Bilingual Language Experience and Its Effect on Conflict Adaptation in Reactive Inhibitory Control Tasks
利用机器学习分析777名双语成人的数据，发现混合语言使用能预测数字Stroop任务中的冲突适应，表明双语经验可调节认知控制系统。
We used machine-learning techniques to assess interactions between language and cognitive systems related to inhibitory control and conflict adaptation in reactive control tasks. We built theoretically driven candidate models of Simon and Number Stroop task data ( N = 777 adult bilinguals ages 18–43 years living in Montréal, Canada) that differed in whether bilingual experience interacted with inhibitory control, including two forms of conflict adaptation: shorter term sequential congruency effects and longer term trial order effects. Models with continuous aspects of bilingual experience provided signal in predicting new, unmodeled data. Specifically, mixed language usage predicted trial order adaptation to conflict. This effect was restricted to Number Stroop, which overtly involves linguistic or symbolic information and relatively higher language- and response-related uncertainty. These results suggest that bilingual experience adaptively tunes aspects of the control system and offers a novel integrative modeling approach that can be used to pursue other complex individual difference questions within the psychological sciences.