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

TL;DR

This study shows how repeated feedback helps people better estimate large distances, but some biases remain.

Contribution

A mobile-based method for studying iterative feedback in egocentric distance estimation is proposed.

Findings

Iterative feedback reduces perceptual error and increases estimation efficiency.

Distance compression bias persists despite feedback, with underestimation increasing at farther distances.

Cognitive load and correction effort rise with target distance, as shown by higher attempts and varied trial durations.

Abstract

Accurate egocentric distance estimation in vista space depends on the interaction between perceptual encoding and cognitive recalibration. This study examined how iterative, feedback-based learning modulates spatial accuracy, perceptual bias, and task efficiency in large-scale environments. A total of 133 participants (mean age = 26.3 ± 7.44 years) performed distance estimations on three outdoor targets (134 m, 575 m, 1463 m) using a mobile web application providing immediate corrective feedback (too short/too long). Six variables were analyzed: first estimate (FE), error of first estimate (EFE), mean estimate (ME), error of mean estimate (EME), number of attempts (NAs), and trial duration (TD). Given the non-normal data distribution, nonparametric tests were applied (Friedman and Wilcoxon signed-rank tests with Bonferroni correction). All variables showed significant within-subject effects across distances (p < 0.001). Post hoc analyses indicated that EFE and EME differed significantly between all target pairs (p < 0.0167), revealing a shift from slight overestimation at 134 m to increasing underestimation at 575 m and 1463 m. NA was significantly higher for the farthest target (p < 0.0167), indicating greater cognitive load and iterative correction effort. TD differed significantly only between consecutive distances (p < 0.0167), suggesting non-linear temporal adaptation. These results demonstrate that iterative feedback improves perceptual stability and efficiency but does not remove distance compression. The consistent bias and adaptive response patterns support a feedback-driven, binary search-like recalibration mechanism. The proposed mobile paradigm offers a scalable and valid approach for assessing perceptual–cognitive calibration in both natural and virtual spatial contexts.

Figures