Understanding Humans' Strategies in Maze Solving
This research provides insights into human cognitive strategies, with potential applications in cognitive neuroscience, AI, robotics, and human-robot interactions, but it is incremental as it builds on existing knowledge of navigation and decision-making.
The study investigated human strategies in solving visual mazes, revealing that people use distinct exploration and guidance modes, learning to trade off visual exploration and motor performance based on factors like memory and confidence.
Navigating through a visual maze relies on the strategic use of eye movements to select and identify the route. When navigating the maze, there are trade-offs between exploring to the environment and relying on memory. This study examined strategies used to navigating through novel and familiar mazes that were viewed from above and traversed by a mouse cursor. Eye and mouse movements revealed two modes that almost never occurred concurrently: exploration and guidance. Analyses showed that people learned mazes and were able to devise and carry out complex, multi-faceted strategies that traded-off visual exploration against active motor performance. These strategies took into account available visual information, memory, confidence, the estimated cost in time for exploration, and idiosyncratic tolerance for error. Understanding the strategies humans used for maze solving is valuable for applications in cognitive neuroscience as well as in AI, robotics and human-robot interactions.