Citation

Paige KJ, Weigard A, Colder CR. Alcohol Clin. Exp. Res. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, John Wiley and Sons)

DOI

10.1111/acer.14759

PMID

35152465

Abstract

BACKGROUND: Implicit alcohol attitudes are considered important in the etiology of drinking, and theory posits reciprocal associations between them. Research testing reciprocal associations between implicit attitudes (using the Implicit Association Task, IAT) and drinking is limited by a failure to consider multiple processes influencing performance on the IAT and to disaggregate within- and between-person effects. The current study addressed these limitations by using a diffusion model to analyze IAT data and Latent Curve Models with Structured Residuals to test reciprocal associations.

METHODS: The sample included 314 emerging adults from the community (52% female; predominantly non-Hispanic Caucasian (76%) or African American (15%)) assessed annually for three years. Differences between IAT conditions in the drift rate parameter of the EZ-diffusion model (vΔ) were used as an alternative to traditional response-time-based indices from the IAT (d-scores). Differences in drift rate have been found to index implicit attitudes effectively.

RESULTS: Within-person reciprocal associations were supported, but between-person associations were not. Positive implicit alcohol attitudes (vΔ) were prospectively associated with heavy drinking, which was positively associated with subsequent positive implicit alcohol attitudes.

CONCLUSIONS: We found that positive implicit alcohol attitudes and heavy drinking reinforce each other in a negative cascade within individuals. The results highlight the importance of disaggregating within- and between-person prospective effects when testing dual process models and suggest that the diffusion model may be a fruitful approach to enhance the construct validity of IAT assessed implicit attitudes.

Language: en

Keywords

dual process theory; emerging adulthood; heavy drinking; implicit attitudes; response time measures