无计划，却有人类：一种反应式机器人模型预测临床任务中人类计划失败
No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task
AI总结：
该研究利用反应式梯度下降框架AICON，通过塔罗伦敦测试揭示人类计划能力下降时的反应模式，发现其能更准确预测24个问题的难度排序，并在留出验证中表现优异，揭示了生物系统组织方式的普遍规律。
摘要：
理解为何某些顺序规划问题比其他问题更难需要超越平均性能的模型。这些模型应捕捉问题难度的具体模式，并理想情况下以与人类计划能力下降时相同的方式失败。我们应用为机器人操作开发的AICON反应式梯度下降框架，应用于塔罗伦敦测试，该测试用于评估帕金森病、轻度认知障碍和中风患者的规划能力。在不进行任何前瞻规划或了解人类认知的情况下，AICON在24个问题上更准确地再现了人类的细粒度难度排序，优于结构任务参数，并在留出验证中泛化到新问题。关键的是，AICON在计划能力下降的群体中优于计划基线，而计划基线更好地捕捉健康对照组。这种分离由原始AICON论文预测，该论文指出模型的失败模式与帕金森患者在目标层次结构上挣扎但不移动计数的情况相似。这表明，随着计划能力的下降，人类行为会转向AICON所建模的反应模式。这一发现扩展了更广泛的模式：AICON最初为机器人开发，现在能捕捉生物行为在感知、眼动和顺序规划方面的特征，表明其核心抽象反映了生物系统组织方式的真实特性。
英文摘要：
Understanding why some sequential planning problems are harder than others requires models that go beyond average performance. They should capture the specific pattern of which problems are hard, and ideally fail in the same way people do when planning capacity is reduced. We apply AICON, a reactive gradient-descent framework developed for robotic manipulation, to the Tower of London test, a cognitive test used to assess planning in Parkinson's disease, mild cognitive impairment, and stroke. Without any lookahead planning or knowledge of human cognition, AICON reproduces the fine-grained human difficulty ordering across 24 problems better than structural task parameters and generalizes to held-out problems in a leave-two-out evaluation. Crucially, AICON outperforms a planning baseline for groups with reduced planning capacity while the planning baseline better captures healthy controls. This dissociation was predicted by the original AICON paper, which noted that the model's failure modes resemble those of Parkinson's patients who struggle with goal hierarchies but not move counts. This suggests that as planning capacity is reduced, human behavior shifts toward the reactive mode AICON models. The finding extends a broader pattern: AICON, originally built for robotics, now captures aspects of biological behavior across perception, eye movements, and sequential planning, suggesting its core abstraction reflects something real about how biological systems are organized.
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