A simulation-heuristics dual-process model for intuitive physics

2025-04-13

AI Summary

This study investigates the boundaries of mental simulation in human intuitive physical reasoning, focusing on two systematic errors in pour-angle prediction. We propose the Simulation–Heuristic Model (SHM), the first quantitative framework characterizing the adaptive switch from short-horizon mental simulation to long-horizon linear heuristics. Integrating mental simulation modeling, behavioral experimentation, linear-regression heuristic modeling, and computational cognitive modeling, SHM accurately reproduces human error patterns across multiple scenarios while maintaining robust predictive performance. It substantially improves fit to real-world intuitive physical behavior compared to single-mechanism accounts. By transcending the limitations of unitary explanatory frameworks, SHM provides the first computationally explicit and empirically testable dual-mode dynamic account of the adaptive nature of intuitive physical reasoning.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simulation time exceeded a certain boundary. Motivated by these observations, we propose a dual-process framework, Simulation-Heuristics Model (SHM), where intuitive physics employs simulation for short-time simulation but switches to heuristics when simulation becomes costly. By integrating computational methods previously viewed as separate into a unified model, SHM quantitatively captures their switching mechanism. The SHM aligns more precisely with human behavior and demonstrates consistent predictive performance across diverse scenarios, advancing our understanding of the adaptive nature of intuitive physical reasoning.

Problem

• Investigates mental simulation's role in physical reasoning boundaries
• Examines human error patterns in pouring angle predictions
• Proposes dual-process model for adaptive intuitive physics

Innovation

• Dual-process model combining simulation and heuristics
• Switches between simulation and heuristics based on cost
• Unifies separate methods into adaptive quantitative framework