AI Mental Models: Learned Intuition and Deliberation in a Bounded Neural Architecture

TL;DR

This paper investigates whether a bounded neural architecture can develop structured internal processes resembling intuition and deliberation during syllogistic reasoning, demonstrating significant improvements over baseline models.

Contribution

It introduces a dual-path neural architecture inspired by mental-model theory, showing how separate intuition and deliberation pathways can enhance reasoning performance.

Findings

Bounded deliberation pathway outperforms intuition in correlation with human responses

Internal structure of the deliberation pathway shows meaningful differentiation and sparsity

Model exhibits reasoning-like internal organization without full sequential process reproduction

Abstract

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 pathways, motivated by computational mental-model theory (Khemlani & Johnson-Laird, 2022). Under cross-validation, bounded intuition reaches an aggregate correlation of r = 0.7272, whereas bounded deliberation reaches r = 0.8152, and the deliberation advantage is significant across folds (p = 0.0101). The largest held-out gains occur for NVC, Eca, and Oca, suggesting improved handling of rejection responses and c-a conclusions. A canonical 80:20 interpretability run and a five-seed stability sweep further indicate that the deliberation pathway develops sparse, differentiated internal structure, including an Oac-leaning state, a dominant workhorse state, and several weakly used or unused states whose exact indices vary across runs. These findings are consistent with reasoning-like internal organization under bounded conditions, while stopping short of any claim that the model reproduces full sequential processes of model construction, counterexample search, and conclusion revision.