Variance misperception under skewed empirical noise statistics explains overconfidence in the visual periphery

Abstract

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 -- but used them incorrectly -- showed increasing peripheral inflation under increasing peripheral inattention. Further, only one model successfully captured previous empirical results which showed a selective increase in confidence in incorrect responses under performance reductions due to inattention accompanied by no change in confidence in correct responses; this was the model that implemented Bayesian estimation of peripheral noise, but using an (incorrect) symmetric rather than the correct positively skewed peripheral noise prior. Our findings explain peripheral inflation, especially under inattention, and suggest future experiments that might reveal the noise expectations used by the visual metacognitive system. SignificancePerceptual confidence can dissociate from accuracy in peripheral visual perception, a phenomenon known as peripheral inflation. No previous model has considered how this phenomenon may arise from metacognitive noise estimation which depends on empirical noise statistics. Here we simulate Bayesian-inspired noise estimation algorithms to show that the systems erroneous beliefs about distributions of noise in the visual periphery can explain the occurrence of peripheral inflation, including how inflation varies with attentional manipulations in surprising ways. Our results explain why peripheral inflation occurs by positing a new Bayesian metacognitive noise estimation mechanism, paving the way for future psychophysical studies.