主动观察者视觉问题解决方法是动态假设的，部署和测试
Active Observer Visual Problem-Solving Methods are Dynamically Hypothesized, Deployed and Tested
摘要：
STAR架构旨在测试复杂的现实世界视觉空间任务和行为的视觉注意力的完整选择性调节模型的价值。然而，关于人类如何在3D中作为主动观察者解决这些任务的知识是贫乏的。因此，我们设计了一个新的实验装置，并检查这种行为。我们发现，人类表现出各种各样的解决问题的策略，其广度和复杂性是令人惊讶的，不容易处理当前的方法。很明显，解决方案方法是通过假设动作序列，测试它们，如果失败，尝试不同的方法来动态组合的。积极观察的重要性是惊人的，因为缺乏任何学习效果。这些结果通知我们的认知程序表示的STAR扩展其相关性，以现实世界的任务。
英文摘要：
The STAR architecture was designed to test the value of the full Selective Tuning model of visual attention for complex real-world visuospatial tasks and behaviors. However, knowledge of how humans solve such tasks in 3D as active observers is lean. We thus devised a novel experimental setup and examined such behavior. We discovered that humans exhibit a variety of problem-solving strategies whose breadth and complexity are surprising and not easily handled by current methodologies. It is apparent that solution methods are dynamically composed by hypothesizing sequences of actions, testing them, and if they fail, trying different ones. The importance of active observation is striking as is the lack of any learning effect. These results inform our Cognitive Program representation of STAR extending its relevance to real-world tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
