论文标题
人类通过交易实用程序和计算成本来分解任务
Humans decompose tasks by trading off utility and computational cost
论文作者
论文摘要
人类的行为是从计划将任务分解为目标，子目标和低级行动的计划中出现的。这些分解如何创建和使用？在这里，我们基于一个简单的想法，建议并评估任务分解的规范框架，即人们分解任务以降低计划的总体成本，同时保持任务绩效。分析11117个不同的图形结构计划任务，我们发现我们的框架证明了几种现有的任务分解启发式方法，并做出可以与两个替代规范性帐户区分开的预测。我们报告了一项对任务分解（$ n = 806 $）的行为研究，该研究使用了30个随机抽样图，这是比以前关于该主题的任何行为研究更大，更多样化的集合。我们发现，人类的反应与我们的任务分解框架更一致，而不是替代规范性账户，并且与我们的方法是合理的，与启发式（中间性 - 中心性）最一致。综上所述，我们的结果提供了对目标指导行为智能结构基础的计算原则的新理论见解。
Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition ($N=806$) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior.
