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Related Experiment Video

Updated: Jun 3, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models

Barnes Maze Testing Strategies with Small and Large Rodent Models

Published on: February 26, 2014

Effects of maze appearance on maze solving.

Yelda Semizer¹, Dian Yu², Qianqian Wan³

¹Department of Humanities and Social Sciences, New Jersey Institute of Technology, Newark, NJ, USA. yelda.semizer@njit.edu.

Attention, Perception & Psychophysics

|January 10, 2025

Summary

Maze solving performance is impacted by visual crowding. Thicker paths and wavy walls increase maze-solving time, suggesting crowding and figure/ground segmentation play key roles.

Keywords:

2D shape and form Spatial vision Visual perception

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Last Updated: Jun 3, 2025

Barnes Maze Testing Strategies with Small and Large Rodent Models

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Published on: February 26, 2014

The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

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Published on: July 8, 2015

Using MazeSuite and Functional Near Infrared Spectroscopy to Study Learning in Spatial Navigation

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Area of Science:

Cognitive Psychology
Visual Perception
Computational Neuroscience

Background:

Maze solving is often hindered by visual crowding due to complex stimuli.
Maze design elements like path thickness, spacing, curvature, and texture may affect performance.

Purpose of the Study:

To investigate how perceptual aspects of maze design influence maze-solving performance.
To understand the roles of visual crowding and complexity in solving mazes.

Main Methods:

Two experiments were conducted using controlled maze stimuli.
Examined the effects of path and wall thickness and rendering style on maze-solving time.

Main Results:

Maze-solving time increased with thicker paths (and consequently thinner walls).
Maze-solving time was longer for mazes with wavy walls compared to straight walls, indicating higher crowding.

Conclusions:

Findings suggest that crowding and figure/ground segmentation are significant in mental maze solving.
Results may necessitate a reformulation of existing growth cone models.