Overhang Tower: Resource-Rational Adaptation in Sequential Physical Planning
2026-04-10 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how people plan physical actions when building something tricky, like stacking blocks to hang over the edge. They found that when people have plenty of time and mental resources, they use detailed mental simulations to predict what will happen. But when things get harder or there's less time, people switch to quicker guesswork and plan less far ahead. This shows our minds balance accuracy and effort by changing strategies depending on how much thinking power we have.
Intuitive Physics Engineheuristicssequential planningcognitive resourcesdeliberative lookaheadmyopic strategyresource-rationalityOverhang Tower taskmental simulationvisual heuristics
Authors
Ruihong Shen, Shiqian Li, Yixin Zhu
Abstract
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.