Active Observer Visual Problem-Solving Methods are Dynamically Hypothesized, Deployed and Tested
This addresses a gap in understanding human visual problem-solving for real-world tasks, though it is incremental as it extends an existing architecture (STAR) rather than introducing a new paradigm.
The study investigated how humans solve complex 3D visuospatial tasks as active observers, discovering that they dynamically hypothesize, test, and switch between diverse problem-solving strategies, with no observed learning effect.
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.