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

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.