Automated scientific minimization of regret
This work addresses the challenge of automating cognitive modeling for researchers, though it appears incremental as it builds on existing methods like Centaur.
The paper tackles the problem of automating cognitive science by introducing the ASMR framework, which identifies gaps in interpretable cognitive models using a foundation model and revises them via language-based reasoning, resulting in models that predict human behavior at noise ceiling while maintaining interpretability.
We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimization, ASMR leverages Centaur -- a recently proposed foundation model of human cognition -- to identify gaps in an interpretable cognitive model. These gaps are then addressed through automated revisions generated by a language-based reasoning model. We demonstrate the utility of this approach in a multi-attribute decision-making task, showing that ASMR discovers cognitive models that predict human behavior at noise ceiling while retaining interpretability. Taken together, our results highlight the potential of ASMR to automate core components of the cognitive modeling pipeline.