Static Estimation of Vista-Space Egocentric Distance with Iterative Feedback: A Cognitive-Perceptual Task.

Area of Science:

• Cognitive Psychology
• Human Perception
• Spatial Cognition

Background:

• Accurate egocentric distance estimation is crucial for navigation and interaction in large-scale environments.
• Perceptual encoding and cognitive recalibration interact to influence spatial accuracy.
• Understanding how feedback influences spatial learning is key to improving performance.

Purpose of the Study:

• To investigate the impact of iterative, feedback-based learning on spatial accuracy, perceptual bias, and task efficiency.
• To examine how participants recalibrate their distance estimations in large-scale outdoor environments.
• To analyze specific performance metrics including estimation error, number of attempts, and trial duration.

Main Methods:

• 133 participants performed outdoor distance estimations using a mobile web application with immediate corrective feedback.
• Estimations were made for targets at 134 m, 575 m, and 1463 m.
• Nonparametric statistical tests (Friedman, Wilcoxon signed-rank) were used due to non-normal data distribution.

Main Results:

• Significant within-subject effects were found across all distances for all analyzed variables (p < 0.001).
• Estimation error shifted from overestimation at 134 m to underestimation at 575 m and 1463 m (p < 0.0167).
• The number of attempts increased for the farthest target, indicating higher cognitive load, while trial duration showed non-linear adaptation.

Conclusions:

• Iterative feedback enhances perceptual stability and task efficiency but does not eliminate inherent distance compression biases.
• Observed bias and adaptation patterns suggest a feedback-driven, binary search-like recalibration mechanism.
• The mobile paradigm is a scalable and valid tool for assessing perceptual-cognitive calibration in diverse spatial contexts.