CogFormer: Learn All Your Models Once
Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fit complex models that were previously difficult or impossible to estimate, while also allowing rapid estimation across large numbers of datasets. However, the utility of SBI for iterating over varying modeling assumptions remains limited: changes to parameterizations, generative functions, priors, and design variables all necessitate model retraining, thereby diminishing the benefits of amortization. To address these issues, we pilot the CogFormer, a meta-amortized framework for cognitive modeling. Our framework trains a transformer-based architectu...
| Authors | Jerry M. Huang, Lukas Schumacher, Niek Stevenson et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 March 2026 |
| Last updated at source | 19 June 2026 |
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