Understanding Humans' Strategies in Maze Solving

TL;DR

This study investigates how humans employ different strategies involving eye movements and memory when solving mazes, revealing complex trade-offs between exploration and guidance that depend on familiarity, visual cues, and individual preferences.

Contribution

It uncovers the existence of distinct exploration and guidance modes and details how humans adapt their maze-solving strategies based on visual, memory, and confidence factors.

Findings

Humans switch between exploration and guidance modes during maze solving.

Learning improves maze navigation efficiency and strategy complexity.

Strategies vary based on maze familiarity and individual error tolerance.

Abstract

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.