Toward the nature of automatic associations: item-level computational semantic similarity and IAT-based alcohol-valence associations

Abstract

Abstract Automatic associations involving alcohol have been proposed to play a role in drinking behavior. Such associations are often assessed using implicit measures such as the Implicit Association Test (IAT). Neural network language models provide computational measures of semantic relationships between words. These model-based measures could be related to behavioral alcohol-related associations as observed using the IAT. If so, this could provide a step toward better understanding of the nature of automatic associations and their relationship to behavior. The current study therefore aimed to test whether there is a systematic covariation over items between model-based and behavior-based associations. Analyses were performed for two single-target IATs from a previously published study. One task involved alcohol versus nonalcohol drinks and positive associates, and the other alcohol versus nonalcohol drinks and negative associates. The GenSim library and a pretrained word2vec model were used to calculate a relative computational association between specific items from the positive and negative categories, respectively, and the alcohol versus nonalcohol word sets. In both tasks, a significant covariance between items’ computational and behavioral measures of association was found over participants. The results thus add to the information on the relationship between neural network language models and psychological associations. They may provide methodological strategies for task design and data analysis. Models of semantic associations connect computational linguistics and social-cognitive psychology and may provide a theoretical link between measures of alcohol-related associations using verbal stimuli and alcohol-related cognition and behaviors.