Fenneman et al.’s (2022) review of formal impulsivity models: Implications for theory and measures of impulsivity.

Abstract

In Fenneman et al.’s (2022) review of theories and integrated impulsivity model, the authors distinguish between information impulsivity (i.e., acting without considering consequences) and temporal impulsivity (i.e., the tendency to pick sooner outcomes over later ones). The authors find that both types of impulsivity can be adaptive in different contexts. For example, when individuals experience scarcity of resources or when they are close to a minimum level of reserves (critical threshold). In this commentary, we extend their findings to a discussion about the measurement of impulsivity. We argue that a common method for measuring temporal impulsivity in which people make decisions between outcomes that are spaced out in time (intertemporal choice tasks), puts individuals in a specific context that is unlikely to generalize well to other situations. Furthermore, trait measures of impulsivity may only be modestly informative about future impulsive behavior because they largely abstract away from important context. To address these issues, we advocate for the development of dynamic measures of the two types of impulsivity. We argue that measuring temporal impulsivity in naturalistic contexts with varying environmental and state parameters could provide insights into whether individuals (i.e., humans and nonhuman animals) react to environmental changes adaptively, while trait measures of impulsivity more generally should collect and provide more contextual information. Dynamic measurement of different types of impulsivity will also allow for more discussion about adaptive impulsive responses in different contexts, which could help combat the stigmatization of various disorders associated with impulsivity.

Computational Analysis of 100K Choice Dilemmas

“…For example, gamble choice tasks, a staple in the decision scientists' toolbox, show weak correlations with a willingness to engage in common risky behaviors 12,13 , and are only modestly correlated with other artificial lab-based measures 14 . Similarly, paradigms designed to elicit people's time preferences show only small correlations with clinical, financial, and healthrelated intertemporal behaviors [15][16][17][18][19] . Recent analyses of field studies of behavioral interventions have likewise found small effect sizes 20,21 .…”