The human visual system is the most complex pattern recognition device known. In ways
that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous …

We perceive the shapes and material properties of objects quickly and reliably despite the
complexity and objective ambiguities of natural images. Typical images are highly complex …

Bayesian decision theory (BDT) is a mathematical framework that allows the experimenter to
model ideal performance in a wide variety of visuomotor tasks. The experimenter can use …

We argue that the study of human vision should be aimed at determining how humans
perform natural tasks with natural images. Attempts to understand the phenomenology of …

Bayesian probability theory has emerged not only as a powerful tool for building
computational theories of vision, but also as a general paradigm for studying human visual …

Through perception, an organism arrives at decisions about the external world, decisions
based on both current sensory information and prior knowledge concerning the …

A survey of probabilistic approaches to modeling and understanding brain function.
Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light …

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2
are static spatiotemporal filters that extract local features from a visual scene. The extracted …

Probability has played a central role in models of perception for more than a century, but a
look at probabilistic concepts in the literature raises many questions. Is being Bayesian the …

In this review we consider how Bayesian logic can help neuroscientists to understand
behaviour and brain function. Firstly, we review some key characteristics of Bayesian …