Unconscious inference of Visual Perception
In psychology, visual perception is the ability to interpret visible light information reaching the eyes which is then made available for planning and action. The resulting perception is also known as eyesight, sight or vision. The various components involved in vision are known as the visual system.
Human eyesHermann von Helmholtz is often credited with the first study of visual perception in modern times. Helmholtz held vision to be a form of unconscious inference: vision is a matter of deriving a probable interpretation for incomplete data.
Inference requires prior assumptions about the world: two well-known assumptions that we make in processing visual information are that light comes from above, and that objects are viewed from above and not below. The study of visual illusions (cases when the inference process goes wrong) has yielded much insight into what sort of assumptions the visual system makes.
The unconscious inference hypothesis has recently been revived in so-called Bayesian studies of visual perception.
Proponents of this approach consider that the visual system performs some form of Bayesian inference to derive a perception from sensory data. Models based on this idea have been used to describe various visual subsystems, such as the perception of motion or the perception of depth.Gestalt psychologists working primarily in the 1930s and 1940s raised many of the research questions that are studied by vision scientists today.
The Gestalt Laws of Organization have guided the study of how people perceive visual components as organized patterns or wholes, instead of many different parts. Gestalt is a German word that translates to “configuration or pattern”. According to this theory, there are six main factors that determine how we group things according to visual perception: Proximity, Similarity, Closure, Symmetry, Common fate and Continuity.
The major problem with the Gestalt laws (and the Gestalt school generally) is that they are descriptive not explanatory. For example, one cannot explain how humans see continuous contours by simply stating that the brain “prefers good continuity”. Computational models of vision have had more success in explaining visual phenomena and have largely superseded.