Abstract
Probabilistic approaches to cognition have had great empirical success, especially in building computational models of perceptual processes. This success has led researchers to propose that the visual system represents sensory information probabilistically, which resulted in high-profile studies exploring the role of probabilistic representations in visual perception. Yet, there is still substantial disagreement over the conclusions that can be drawn from this work. In the first part of this talk, I will outline the critical views over the probabilistic nature of visual perception. Some critics underline the inability of experimental methodologies to distinguish between perceptual processes and perceptual decisions, while others point to the successful utilization of non-probabilistic representational schemes in explaining these experimental results. In the second part of the talk, I will propose two criteria that must be satisfied to provide empirical evidence for probabilistic visual representations. The first criterion requires experiments to demonstrate that representations involving probability distributions are actually generated by the visual system, rather than being imposed on the task by the experimenter. The second criterion requires the utilization of structural correspondence (as opposed to correlation) between the internal states of the visual system and stimulus uncertainty. Finally, I will illustrate how these two criteria can be met through a psychophysical methodology using priming effects in visual search tasks.