Hypothesis-guided discovery of cognitive algorithms via program refinement
Quick summary
arXiv:2610.02523v1 Announce Type: new Abstract: Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Traditional approaches to cognitive modeling are interpretable and benefit from human expertise, but lack flexibility and scalability. Emerging techniques using large language models (LLMs) for de novo generation of cognitive models are scalable and flexible, but lack a role for human expertise and have mostly been applied to simpler tasks than algorithm recovery. We propose a hybrid system that trea
Key takeaways
- arXiv:2610.02523v1 Announce Type: new Abstract: Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods.
- Traditional approaches to cognitive modeling are interpretable and benefit from human expertise, but lack flexibility and scalability.
- Emerging techniques using large language models (LLMs) for de novo generation of cognitive models are scalable and flexible, but lack a role for human expertise and have mostly been applied to simpler tasks than algorithm recovery.
Why it matters
“Hypothesis-guided discovery of cognitive algorithms via program refinement” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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