Perceptual confidence typically corresponds to accuracy. However, observers can be overconfident relative to accuracy, termed ‘subjective inflation’. Inflation is stronger in the visual periphery relative to central vision, especially under conditions of peripheral inattention. Previous literature suggests inflation stems from errors in estimating noise, i.e. ‘variance misperception’. However, despite previous Bayesian hypotheses about metacognitive noise estimation, no work has systematically explored how noise estimation may critically depend on empirical noise statistics which may differ across the visual field, with central noise distributed symmetrically but peripheral noise positively skewed. Here we examined central and peripheral vision predictions from five Bayesian-inspired noise-estimation algorithms under varying usage of noise priors, including effects of attention. Models that failed to optimally estimate noise exhibited peripheral inflation, but only models that explicitly used peripheral noise priors incorrectly showed increasing peripheral inflation under increasing peripheral inattention. Our findings explain peripheral inflation, especially under inattention.
bioRxiv Subject Collection: Neuroscience