Texture Density Adaptation

Novel results elucidating the magnitude, binocularity and retinotopicity of aftereffects of visual texture density adaptation are reported as is a new contingent aftereffect of texture density which suggests that the perception of visual texture density is quite malleable. Texture aftereffects contingent upon orientation, color and temporal sequence are discussed. A fourth effect is demonstrated in which auditory contingencies are shown to produce a different kind of visual distortion. The merits and limitations of error-correction and classical conditioning theories of contingent adaptation are reviewed. It is argued that a third kind of theory which emphasizes coding efficiency and informational considerations merits close attention. It is proposed that malleability in the registration of texture information can be understood as part of the functional adaptability of perception. 2/19/10 9:11 AM Texture Density Adaptation Page 2 of 55 http://www.swarthmore.edu/SocSci/fdurgin1/PerceptualLearning/PerLearn.html Introduction In order to maintain its wonderful illusion of direct and complete perception, the visual system must find ways to efficiently and accurately encode information. This problem seems of particular importance in the perception of visual details, such as textures, which seem to exist in such astonishing variety that their representation appears to have endless dimensionality. Casual introspection suggests that we seem to perceive textures in full detail, but this 'seeming' is suspicious. That this seemingly direct perception of texture is an illusion is made starkly evident by the aftereffects of texture density to be described in this paper. Still, our ability to identify and discriminate visual textures is impressive. What processes does the perceptual representation of texture depend on to be as successful as it normally is? One might expect perceptual learning to play an important role in the perception of visual texture because the visual system must choose how to represent texture. It ought to do it wisely and therefore adaptively. Indeed, evidence of perceptual learning has been reported, both in the learned improvement of texture discrimination (Karni and Sagi, 1991, 1993) and in the choice of coding mechanisms (e.g. Field, 1994; Foldiak, 1990) for the representation of textures. The purpose of the present article is to describe another kind of evidence of perceptual learning in the perception of texture that has emerged from work performed in our laboratories on the adaptation of perceived texture density. To accomplish our goal, our paper is divided into three distinct parts: To set the stage for understanding the contingent adaptation of texture perception, we will first provide a description of simple texture density adaptation and present data demonstrating some properties of the density aftereffect in the non-Fourier domain. Although there have been previous reports of texture density aftereffects (e.g. Anstis, 1974; Walker, 1966), none have heretofore distinguished density from spatial frequency (i.e. texture size). In the second portion of the paper we will review two contingent aftereffects of texture density recently reported by our laboratories and report a new one. To preview, Durgin (in press) has shown that perceived texture density can be made contingent on the color of a surrounding region, and Durgin and Hammer (1994, 1995) have made both texture density and texture brightness contingent on the temporal order of texture presentation. We will also describe experiments in which we have investigated the extent to which texture density can be made contingent on texture element orientation, and, in addition to these contingent aftereffects, we will report the results of an experiment in which adaptation to intermodal contingencies produced a positive (assimilatory) aftereffect. The reason we will focus on contingent adaptation is that several models of efficient perceptual processing make appeals to analyses of environmental contingencies (e.g. Barlow and Foldiak, 1989; Helson, 1964). It has been our goal to artificially manipulate various environmental contingencies and to examine the effects of these manipulations on the perceptual experience of visual texture. We will argue that the visual perception of texture draws upon experience in a variety of ways that are both surprising and are perhaps achieved very simply. In the final section of the article, we will evaluate several theoretical models of contingent adaptation and compare contingent adaptation to other forms of visual learning. Having elucidated the nature of density aftereffects and demonstrated a variety of contingent density aftereffects, we will then try to demonstrate that there are common principles of efficient information representation pertaining to the representation of visual texture and to its adaptation. 2/19/10 9:11 AM Texture Density Adaptation Page 3 of 55 http://www.swarthmore.edu/SocSci/fdurgin1/PerceptualLearning/PerLearn.html I. The Perception of Texture Density: Studies in Adaptation. It has been recognized for some time that spatial-frequency (Fourier) analysis captures important aspects of the human visual system's analysis of texture. However, one non-Fourier spatial property of textures, element density, has been recently shown, by its adaptation, to also be implicated in the perceptual representation of texture (Durgin and Proffitt, 1991; Durgin, 1995a, 1995b). Prior demonstrations of texture density adaptation had been based on texture magnification (e.g. MacKay, 1964; Walker, 1966) and attributed to spatial-frequency channel adaptation (Anstis, 1974), but the same effects can be observed with stimuli in which both luminance and spatial frequency are kept constant. In Fig. 1 is shown the earlier version of the texture density