The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.

Several domains of neuroscience offer map-like models that link location on the cortical surface to properties of sensory representation. Within cortical visual areas V1, V2, and V3, algebraic transformations can relate position in the visual field to the retinotopic representation on the flattened cortical sheet. A limit to the practical application of this structure-function model is that the cortex, while topologically a two-dimensional surface, is curved. Flattening of the curved surface to a plane unavoidably introduces local geometric distortions that are not accounted for in idealized models. Here, we show that this limitation is overcome by correcting the geometric distortion induced by cortical flattening. We use a mass-spring-damper simulation to create a registration between functional MRI retinotopic mapping data of visual areas V1, V2, and V3 and an algebraic model of retinotopy. This registration is then applied to the flattened cortical surface anatomy to create an anatomical template that is linked to the algebraic retinotopic model. This registered cortical template can be used to accurately predict the location and retinotopic organization of these early visual areas from cortical anatomy alone. Moreover, we show that prediction accuracy remains when extrapolating beyond the range of data used to inform the model, indicating that the registration reflects the retinotopic organization of visual cortex. We provide code for the mass-spring-damper technique, which has general utility for the registration of cortical structure and function beyond the visual cortex.

The problem of color constancy may be solved if we can recover the physical properties of illuminants and surfaces from photosensor responses. We consider this problem within the framework of Bayesian decision theory. First, we model the relation among illuminants, surfaces, and photosensor responses. Second, we construct prior distributions that describe the probability that particular illuminants and surfaces exist in the world. Given a set of photosensor responses, we can then use Bayes's rule to compute the posterior distribution for the illuminants and the surfaces in the scene. There are two widely used methods for obtaining a single best estimate from a posterior distribution. These are maximum a posteriori (MAP) and minimum mean-square-error (MMSE) estimation. We argue that neither is appropriate for perception problems. We describe a new estimator, which we call the maximum local mass (MLM) estimate, that integrates local probability density. The new method uses an optimality criterion that is appropriate for perception tasks: It finds the most probable approximately correct answer. For the case of low observation noise, we provide an efficient approximation. We develop the MLM estimator for the color-constancy problem in which flat matte surfaces are uniformly illuminated. In simulations we show that the MLM method performs better than the MAP estimator and better than a number of standard color-constancy algorithms. We note conditions under which even the optimal estimator produces poor estimates: when the spectral properties of the surfaces in the scene are biased.

Summary In 1918, Gordon Holmes combined observations of visual field scotomas across brain lesioned soldiers to produce a schematic map of the projection of the visual field upon the striate cortex [1]. One limit to the precision of his result, and the mapping of anatomy to retinotopy generally, is the substantial individual variation in the size [2,3], volumetric position [4], and cortical magnification [5] of area V1. When viewed within the context of the curvature of the cortical surface, however, the boundaries of striate cortex fall at a consistent location across individuals [6]. We asked if the surface topology of the human brain can be used to accurately predict the internal, retinotopic function of striate cortex as well. We used fMRI to measure polar angle and eccentricity in 25 participants and combined their maps within a left-right, transform-symmetric representation of the cortical surface [7]. These data were then fit using a deterministic, algebraic model of visual field representation [8]. We found that an anatomical image alone can be used to predict the retinotopic organization of striate cortex for an individual as accurately as 10–25 minutes of functional mapping. This indicates tight developmental linkage of structure and function within a primary, sensory cortical area.

In the human, cone photoreceptors (L, M, and S) and the melanopsincontaining, intrinsically photosensitive retinal ganglion cells (ipRGCs) are active at daytime light intensities. Signals from cones are combined both additively and in opposition to create the perception of overall light and color. Similar mechanisms seem to be at work in the control of the pupil's response to light. Uncharacterized however, is the relative contribution of melanopsin and S cones, with their overlapping, short-wavelength spectral sensitivities. We measured the response of the human pupil to the separate stimulation of the cones and melanopsin at a range of temporal frequencies under photopic conditions. The S-cone and melanopsin photoreceptor channels were found to be low-pass, in contrast to a band-pass response of the pupil to L-and M-cone signals. An examination of the phase relationships of the evoked responses revealed that melanopsin signals add with signals from L and M cones but are opposed by signals from S cones in control of the pupil. The opposition of the S cones is revealed in a seemingly paradoxical dilation of the pupil to greater S-cone photon capture. This surprising result is explained by the neurophysiological properties of ipRGCs found in animal studies. Distinct neural pathways process signals originating in cone photoreceptors for visual perception. Luminance pathways combine signals from separate classes of cones synergistically, providing a spectrally broadband indication of the overall light intensity at each location in the retinal image. Red-green and blue-yellow chromatic channels combine signals from separate classes of cones in an opponent (subtractive) fashion, providing sensitivity to the relative spectral content of light and supporting the perception of color independent of luminance (1).A parallel set of pathways contributes to the response of the pupil of the eye to light. Most familiar is a synergistic cone effect that causes the pupil to constrict in response to increased luminance. Illustrating a commonality of principles that characterize neural mechanisms for perception and pupil response, rectified signals from red-green and blue-yellow opponent channels also contribute to the pupil's light response (2-7).Recently, it has been discovered that mammalian retinas contain an additional photoreceptor class that also operates under daylight conditions. Intrinsically photosensitive retinal ganglion cells (ipRGCs) express the photopigment melanopsin, which has a peak spectral sensitivity (480 nm) between that of S and M cones (8, 9). Among other, "non-image-forming" functions of the eye, melanopsin-containing ipRGCs contribute to a delayed and sustained constriction of the pupil (10). Studies in patients with loss of photoreceptor function (11) suggest that melanopsin may also contribute to conscious visual perception.The discovery of an additional photoreceptor class raises the fundamental question of how melanopsin signals are combined with those from the cones. Do melanopsin signals add to con...