The cost of thinking is similar between large reasoning models and humans

Do neural network models capture the cognitive demands of human reasoning? Across four reasoning domains, we show that the length of the chain-of-thought generated by a large reasoning model predicts human reaction times both within tasks—tracking item-level difficulty—and across tasks—capturing broader differences in cognitive demands. This model-to-human alignment shows that reasoning models mirror core features of problem and task complexity in human cognition.