Newswise — Impulsivity is broadly described as the tendency to act without prior thought. It is often linked to alcohol misuse in college students. However, impulsivity is a complex concept and it is likely that different subtypes of this psychological construct are associated with different patterns of alcohol misuse. This study examined links among several categories of impulsivity with both the frequency of alcohol consumption and the frequency of alcohol intoxication.
Researchers recruited 106 university students (59 males, 47 females), all of whom reported alcohol use in the previous 12 months. The students self-reported their personal use of alcohol, nicotine, cannabis, and other drugs, and completed tasks used to measure different aspects of impulsivity. Computerized learning, using mathematical principles that power artificial intelligence (also known as machine learning), was used to predict alcohol intoxication and consumption frequency over several impulsivity subcategories.
Individual differences in impulsivity significantly predicted intoxication frequency but not consumption frequency. The three subcategories that supported a tendency toward more frequent intoxication were called trait impulsivity (attentional, non-planning, disinhibition, and experience seeking), questionnaire-based choice impulsivity (delay discounting), and task-based cognitive impulsivity (sustained attention). The findings support the view that different components of impulsivity contribute to different patterns of alcohol misuse. Furthermore, those students with higher intoxication frequency were significantly more likely to have experienced negative consequences of alcohol use, such as alcohol-related unprotected sex and physical injury. The researchers also noted that machine learning is a useful method for analyzing large amounts of data and it provides more nuanced insights into the relationship between alcohol use and psychological characteristics such as impulsivity.