of hierarchical Bayesian analysis over traditional approaches for testing theories of psychopathology spanning levels of analysis.

Disentangling cognitive processes in externalizing psychopathology using drift diffusion modeling: Antagonism, but not disinhibition, is associated with poor cognitive control

“…These problems, however, may largely reflect the use of person‐level summary statistics that (a) do not retain trial‐to‐trial variability within persons and (b) do not examine individual differences in cognitive processes thought to underlie performance on the task (Haines, Kvam, et al., 2020; Rouder & Haaf, 2019). Recent work on this topic has highlighted the importance of allowing for heterogeneity in experimental effects in data analyses (i.e., relative to simpler ANOVA‐style analysis; Bolger et al., 2019) and the value of computational cognitive models that provide testable predictions about the processes that putatively generate performance on tasks (Haines, Beauchaine, et al., 2020).…”

Different components of cognitive-behavioural therapy affect specific cognitive mechanisms

“…Joint modelling allows maximum use of participant-level data, whilst retaining information about uncertainty or precision of each kind of measurement (Turner et al, 2017;Haines et al, 2020a;Haines, 2021;Hopkins et al, 2021).…”

“…For the joint model, individual estimates for trait amotivation (θ A ) and/or trait negative cognition (θ N ; constructed as above), were allowed to influence the effect of intervention on time 2 parameter estimates (ϕ IN T ) found to show evidence of heterogeneous individual responses via the inclusion of additional β weight parameters (β IN T ; see Haines et al 2020a;Hopkins et al 2021) for previous examples of this approach). These β weights can interpreted similarly as in a standard regression model, with the group-level intervention effect (ϕ IN T ) now representing the intercept (see below).…”

Troubleshooting Bayesian cognitive models.

“…Brooks et al, 2011) most popularly used for Bayesian model fitting 2 (such as JAGS: Plummer, 2003; and Stan: Carpenter et al, 2017) and software that likewise automates Bayesian model specification (for linear models: Bürkner, 2017; and for select cognitive models: Ahn et al, 2017). These developments have made Bayesian cognitive modeling accessible to psychologists across subfields, including cognitive psychologists (e.g., Donkin et al, 2016; Navarro et al, 2016; Westfall & Lee, 2021), cognitive neuroscientists (e.g., Frank et al, 2015; Nunez et al, 2019; Peters & D’Esposito, 2020), clinical psychologists (e.g., Haines et al, 2020; Brown et al, 2021; Lasagna et al, 2022), and social psychologists (e.g., Pleskac et al, 2018; Golubickis et al, 2018; Schaper et al, 2019).…”

“…While using Bayesian methods for cognitive modeling had long been the province of mathematical psychologists, as it required comfort with mathematical statistics and statistical programming (Gilks et al, 1995;Gelman et al, 2013; for an example, see Rouder & Lu, 2005), this has changed with the maturation of software that automates the Markov chain Monte Carlo (MCMC) methods (S. Brooks et al, 2011) most popularly used for Bayesian model fitting 2 (such as JAGS : Plummer, 2003;and Stan: Carpenter et al, 2017) and software that likewise automates Bayesian model specification (for linear models : Bürkner, 2017; and for select cognitive models: Ahn et al, 2017). These developments have made Bayesian cognitive modeling accessible to psychologists across subfields, including cognitive psychologists (e.g., Donkin et al, 2016;Navarro et al, 2016;Westfall & Lee, 2021), cognitive neuroscientists (e.g., Frank et al, 2015;Nunez et al, 2019;Peters & D'Esposito, 2020), clinical psychologists (e.g., Haines et al, 2020;Brown et al, 2021;Lasagna et al, 2022), and social psychologists (e.g., Pleskac et al, 2018;Golubickis et al, 2018;Schaper et al, 2019).…”