The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits
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Impulsivity has been strongly linked to addictive behaviors, but can be operationalized in a number of ways that vary considerably in overlap, suggesting multidimensionality.
This study tested the hypothesis that the latent structure among multiple measures of impulsivity would reflect the following three broad categories: impulsive choice, reflecting discounting of delayed rewards; impulsive action, reflecting ability to inhibit a prepotent motor response; and impulsive personality traits, reflecting self-reported attributions of self-regulatory capacity.
The study used a cross-sectional confirmatory factor analysis of multiple impulsivity assessments. Participants were 1252 young adults (62 % female) with low levels of addictive behavior, who were assessed in individual laboratory rooms at the University of Chicago and the University of Georgia. The battery comprised a Delay (replace hyphen with space) Discounting Task, Monetary Choice Questionnaire, Conners’ Continuous Performance Test, Go/NoGo Task, Stop Signal Task, Barratt Impulsiveness Scale, and the UPPS-P Impulsive Behavior Scale.
The hypothesized three-factor model provided the best fit to the data, although sensation seeking was excluded from the final model. The three latent factors were largely unrelated to each other and were variably associated with substance use.
These findings support the hypothesis that diverse measures of impulsivity can broadly be organized into three categories that are largely distinct from one another. These findings warrant investigation among individuals with clinical levels of addictive behavior and may be applied to understanding the underlying biological mechanisms of these categories.
KeywordsImpulsivity Addiction Delay discounting Behavioral inhibition Impulsive personality Confirmatory factor analysis
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This work was partially supported by National Institutes of Health grant R01 DA032015 (de Wit, Palmer, MacKillop) and the Peter Boris Chair in Addictions Research (MacKillop).
- Babor TF, Biddle-Higgins JC, Saunders JB, Monteiro MG (2001) AUDIT: the Alcohol Use Disorders Identification Test: guidelines for use in primary health care. World Health Organization, GenevaGoogle Scholar
- Brown TA (2015) Confirmatory factor analysis for applied research. Guilford Press, New York, NY, USGoogle Scholar
- Eisenberg DT, Mackillop J, Modi M, et al. (2007) Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI a and DRD4 48-bp VNTR association study. Behav Brain Funct 3:–2. doi: 10.1186/1744-9081-3-2
- Little R, Rubin DB (2002) Statistical analysis with missing values. Wiley, New York, NY USGoogle Scholar
- Mazur JE (1987) An adjusting procedure for studying delayed reinforcement. In: Commons ML, Mazur JE, Nevin JA, Rachlin H (eds) Quantitative analysis of behavior. The effect of delay and of intervening events of reinforcement value, vol 5. Hillsdale, NJ: Erlbaum, pp 55–73Google Scholar
- Muthén, L.K. and Muthén, B.O. (1998–2015). Mplus user’s guide. Seventh Edition. Los Angeles, CA: Muthén & MuthénGoogle Scholar
- Substance Abuse and Mental Health Services Administration (2014) Results from the 2013 National Survey on Drug Use and Health: summary of national findings. Rockville, MDGoogle Scholar