Overhang Tower: Resource-Rational Adaptation in Sequential Physical Planning
Not on the map. Why?
Abstract (arXiv)
Humans effortlessly navigate the physical world by predicting how objects behave under gravity and contact forces, yet how such judgments support sequential physical planning under resource constraints remains poorly understood. Research on intuitive physics debates whether prediction relies on the Intuitive Physics Engine (IPE) or fast, cue-based heuristics; separately, decision-making research debates deliberative lookahead versus myopic strategies. These debates have proceeded in isolation, leaving the cognitive architecture of sequential physical planning underspecified. How physical prediction mechanisms and planning strategies jointly adapt under limited cognitive resources remains an open question. Here we show that humans exhibit a dual transition under resource pressure, simultaneously shifting both physical prediction mechanism and planning strategy to match cognitive budget. Using Overhang Tower, a construction task requiring participants to maximize horizontal overhang while maintaining stability, we find that IPE-based simulation dominates early stages while CNN-based visual heuristics prevail as complexity grows; concurrently, time pressure truncates deliberative lookahead, shifting planning toward shallower horizons: a dual transition unpredicted by prior single-mechanism accounts. These findings reveal a hierarchical, resource-rational architecture that flexibly trades computational cost against predictive fidelity. Our results unify two long-standing debates (simulation vs. heuristics and myopic vs. deliberative planning) as a dynamic repertoire reconfigured by cognitive budget.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 39 references that are in the map. First posted on arXiv 10 Apr 2026.
Builds on
11 of 39 references.
References · 11
- 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
- 2021Physion: Evaluating Physical Prediction from Vision in Humans and Machinescited by 145
- 2019PHYRE: A New Benchmark for Physical Reasoningcited by 137
- 2020Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Futurecited by 83
- 2019Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoningcited by 60
- 2018ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stackingcited by 55
- 2023I-PHYRE: Interactive Physical Reasoningcited by 15
- 2022On the Learning Mechanisms in Physical Reasoningcited by 11
- 2016A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understandingcited by 9
- 2025Neural Force Field: Few-shot Learning of Generalized Physical Reasoningcited by 1
- 2025A simulation-heuristics dual-process model for intuitive physicscited by 1
Built on by
Not cited yet.