In collaboration with @tomlamarra.bsky.social Andrea Amelio Ravelli @chiarasaponaro.bsky.social @beatricegiustolisi.bsky.social @mariannabolog.bsky.social

I'd love to watch this, is there a recording?

Why does this alignment emerge? There are similarities in how reasoning models and humans learn: first by observing worked examples (pretraining), then by practicing with feedback (RL). In the end, just like humans, they allocate more effort to harder problems. (6/6)

Token count also captures differences across tasks. Avg. token count predicts avg. RT across domains (r = 0.97, left), and even item-level RTs across all tasks (r = 0.92 (!!), right). (5/6)

We found that the number of reasoning tokens generated by the model reliably correlates with human RTs within each task (mean r = 0.57, all ps < .001). (4/6)

Large reasoning models can solve many reasoning problems, but do their computations reflect how humans think?
We compared human RTs to DeepSeek-R1’s CoT length across seven tasks: arithmetic (numeric & verbal), logic (syllogisms & ALE), relational reasoning, intuitive reasoning, and ARC (3/6)

Neural networks are powerful in-silico models for studying cognition: LLMs and CNNs already capture key behaviors in language and vision. But can they also capture the cognitive demands of human reasoning? (2/6)