Abstract
Humans use their subjective uncertainty in their internal representations to make optimal decisions. However, perceptual uncertainty can have two components which have not been systematically distinguished or separately measured. Reducible uncertainty stemming from the noisiness of the system that can be modulated by external and internal factors such as contrast level or attention and that can be eliminated by integrating the noisy samples about the stimulus over extended time. In contrast, irreducible uncertainty originates from the inherent ambiguity of perception, it cannot be eliminated even with longer integration, and it lies at the core of the argument that perception is a probabilistic process. Previous studies reported evidence for representing uncertainty in early visual areas but have not clarified whether an irreducible component of uncertainty crucial for probabilistic perception is included in these low-level representations. To address this question, we used an orientation estimation paradigm, in which observers reported the perceived orientation of one of several briefly presented line segments or Gabor-patches together with their subjective uncertainty about their response. We varied looking time across trials and defined the irreducible component of uncertainty by the asymptotic level of performance in the limit of infinitely long looking time. We found a diverse modulation of reducible versus irreducible uncertainty by various stimulus properties. Contrast primarily affected reducible uncertainty, whereas increasing set size introduced irreducible uncertainty in perceptual representations. However, low contrast could also introduce irreducible uncertainty for more complex stimuli (Gabor-patches). Crucially, observers’ subjective uncertainty reports reflected both reducible and irreducible uncertainty and accurately followed their sum, total uncertainty. Our results indicate that perceptual representations reflect both the inherent ambiguity of perception and the internal noise of the system. This suggests that even low-level perceptual representations are fundamentally probabilistic, and appropriately take into account both kinds of uncertainties to achieve optimal decisions.