Computational modeling of social evaluative decision-making elucidates individual differences in adolescent anxiety

Adolescents experience significant developmental changes during a time of heightened sensitivity to social cues, particularly rejection by peers, which can be especially overwhelming for those with elevated levels of social anxiety. Social evaluative decision-making tasks have been useful in uncovering the neural correlates of information processing biases; however, linking youths' task-based performance to individual differences in psychopathology (e.g., anxiety symptoms) has proven more elusive. Here, we address this weakness with drift diffusion modeling to decompose youths' performance on the social judgment paradigm (SJP) to determine if this approach is useful in discovering individual differences in anxiety symptoms, as well as puberty, age, and sex. A sample of 103 adolescents (55 males, M_age = 14.49, SD = 1.69) completed the SJP and self-report measures of anxiety, as well as self- and parent-reported measures of puberty. The decision threshold parameter, reflecting the amount of evidence needed to make a social evaluative decision, predicted youth self-reported anxiety, above and beyond typical metrics of SJP performance. Our results highlight the potential advantage of parsing task performance according to the underlying cognitive processes. Future research would likely benefit from applying computational modeling approaches to social judgment tasks when attempting to uncover performance-based individual differences in psychopathology.