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

Abstract

Do neural network models capture the cognitive demands of human reasoning? Across seven reasoning tasks, we show that the length of the chain-of-thought generated by large reasoning models 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 out-of-the-box reasoning models reflect core features underlying problem and task complexity in human cognition, without requiring any built-in symbolic mechanisms.