Emerging Insights into Cognitive Control from Computational Models of Choice

Research into cognitive control has flourished over the last three decades across many areas of the psychological and neural sciences, but problems have recently emerged that raise foundational questions about what cognitive control is, how individual differences should be measured, and the validity of cognitive control as an explanation for variation in behavioral traits and clinical conditions. Here, we outline a novel perspective that is rooted in evidence accumulation models, formal mathematical models that explain how individuals make choices across many contexts. We review model-based work that distinguishes conflict-specific processes, which are selectively engaged to address goal-conflicting information, from task-general processes that facilitate goal-congruent responding irrespective of the presence of conflict. We then highlight the computationally-derived measure we call efficiency of evidence accumulation (EEA) that reflects the task-general amplification of goal-congruent information. EEA forms a trait-like individual difference dimension, explains individual differences in performance in cognitive control tasks, and shows clear relevance to behavioral traits and clinical conditions in which control is thought to be impaired. These findings help address multiple theoretical, methodological, and empirical challenges in the study of cognitive control and provide a promising way forward for characterizing control processes that enhance goal-relevant responding across time and contexts.