Abstract
Both the proponents and the opponents of probabilistic perception draw a distinction between representations of probabilities (e.g., the object I see is more likely to have orange hues than green) and probabilistic representations (this object is probably an orange and not an apple). The former corresponds to the probability distribution of sensory observations given the stimulus, while the latter corresponds to the opposite, the probabilities of potential stimuli given the observations. This dichotomy is important as even plants can respond to probabilistic inputs presumably without making any inferences about the stimulus. It is also important for the computational models of perception as the Bayesian observer aims to infer the stimulus, not the observations. It is then essential to evaluate the empirical evidence for probabilistic representations and not the representation of probabilities to answer the question posed by this symposium. However, is it possible to empirically distinguish between the two? We will discuss this question using the data from our recent work on probabilistic perception as an illustration.