No abstract

A number of researchers have suggested that in order to understand the response properties of cells in the visual pathway, we must consider the statistical structure of the natural environment. In this paper, we focus on one aspect of that structure, namely, the correlational structure which is described by the amplitude or power spectra of natural scenes. We propose that the principle insight one gains from considering the image spectra is in understanding the relative sensitivity of cells tuned to different spatial frequencies. This study employs a model in which the peak sensitivity is constant as a function of frequency with linear bandwith increasing (i.e., approximately constant in octaves). In such a model, the "response magnitude" (i.e., vector length) of cells increases as a function of their optimal (or central) spatial frequency out to about 20 cyc/deg. The result is a code in which the response to natural scenes, whose amplitude spectra typically fall as 1/f, is roughly constant out to 20 cyc/deg. An important consideration in evaluating this model of sensitivity is the fact that natural scenes show considerable variability in their amplitude spectra, with individual scenes showing falloffs which are often steeper or shallower than 1/f. Using a new measure of image structure (the "rectified contrast spectrum" or "RCS") on a set of calibrated natural images, it is shown that a large part of the variability in the spectra is due to differences in the sparseness of local structure at different scales. That is, an image which is "in focus" will have structure (e.g., edges) which has roughly the same magnitude across scale. That is, the loss of high frequency energy in some images is due to the reduction of the number of regions that contain structure rather than the amplitude of that structure. An "in focus" image will have structure (e.g., edges) across scale that have roughly equal magnitude but may vary in the area covered by structure. The slope of the RCS was found to provide a reasonable prediction of physical blur across a variety of scenes in spite of the variability in their amplitude spectra. It was also found to produce a good prediction of perceived blur as judged by human subjects.

We examine interhemispheric cooperation in the recognition of personally known faces whose long-term familiarity ensures frequent co-activation of face-sensitive areas in the right and left brain. Images of self, friend, and stranger faces were presented for 150 ms in upright and inverted orientations both unilaterally, in the right or left visual field, and bilaterally. Consistent with previous research, we find a bilateral advantage for familiar but not for unfamiliar faces, and we demonstrate that this gain occurs for inverted as well as upright faces. We show that friend faces are recognized more quickly than unfamiliar faces in upright but not in inverted orientations, suggesting that configural processing underlies this particular advantage. Novel to this study is the finding that people are faster and more accurate at recognizing their own face over both stranger and friend faces and that these advantages occur for both upright and inverted faces. These findings are consistent with evidence for a bilateral representation of self-faces.

"Contrast constancy" refers to the ability to perceive objects as maintaining a constant contrast independent of size or distance. When tested with high contrast sinusoidal gratings, contrast constancy has been shown to hold for a wide range of spatial frequencies, suggesting that sensitivity is constant across the spectrum at suprathreshold. In this study, we show that contrast constancy also holds for relatively broadband patterns. We describe how the frequency spectra of such functions change as the patterns scale in size. In particular, we emphasize how these changes in the spectra depend on whether the functions are localized (coherent phase) or spatially distributed (incoherent phase). In Fourier terms, the scaling properties depend on the phase spectra of the patterns. Contrast constancy is shown to hold for both localized Gabor patches (coherent phase spectra) and bandpass noise patterns (incoherent phase spectra). Constancy holds over a wide range of suprathreshold contrasts; in fact, matching is quite accurate as soon as the pattern is suprathreshold. These results are explained with a model in which mechanism bandwidths increase with frequency (constant in octaves) and peak spectral sensitivity is equal across frequency out to around 16 c/deg. In the case of the Gabor stimuli, perceived contrast is assumed to be mediated by a mechanism centered on the patch. For the bandpass noise, contrast is determined by the average response of units distributed across the stimulus. This model can account for the matching data without assuming that the contrast-response gain of the underlying channels changes with spatial frequency. Neither does the model assume "response pooling". In addition to explaining the experimental results, the model also predicts that perceived contrast will be approximately constant across scale for scenes whose spectra fall as 1/f, as is typical of natural scenes.

When comparing psychological models a researcher should assess their relative selectivity, scope, and simplicity. The third of these considerations can be measured by the models' parameter counts or equation length, the second by their ability to fit random data, and the first by their differential ability to fit patterned data over random data. These conclusions are based on exploration of integration models reflecting depth judgments. Replication of Massaro's (1988a) results revealed an additive model , and Massaro's fuzzy-logical model of perception (FLMP) fit data equally well, but further exploration showed that the FLMP fit random data better. The FLMP's successes may reflect not its sensitivity in capturing psychological process but its scope in fitting any data and its complexity as measured by equation length.Good scientific theories are usually thought to have several properties: They are accurate, simple, broad in scope, internally consistent, and have the ability to generate new research (Kuhn, 1977). When models can be used to instantiate theories, they might reflect these same properties. For our purposes the two key concepts in this set are simplicity and scope. Simplicity can be measured in several ways. We measure it in two: by the number of parameters in a model and, in a way not customary to experimental psychology, by the length of the equation that instantiates a model. Scope can also be measured in various ways, but here we consider how theory or model accounts for all possible data functions, where those functions are generated by a reasonably large sample of random data sets. Under this construal, broad scope is a mixed blessing. A model with greater scope than another may fit more data functions of interest to the researcher, but simultaneously it may also fit more functions of no interest. Thus, we propose a new criterion for testing and comparing models: selectivity. We define selectivity as the relative ability of a This research was supported by National Science Foundation Grant BNS-8818971 to James E. Cutting. Results without modeling or simulations were reported briefly at the 28th annual meeting of the Psychonomic Society, Seattle, Washington, November 1987.We thank Dominic W. Massaro for helping us understand and implement the fuzzy-logical model of perception; Michael S. Landy and Mark J. Young for insights into implementing other models; James L. McClelland for a general discussion about modeling; William Epstein, James A. Ferwerda, and Mary M. Hayhoe for random discussions related to the topics presented here; Carol L. Krumhansl, Michael S. Landy, Geoffrey R. Loftus, Dominic W. Massaro, and an anonymous reviewer for comments on previous versions of this article; and Nan E. Karwan for sustained interest in and discussions about the project.Correspondence concerning this article should be addressed to James E. Cutting, Department of Psychology, Uris Hall, Cornell University, Ithaca, New York 14853-7601. model to fit data functions of interest with its ability to fit random da...