Our conclusion was that this is a solid first round of evidence for EEA as a computationally rigorous and biologically grounded metric of task-general executive functioning in adolescents. My personal conclusion is that I can’t wait to model more data! 💜

We found that EEA had reasonable rank-order stability as kids developed (with over a year between visits!) and had robust links to both brain activation and behavior. (You’ll see SDRT on the brain picture — it performs pretty well too ☺️)

Following Alex Weigard’s work in adults, we used latent factor modeling to generate a task-general Efficiency of Evidence Accumulation (EEA) from condition-specific drift rates. EEA indexes how efficiently a person can make correct decisions among noise.

Modeling task data from a study of adolescents that was NOT designed for this sort of thing (my grad mentor Luke Hyde’s MTwiNS project) provided us with a meaningful metric (drift rate) that integrated accuracy and reaction time info and correlated across tasks and conditions.

Have you ever wondered “how should I be analyzing my cognitive task data”? Well, I’ve been obsessed with that question since first reading my co-author Alex Weigard’s work in 2020. And now our deep dive is out in the Journal of Psychopathology and Clinical Science! psycnet.apa.org/doi/10.1037/...