S. Sabina Wolfson

Examining edge- and region-based texture analysis mechanisms

Instantaneous texture discrimination performance was examined for different texture stimuli to uncover the use of edge-based and region-based texture analysis mechanisms. Textures were composed of randomly placed, short, oriented line segments. Line segment orientation was chosen randomly using a Gaussian distribution (described by a mean and a standard deviation). One such distribution determined the orientations on the left side of the image, and a second distribution was used for the right side. The two textures either abutted to form an edge or were separated by a blank region. A texture difference in mean orientation led to superior discrimination performance when the textures abutted. On the other hand, when the textures differed in the standard deviation of the orientation distribution, performance was similar in the two conditions. These results suggest that edge-based texture analysis mechanisms were used (i.e. were the most sensitive) in the abutting difference-in-mean case, but region-based texture analysis mechanisms were used in the other three cases.

Long range interactions between oriented texture elements.

Long range interactions between texture elements (short, oriented line segments) were examined. Specifically, we studied the influence of a background array of texture elements on the detectability of a target element (separated from the background by an intermediate textured region) using textures like those of Caputo (Vis. Res. 1996, 36, 2815-2826). We found that, in general, when the background elements were oriented orthogonally to the target element, detection of the target element was better than when the background elements had the same orientation as the target element. We discuss these interactions in terms of inhibitory and excitatory connections between orientation and spatial frequency selective linear filters (e.g. filters which mimic V1 simple cells) which would respond to the individual texture elements.

Contrast–response functions for multifocal visual evoked potentials: A test of a model relating V1 activity to multifocal visual evoked potentials activity

The multifocal visual evoked potential (mfVEP) is largely generated in V1. To relate the electrical activity recorded from humans to recordings from single cells in nonhuman primate (V1) cortex, contrast-response functions for the human mfVEP were compared to predictions from a model of V1 activity (D. J. Heeger, A. C. Huk, W. S. Geisler, & D. G. Albrecht, 2000) based upon single-cell recordings from monkey V1 (e.g., D. G. Albrecht, 1995; D. G. Albrecht, W. S. Geisler, R. A. Frazor, & A. M. Crane, 2002; D. G. Albrecht & D. B. Hamilton, 1982; W. S. Geisler & D. G. Albrecht, 1997). A second purpose was to fully articulate the assumptions of this model to better understand the implications of this comparison. Finally, as the third purpose, one of these assumptions was tested. Monocular mfVEPs were obtained from normal subjects with a contrast-reversing dartboard pattern. The display contained 16 sectors each with a checkerboard. Both the sectors and the checks were scaled approximately for cortical magnification. In Experiment 1, there were 64 checks per sector. The contrast-response functions were fitted well up to 40% contrast by the theoretical population curve for V1 neurons; there was a systematic deviation for higher contrasts. The model, as articulated here, predicts that the contrast-response function should be the same and independent of the size of the elements in the display. Varying the size of the elements by varying the viewing distance in Experiment 2 produced similar results to those in Experiment 1. In Experiment 3, the viewing distance and sector size were held constant, but the size of the elements (and therefore the number of checks per sector) was varied. Changing check size by a factor of 16 had relatively little effect on the contrast-response function. In general, the mfVEP results were consistent with the model based upon the V1 neuron population. However, two aspects of the results require further exploration. First, there was a systematic deviation from the models contrast-response function for higher contrasts. This deviation suggests that one or more of the models assumptions may be violated. Second, the latency of the mfVEP changed far less than expected based upon single-cell data.

Exploring the dynamics of light adaptation: the effects of varying the flickering background's duration in the probed-sinewave paradigm

In the probed-sinewave paradigm, threshold for detecting a probe is measured at various phases with respect to a sinusoidally-flickering background. Here we vary the duration of the flickering background before (and after) the test probe is presented. The adaptation is rapid; after approximately 10-30 ms of the flickering background, probe threshold is the same as that on a continually-flickering background. It is interesting that this result holds at both low (1. 2 Hz) and middle (9.4 Hz) frequencies because at middle frequencies (but not at low) there is a dc-shift, i.e. probe threshold is elevated at all phases relative to that on a steady background (of the same mean luminance). We compare our results to predictions from Wilsons model [Wilson (1997), Visual Neuroscience, 14, 403-423; Hood & Graham (1998), Visual Neuroscience, 15, 957-967] of light adaptation. The model predicts the rapid adaptation, and the dc-shift, but not the detailed shape of the probe-threshold-versus-phase curve at middle frequencies.

Comparing increment and decrement probes in the probed-sinewave paradigm

Using the probed-sinewave paradigm, we explore the differences between increment and decrement probes across a range of frequencies (approx. 1-19 Hz). In this paradigm, detection threshold is measured for a small test probe presented on a large sinusoidally flickering background (at eight different phases). Probe thresholds are very similar for increment and decrement probes, but there is a very small (and systematic) difference: increment thresholds are usually slightly higher relative to decrement thresholds during the part of the cycle when the backgrounds intensity is increasing. Although Wilsons (1997, Vis. Neuro., 14, 403-423) model substantially underestimates the size of this difference, it predicts some phase dependency. However, the existence of On- and Off-pathways in Wilsons model is not important for these predictions. A recent model by Snippe, Poot, and van Hateren (2000, Vis. Neuro., 17, 449-462) may be able to predict this result by using explicit contrast-gain control rather than separate On- and Off-pathways. Auxiliary experiments measuring the perceived polarity of the probe provide a further argument suggesting that separate On- and Off-pathways are not useful in explaining increment and decrement probe thresholds.

