Learning expectations shape cognitive control allocation - Javier Alejandro Masís Obando; Sebastian Musslick; Jonathan D. Cohen (Proceedings of the National Academy of Sciences, 2025)
Current models frame the allocation of cognitive control as a process of expected utility maximization. The benefits of a candidate control signal are weighed against its costs (e.g., opportunity costs). Recent theorizing has found that, despite promoting the counterintuitive behavior of longer deliberation, which is less rewarding in the short term, it is nevertheless normative to account for the value of learning when determining control allocation. Here, we sought to test this proposal by examining whether people were willing to allocate greater control and thereby expend greater effort (e.g., deliberate for longer) when they perceived a task to be learnable compared to when they did not. We found that participants’ proficiency and learning rate in the first block of a simple perceptual dot-motion task were able to predict their willingness to deliberate in a second block. These findings support the hypothesis that agents consider learnability when allocating cognitive control, and comply with a formal model of control allocation that considers the future discounted value of learning on reward.