# Signal-to-Noise Efficiency Explains Inter-Observer Variability in Orientation Discrimination

## TL;DR

This study shows that how well people can tell the orientation of visual patterns depends on noise, where the pattern appears in the visual field, and individual differences in perceptual efficiency.

## Contribution

The study introduces signal-to-noise efficiency as a key factor explaining variability in orientation discrimination across individuals and visual conditions.

## Key findings

- Accuracy in orientation discrimination decreases with higher external noise and increases with larger orientation offsets.
- Thresholds for orientation discrimination increase with both noise levels and retinal eccentricity.
- Signal-to-noise efficiency is the strongest predictor of performance, with age effects emerging selectively in the periphery.

## Abstract

Background: Orientation discrimination tasks provide a core measure of visual sensitivity and are widely used to study how perceptual performance varies with stimulus uncertainty and visual field location. Here, we examined how external noise, retinal eccentricity, and individual perceptual efficiency shape orientation discrimination thresholds. Methods: Forty-two adults (mean age = 32.35 years, SD = 7.23) completed a two-alternative forced-choice task judging the orientation (clockwise vs. counterclockwise) of briefly presented Gabor patches under varying levels of external noise (low, medium, high) and eccentricity (0°, 5°, 10°). Orientation offsets ranged from −8° to +8°. Thresholds were estimated using psychometric functions and analyzed via rm ANOVA, linear mixed-effects models, and supervised machine learning. Results: Accuracy declined with increasing noise (ω2 = 0.48, p < 0.001) and improved with larger orientation offsets (ω2 = 0.62, p < 0.001). Thresholds increased with both noise (ω2 = 0.31, p = 0.002) and eccentricity (ω2 = 0.27, p = 0.003). Signal-to-noise efficiency was the strongest predictor (β = −0.72, p < 0.001); age alone was nonsignificant, but its interaction with eccentricity showed selective peripheral declines. Mixed-effects models confirmed spatial effects (β = 0.058, p < 0.001) and residual between-subject variability (σ2 = 0.14). Predictive models generalized well (R2 = 0.54). Conclusions: Orientation discrimination is shaped by both stimulus-level difficulty and individual differences in perceptual efficiency, which account for variability in sensitivity across visual conditions. Age-related differences emerge primarily under spatial load and depend on interactions between observer traits and task demands.