An unusual kind of contrast adaptation: Shifting a contrast comparison level

We have found an unusual kind of contrast adaptation in human pattern vision that seems fundamentally different from previously reported effects. As the observer adapts to different levels of contrast, the visibility of some contrast-defined (second-order) patterns dramatically increases and that of others dramatically decreases. Oddly, visibility is poor for patterns containing contrasts both above and below the recent average contrast. To explain these effects, we hypothesize a new kind of process acting in concert with a known contrast-gain control of the normalization type. The new process compares current contrast to an adaptable comparison level; this level reflects the recent average contrast. Such a process existing at an early stage of visual processing is likely to have widespread effects at higher stages.

Forty-four years of studying light adaptation using the probed-sinewave paradigm.

Here we examine results from 44 years of probed-sinewave experiments investigating the dynamics of light adaptation. We also briefly examine four models that have been tested against the results. In these experiments, detection threshold is measured for a test stimulus superimposed at various times (phases) on a sinusoidally flickering homogeneous background. The results can be plotted as probe-threshold versus phase curves. Overall, the curves from different laboratories are remarkably similar given the substantial differences in experimental parameters. However, at medium frequencies of background flicker, there are some differences between the majority of the studies and a minority of two. An examination of the full set of results suggests that the differences are not as significant as they first appear and that the experimental condition leading to the differences is the use of long wavelength light in the two minority studies. Of the four models that have been tested, two fail to predict important features of the results, another is critically dependent on a mechanism unlikely to exist in the appropriate physiology, and the last seems quite promising.

Processing in the probed-sinewave paradigm is likely retinal

In the probed-sinewave paradigm-used to study the dynamics of light adaptation-a small probe of light is superimposed on a sinusoidally flickering background. Detection threshold for the probe is measured at various times with respect to the flickering background. Here we present such stimuli using three methods: monoptic (the probe and the flickering background are presented to the same eye), dichoptic (the probe is presented to one eye and the flickering background is presented to the other eye), and binocular (the probe and the flickering background are both presented to both eyes). The results suggest that the processing associated with detecting the probe is primarily in the retina (or any place with monocular input). However, the results also suggest a slight amount of processing in the cortex (or any place with binocular input), particularly at the higher frequency of flickering background used here (9.4 Hz vs. 1.2 Hz). A simple schematic model with three ocular-dominance channels is consistent with the results.

Element-arrangement textures in multiple objective tasks

In his long years of studying visual perception, Jacob Beck made many contributions. This article is a short review of one line of his research--that we shared in--and then a presentation of some results from on-going research down the same line. In the 1980s Beck and his colleagues introduced a new kind of visual stimulus: element-arrangement texture patterns. A series of studies with these patterns has shown that a model containing spatial-frequency and orientation-selective channels can explain many aspects of texture perception as long as two kinds of nonlinear processes are also included; the published studies are briefly summarized. The new results come from multiple objective tasks requiring the observer to make simple discriminations between second-order element-arrangement textures. Results with the objective tasks replicate previously published results using subjective ratings, and the use of the objective tasks allows us to explore several more fine-grained questions about complex (second-order) channels and normalization.

Two visual contrast processes: One new, one old.

In everyday life, we occasionally look at blank, un-textured regions of the world around us, a blue unclouded sky for example. But most of the time our eyes see regions occupied by spatial patterning ‐ by texture, form or patterns -- as when looking at a person to whom we are talking or at the text on this page. Further, there is constant temporal change as well as spatial patterning ‐ if only as a result of eye movements. Thus, the eye is usually looking at a visual scene where different parts of the scene are characterized by different levels of visual contrast, and, from moment to moment, the contrast at any point on the retina is changing. (Visual contrast in any region of the scene is the difference between the lightest and darkest parts of that region, relative to some measure of overall intensity in that region.) So one might wonder how the spatial patterning an observer has just seen affects the visual processing of the spatial patterning that an observer sees now. And, more specifically, one might wonder how the visual contrast one has just seen in a region affects the processing of visual contrast there now. The first part of this chapter is about an effect of contrast adaptation discovered rather recently, nicknamed Buffy adaptation. (For the origin of the nickname, see Graham and Wolfson, 2007. We are using the term adaptation here only to mean the effect of preceding contrast on the processing of subsequent visual contrast. Our procedure, which will be described in Fig. 1, might also be called masking or a procedure to study temporal processing.) This recently discovered effect of contrast adaptation dramatically increases the visibility of some contrast-defined patterns and dramatically decreases that of others. The second part of the chapter briefly places this new effect in the context of a previously known effect (called the old effect here), which exhibits more conventional Weber-law-like behavior.