Feedback refines visual perception by reducing sensory noise and calibrating perception-action mapping

doi:10.3724/SP.J.1041.2026.2167

Abstract

Abstract: Visual perception is often characterized by systematic biases, which have been successfully explained by a three-stage cascade model comprising efficient sensory encoding, Bayesian decoding, and perception-action mapping. However, this framework was developed primarily under no-feedback conditions, leaving it unclear how feedback—an essential component of perceptual learning—modulates these computational stages. The current study investigates how feedback influences visual estimation of self-motion direction (heading) from optic flow, and whether its effects can be explained by changes in sensory noise, prior integration, or perception-action scaling within a unified Bayesian framework.
Forty participants performed a heading estimation task using optic flow stimuli simulating self-motion directions ranging from -33° to 33°. A between-subjects design was employed: one group (n = 20) completed the task without feedback, while the other group (n = 20) received trial-by-trial visual feedback indicating the discrepancy between their estimate and the true heading. Both groups performed the task under three response range conditions (80°, 160°, and 240°), with 720 trials per participant. To uncover the computational basis of feedback effects, we developed seven Bayesian observer models that varied in their assumptions about which components were modulated by feedback: sensory noise (κ), prior integration (weighted combination of natural and experimental priors, w), perception-action scaling (α), and the boundary of the experimental prior (b). Model parameters were jointly fitted to individual participant data using maximum likelihood estimation, and model performance was compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC).
Behaviorally, wider response ranges induced a systematic transition from underestimation (at 80°) to overestimation (at 160° and 240°). Feedback significantly reduced overestimation bias and estimation variability, particularly for the wider response ranges. Model comparison revealed that Model 4—which incorporated feedback-dependent sensory noise, weighted integration of natural and experimental priors, and range-specific perception-action scaling—provided the best account of the data, with the lowest AIC and BIC values across conditions. Parameter estimates from Model 4 showed that feedback significantly increased κ (indicating reduced sensory noise) and significantly decreased the scaling factors α₂ and α₃ for the two wider response ranges, while α₁ (80° range) remained unchanged. Critically, the prior weighting parameter w did not differ between feedback conditions, suggesting that feedback did not alter how observers combined long-term natural priors with short-term experimental priors.
Feedback refines visual heading estimation by reducing sensory encoding noise and attenuating the gain of perception-action mapping, rather than by modifying prior integration. These findings reveal dissociable computational roles for feedback across distinct stages of visual processing. By extending a validated cascade model to incorporate feedback, this study provides a unified computational account of how external error signals improve perceptual accuracy. The results bridge behavioral performance with mechanistic modeling, offering theoretical insights into feedback-driven perceptual learning and practical implications for paradigms involving perceptual training, sensorimotor adaptation, and clinical rehabilitation where feedback plays a central role.

Key words: visual perception, Bayesian observer model, efficient coding, feedback, perception-action mapping

SUN Qi. (2026). Feedback refines visual perception by reducing sensory noise and calibrating perception-action mapping. Acta Psychologica Sinica, 58(11), 2167-2177.

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