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Why We See What We Do: An Empirical Theory of Vision by Dale Purves and R. Beau Lotto

This book will immediately attract attention because of its ambitious title, the scientific credentials of Dale Purves (an eminent neurobiologist) and for the current need for a new and comprehensive theory of vision. The book is very well written in a clear didactic style and beautifully illustrated with some superb examples of visual illusions, old and new. As the subtitle suggests, the authors advance an empirical theory of vision, suggesting that perception is determined by visual experience. Of course, the idea that the visual system can be modified and calibrated by experience is hardly new, nor indeed controversial. But Purves and Lotto are claiming much more: that ‘‘experience does not merely modulate the appearance of the world’’ but that ‘‘the spatial perceptions elicited by geometric (or any other) stimuli are determined entirely by the statistical relationship between the retinal image and all its possible sources’’ (p. 144, my italics). Indeed, ‘‘work over the last 50 years on the properties of visual neurones and the circuits they form has somehow been headed in the wrong direction, . . . based on the suspect notion that visual circuits are in the business of detecting features, coding stimulus elements and processing them according to a set of rules’’ (p. 11). To illustrate their theory, let us consider a specific but representative example: Mach bands, the bright and dark bands that are seen where luminance ramps meet plateaux. As the authors rightly assert, ‘‘explaining these bands is clearly a necessity for any theory that purports to have rationalized perceptions of luminance’’ (p. 72). Their empirical argument goes something like this. Luminance ramps are often associated with bright and dark bands because of the nature of reflecting surfaces. For example, the luminance profile of the angular surface of a refrigerator door may vary gradually, following Lambert’s law, but if the surface is specular, as many such surfaces are, it will produce clear highlights where the ramp meets the plateaux. Lowlights will also result from shading effects. Other types of ramp luminance profiles, such as cast shadows, do not have associated light and dark bands. The visual system compiles a dossier from all visual experience, from which it computes a probability density function for the range of possible percepts. Future observation of a ramp pattern will trigger a ‘‘reflex’’ response based on the mean of this probability density function, which will be in an attenuated version of the bright and dark bands. I have several difficulties with this whole scheme (far too many to list here). For the bands to be represented in the probability density function, the system has to be capable of sensing them: It must encode the physical highlights and lowlights with photometer precision, although they convey no useful information about object shape or texture. Obviously, if the system is capable of detecting the physical bands, it must also be able of detecting their absence, on shadows, for example. Why then does it lose this capacity to discriminate the presence of bands and group all ramplike stimuli together? Does this mean that at some stage of development, children are more sensitive than adults to subtle luminance changes (akin to their greater capacity to learn languages)? I know of no evidence to suggest this and much that is against it. Although it is not obvious from this book, Mach bands have been studied extensively and quantitatively over many years. For example, they are highly contrastdependent, with a clear and precise threshold below which the bands disappear. The thresholds for white and dark bars are very similar (although the reasons for their physical existence on refrigerators are very different), and they vary systematically with ramp width in a predictable fashion, never occurring for ramps smaller than 4-min arc. Importantly, contrast thresholds for Mach bands (and indeed for most stimuli) vary very little between individuals (regardless of how many refrigerators they were exposed to as babies). How can a probabilistic theory of vision predict all these facts quantitatively (as other theories have done) with such little variation between individuals? What is conspicuously lacking in this book is a quantitative approach: systematic measurements of the effects of relevant variables on subject performance combined with quantitative modeling of how their model fits the data (compared with existing models). On the few occasions that this is attempted, the measured effects are generally consistent with most off-theshelf models of vision and none of the manipulations address specifically the issue of visual experience.