Introduction

Visual scenes are often complex and ambiguous to interpret because of the myriad causes that generate them. To understand visual scenes, our visual systems have to rely on our prior experience and assumptions about the world. These priors are rooted in the statistical correlation structures of visual events in our experience. They can be learned and exploited for probabilistic inference in a Bayesian framework using graphical models. Thus, we believe that understanding the statistics of natural scenes and developing graphical models with these priors for inference are crucial for gaining theoretical and computational insights to guide neurophysiological experiments. In this chapter, we will provide our perspective based on our work on scene statistics, graphical models, and neurophysiological experiments.

An important source of statistical priors for inference is the statistical correlation of visual events in our natural experience. In fact, it has long been suggested in the psychology community that learning due to coherent covariation of visual events is crucial for the development of Gestalt rules (Koffka 1935) as well as models of objects and object categories in the brain (Gibson 1979; Roger and McClelland 2004). Nevertheless, there has been relatively little research on how correlation structures in natural scenes are encoded by neurons. Here, we will first describe experimental results obtained from multielectrode neuronal recording in the primary visual cortex of awake-behaving monkeys. Each study was conducted on at least two animals. These results reveal mechanisms at the neuronal level for the encoding and influence of scene priors in visual processing.