aftereffect which can be understood as a spatialfrequency aftereffect in the perception of texture (Anstis, 1974). Adaptation to the pair of textures in the left panel (while gazing at the central fixation mark for several seconds) will produce a negative aftereffect which can be experienced by rapidly switching to the right panel; because the upper region is denser in the left panel, the upper region will probably seem less dense than the lower region. By magnifying textures, average luminance was held constant but element size was varied. Figure 1. Demonstration of texture size/density aftereffect (after Anstis, 1974). To experience the aftereffect, gaze at the left fixation mark for several seconds (20-60), moving eyes along fixation bar to avoid the formation of afterimages. Then quickly shift gaze to right fixation square and compare the apparent density of the two right-hand textures. The lower texture should now appear less dense (coarser) than the upper texture. Note that in this demonstration, density is manipulated by texture magnification and, as a result, is confounded with size. Anstis (1974) argued that density aftereffects were due, in part, to 2/19/10 9:11 AM Texture Density Adaptation Page 4 of 55 http://www.swarthmore.edu/SocSci/fdurgin1/PerceptualLearning/PerLearn.html spatial frequency adaptation. Figure 2. Demonstration of texture density aftereffect without size confound (after Durgin and Proffitt, 1991). To experience aftereffect, follow procedure as in Fig. 1. After adapting to the left pair, the upper texture on the right should appear denser than the lower one. Note that in this demonstration, density is not confounded with element size or spatial frequency because element size is held constant. Although density is confounded with luminance, this can be remedied by the use of luminance-balanced texture elements. (See Fig. 3). Figure 2 illustrates a related aftereffect in which density is varied without varying element size (Durgin and Proffitt, 1991). Following the same procedure as for Fig. 1, an aftereffect of apparent texture density can be observed in this condition as well, though the Fourier transforms of the two adaptation fields have similar spectral properties. In this case there are obviously space-average luminance differences between the two fields, but these are probably of no consequence. Although Mulligan and MacLeod (1988) and Cornelissen and Kooijman (1994) have suggested that density and brightness modulations (with unbalanced dots) may in part be interchangeable (cf., also Chubb, Sperling, and Solomon, 1989), Durgin (1995a; Durgin and Hammer, 1994) has found that texture brightness aftereffects (from contrast adaptation) and texture density aftereffects show little inter-dimension transfer and have different patterns of interocular transfer (cf. also Burgess and Barlow, 1983). 2/19/10 9:11 AM Texture Density Adaptation Page 5 of 55 http://www.swarthmore.edu/SocSci/fdurgin1/PerceptualLearning/PerLearn.html Figure 3. Schematic illustration of pixel structure of a balanced dot (left) and a representation of a dense balanced dot texture (right). In left image, theoretical luminance values for individual pixels are meant to illustrate the fact that the space-average luminance of the dot (L) is maintained in the dot, though it is a high-contrast stimulus. A small patch of dense texture is shown at right (337 dots are shown). In order to demonstrate that the effect in Fig. 2 could be obtained even when luminance differences were not present, we have created dot-textures composed of balanced-dot elements. Figure 3 depicts both the pixel structure of a single square balanced dot and a representative illustration of a balanced dot texture. Round balanced dots were originally used by Carlson, Moeller and Anderson (1984) to investigate geometrical illusions of size in the absence of low-spatial frequencies. Although non-linearities in visual processing (such as logarithmic transformations of input luminances at the retina) can render ostensibly balanced dots unbalanced (cf. Garcia-Perez, 1991), they represent a fairly well-filtered stimulus, and have been used effectively in investigations of motion processing (Gilden, Bertenthal and Othman, 1990). Overview of simple density aftereffect experiments. The three experiments to be reported concerning simple texture density adaptation are previously unpublished(Note 1) demonstrations that the texture density aftereffect is obtainable with balanced-dot textures. The first experiment we report here assesses the magnitude of the aftereffect both when observers were readapted immediately before each measurement trial and, following this, when they were retested without further readaptation. The magnitude of the effect, which will be shown to approximate a proportional reduction in perceived density, is striking in both conditions. In the second experiment the contribution of peripheral factors to the short-term density aftereffect is investigated by dichoptic presentation of the adaptation and testing stimuli. The effects of very short-term adaptation do not show complete interocular transfer, though previous evidence (Durgin, 1992) suggests that the longer-term aftereffects do. In a third experiment the retinotopicity of the effect is investigated. In order to test a classical conditioning hypothesis of the effect we dissociated screen position and retinal position in order to show that retinal, not screen, position determined the presence of the aftereffect. 2/19/10 9:11 AM Texture Density Adaptation Page 6 of 55 http://www.swarthmore.edu/SocSci/fdurgin1/PerceptualLearning/PerLearn.html Because the same basic methodology for inducing and testing visual aftereffects of texture density will be used in all of the studies in this section and others that follow, an outline of our general method is provided here.