arXiv:2604.09072cs.AI2026-04

人类在资源有限时会同时调整物理预测和规划策略,实现高效决策。

Overhang Tower: Resource-Rational Adaptation in Sequential Physical Planning

  • 用过悬塔任务发现:复杂度高时从模拟转向视觉启发式
  • 时间压力下规划深度变浅,放弃长远思考
  • 揭示认知资源动态分配的双轨适应机制

人类能自如应对物理世界中的重力与接触力,但其在资源受限条件下如何进行序列式物理规划仍不明确。关于直觉物理判断是依赖物理引擎(IPE)还是快速启发式存在争议;同时,决策研究也争论着深思熟虑的前瞻规划与短视策略的优劣。这些争论长期孤立,未形成统一认知架构。本文通过过悬塔任务(Overhang Tower),要求参与者在保持稳定前提下最大化水平悬出长度,发现:早期阶段以基于IPE的模拟为主,随着任务复杂度上升,转为使用卷积神经网络(CNN)驱动的视觉启发式;同时,在时间压力下,前瞻规划被截断,转向浅层规划。这种双重转变超出单一机制解释,揭示了一种分层、资源理性、可动态调节的认知架构,将物理预测与规划策略统一于认知预算框架下。

原文摘要 · Abstract (English)

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.

认知科学物理推理资源理性规划算法

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。