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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.17661 |
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| _version_ | 1866910964332363776 |
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| author | Binz, Marcel Jagadish, Akshay K. Rmus, Milena Schulz, Eric |
| author_facet | Binz, Marcel Jagadish, Akshay K. Rmus, Milena Schulz, Eric |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated scientific minimization of regret Binz, Marcel Jagadish, Akshay K. Rmus, Milena Schulz, Eric Machine Learning 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. |
| title | Automated scientific minimization of regret |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.17661 |