AI Mental Models: Learned Intuition and Deliberation in a Bounded Neural Architecture
This paper asks whether a bounded neural architecture can exhibit a meaningful division of labor between intuition and deliberation on a classic 64-item syllogistic reasoning benchmark. More broadly, the benchmark is relevant to ongoing debates about world models and multi-stage reasoning in AI. It provides a controlled setting for testing whether a learned system can develop structured internal computation rather than only one-shot associative prediction. Experiment 1 evaluates a direct neural baseline for predicting full 9-way human response distributions under 5-fold cross-validation. Experiment 2 introduces a bounded dual-path architecture with separate intuition and deliberation path...
| Authors | Laurence Anthony |
|---|---|
| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 5 October 2026 |
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