Engaging proactive control: Influences of diverse language experiences using insights from machine learning

We used insights from machine learning to address an important but contentious question: is bilingual language experience associated with executive control abilities? Specifically, we assess proactive executive control for over 400 young adult bilinguals via reaction time on an AX continuous performance task (AX-CPT). We measured bilingual experience as a continuous, multidimensional spectrum (i.e., age of acquisition, language entropy, and sheer second language exposure). Linear mixed effects regression analyses indicated significant associations between bilingual language experience and proactive control, consistent with previous work. Information criteria (e.g., AIC) and cross-validation further suggested that these models are robust in predicting data from novel, unmodeled participants. These results were bolstered by cross-validated LASSO regression, a form of penalized regression. However, the results of both cross-validation procedures also indicated that similar predictive performance could be achieved through simpler models that only included information about the AX-CPT (i.e., trial type). Collectively, these results suggest that the effects of bilingual experience on proactive control, to the extent that they exist in younger adults, are likely small. Thus, future studies will require even larger or qualitatively different samples (e.g., older adults or children) in combination with valid, granular quantifications of language experience to reveal predictive effects on novel participants.