Bayesian sampling in visual perception

Bayesian sampling in visual perception.

It is well-established that some aspects of perception and action can be understood as probabilistic inferences over underlying probability distributions. In some situations, it would be advantageous for the nervous system to sample interpretations from a probability distribution rather than commit to a particular interpretation. In this study, we asked whether visual percepts correspond to sam...

Bayesian Modelling of Visual Perception

Innateness and ( Bayesian ) Visual Perception

Because innateness is such a complex and controversial issue when applied to higher level cognition, it can be useful to explore how nature and nurture interact in simpler, less controversial contexts. One such context is the study of certain aspects of visual perception—where especially rigorous models are possible, and where it is less controversial to claim that certain aspects of the visual...

Bayesian model learning in human visual perception

Humans make optimal perceptual decisions in noisy and ambiguous conditions. Computations underlying such optimal behavior have been shown to rely on probabilistic inference according to generative models whose structure is usually taken to be known a priori. We argue that Bayesian model selection is ideal for inferring similar and even more complex model structures from experience. We find in e...

Exploiting Visual Perception for Sampling-Based Approximation on Aggregate Queries

Efficient sampling algorithms have been developed for approximating answers to aggregate queries on large data sets. In some formulations of the problem, concentration inequalities (such as Hoeffding’s inequality) are used to estimate the confidence interval for an approximated aggregated value. Samples are usually chosen until the confidence interval is arbitrarily small enough regardless of h...