We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Aubert and Foerster (1857) are frequently cited for having shown that the lower visual acuity of peripheral vision can be compensated for by increasing stimulus size. This result is seemingly consistent with the concept of cortical magnification, and it has beeReonfirmed by many subsequent authors. Yet it is rarely noted that Aubert and Foerster also observed a loss of the "quality of form." We have studied the recognition of numeric characters in foveal and eccentric vision by determining the contrast required for 67% correct identification. At each eccentricity, the lowest contrast threshold is achieved with a specific stimulus size. But the contrast thresholds for these optimal stimuli are not independent of retinal eccentricity as cortical magnification scaling would predict. With high-contrast targets, however, threshold target sizes were consistent with cortical magnification out to 6°eccentricity. Beyond 6°,threshold target sizes were larger than cortical magnification predicted. We also investigated recognition performance in the presence of neighboring characters (crowding phenomenon). Target character size, distance of flanking characters, and precision of focusing of attention all affect recognition. The influence of these parameters is different in the fovea and in the periphery. Our findings confirm Aubert and Foerster's original observation of a qualitative difference betweenfoveal and periphe.raivision.It has been known for more than 130 years that under photopic conditions visual performance is lower in peripheral vision than it is in foveal vision (Aubert & Foerster, 1857;Wertheim, 1894). There are two ways of specifying visual performance: to specify the stimulus properties necessary to achieve a certain level of performance, and to specify the level of performance reached with fixed stimuli. Specifications of the first type include the stimulus luminance, contrast, spatial frequency, or size. Specifications of the second type include percent correct, d', and reaction time. Threshold measurements are of the first type, since they are expressed in terms of some stimulus property required for constant performance.Alphanumeric characters have been used in many studies of visual resolution, and visual acuity may be defined in terms of the smallest character that can be identified with a specified level of accuracy. But to use the size of a

A constrained generalized maximum likelihood routine for fitting psychometric functions is proposed, which determines optimum values for the complete parameter set--that is, threshold and slope--as well as for guessing and lapsing probability. The constraints are realized by Bayesian prior distributions for each of these parameters. The fit itself results from maximizing the posterior distribution of the parameter values by a multidimensional simplex method. We present results from extensive Monte Carlo simulations by which we can approximate bias and variability of the estimated parameters of simulated psychometric functions. Furthermore, we have tested the routine with data gathered in real sessions of psychophysical experimenting.

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The loss of positional information for whole letters is one of the most important factors contributing to impaired letter and word recognition. Here we study the quantitative characteristics of flanker confusions in a crowding paradigm and test whether transient spatial attention relieves the crowding effect by reducing flanker confusions. We examined the crowding effect at three eccentricities for a range of flanker distances and attentional cue sizes. The effects of flanker distance confirm earlier findings that errors of both content and position are highest with flankers close by. However, the cue has no effect on flanker confusions and affects content information only, by enhancing target contrast sensitivity independent of cue size. Confusions with the inward, but not the outward, flanker increase linearly with eccentricity. Inward-flanker confusions dominate unlike reported asymmetries for masking. Our results are a psychophysical counterpart to separate neural coding of what and where in pattern recognition. The dependencies of cue effect and confusions on flanker distance scale with eccentricity and can be described by a generalized Bouma critical-separation rule. That rule shows a formal analogy to M scaling, from which the critical crowding distances on a cortical map can be derived as a logarithmic function. The perceptual results are visualized in a "doughnut" model.