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

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.

Figures (7)

• Figure 1: Architecture of the bounded intuition-and-deliberation model used in Experiment 2. The intuition pathway and the deliberation pathway receive the same structured syllogism input through separate encoders, and the deliberation pathway computes through five candidate deliberative states combined by a learned gate.
• Figure 2: Representative held-out performance for the direct baseline in Experiment 1. The three panels show the human response matrix, the model prediction matrix, and the signed difference matrix on the test split.
• Figure 3: Intuition-versus-deliberation performance on the canonical single split used for interpretability. Deliberation improves both correlation and error relative to intuition.
• Figure 4: Held-out response-distribution predictions from the bounded intuition pathway in Experiment 2.
• Figure 5: Held-out response-distribution predictions from the deliberation pathway in Experiment 2. Relative to intuition, deliberation more closely tracks the human response matrix on the same held-out items.