Adaptation and visual coding

Abstract

Visual coding is a highly dynamic process and continuously adapting to the current viewing context. The perceptual changes that result from adaptation to recently viewed stimuli remain a powerful and popular tool for analyzing sensory mechanisms and plasticity. Over the last decade, the footprints of this adaptation have been tracked to both higher and lower levels of the visual pathway and over a wider range of timescales, revealing that visual processing is much more adaptable than previously thought. This work has also revealed that the pattern of aftereffects is similar across many stimulus dimensions, pointing to common coding principles in which adaptation plays a central role. However, why visual coding adapts has yet to be fully answered.

Figure 1

Examples of visual aftereffects. (a) The Lilac Chaser illusion (http://en.wikipedia.org/wiki/Lilac_chaser). With fixation on the cross, the lilac dots fade revealing a strong greenish afterimage when each dot is briefly removed. (b) Adaptation effects analogous to color are found for faces. When fixating the central face, the distortions in the peripheral faces become less apparent, leading to a strong perceived distortion when each face is briefly replaced with the undistorted center face.

Figure 2

Simulations of the changes in color appearance predicted in observers adapted to the color characteristics of different environments. Top images show roughly the same scene in two different seasons. Bottom images depict how the scenes might appear to an observer completely adapted to the color distributions from either wet or arid seasons.

Figure 3

Simulations of the changes in color appearance predicted as observers adapt to age-related changes in lens pigment density. (Left) The upper left image from Figure 2 as filtered through the eyes of an observer with the average lens density of a 70 year old (relative to the reference lens density of a 12 year old). (Right) The filtered image after adapting to the mean color shift by rescaling the cone sensitivities for the mean spectral change. Independent gain changes in the cones compensate for most (but not all) of the predicted effect of the lens screening on color appearance.

Figure 4

Adaptation and visual channels. Curves show the sensitivity of a set of channels before (dashed gray) or after (solid) adaptation. (a) Narrowly tuned channels and narrowband stimuli. Adaptation reduces sensitivity within the subset of channels sensitive to the adapting level, biasing the modal response to flanking stimuli away from the response to the adaptor. Response changes are similar for different adapting levels and thus there is no narrowband stimulus corresponding to a unique norm. (b) Broadly tuned channels and narrowband stimuli. Adaptation reduces sensitivity in the more strongly stimulated channel, shifting the balance point or norm (n) toward the adapting level. (c) Narrowly tuned channels and broadband stimuli. A unique norm occurs when the balance of activity is equal across the channels. Adaptation to a biased stimulus distribution renormalizes the balance, shifting the norm toward the adapting stimulus. (d) An opponent mechanism, in which the norm corresponds to the null between excitation and inhibition. Adaptation at a pre-opponent site can change the balance of inputs resulting in a mean shift in the norm toward the adapt level. Adaptation to contrast, at the opponent site, instead alters sensitivity without shifting the null point.

Figure 5

Simulations of changes in color appearance with adaptation to more extreme environmental variations. Upper images show the surface of Mars or an underwater scene as perceived by an observer adapted to a natural land environment on Earth. Lower images depict how the same scenes are perceived if observers are adapted within each new environment so that the responses in color mechanisms are matched to their responses under adaptation to the terrestrial Earth distribution.