Visual perception can be influenced by top-down processes related to the observer's goals and expectations, as well as by bottom-up processes related to low-level stimulus attributes, such as luminance, contrast, and spatial frequency. When using different physical stimuli across psychological conditions, one faces the problem of disentangling the contributions of low- and high-level factors. Here, we make available the SHINE (spectrum, histogram, and intensity normalization and equalization) toolbox for MATLAB, which we have found useful for controlling a number of image properties separately or simultaneously. The toolbox features functions for specifying the (rotational average of the) Fourier amplitude spectra, for normalizing and scaling mean luminance and contrast, and for exact histogram specification optimized for perceptual visual quality. SHINE can thus be employed for parametrically modifying a number of image properties or for equating them across stimuli to minimize potential low-level confounds in studies on higher level processes.

BackgroundFace processing, amongst many basic visual skills, is thought to be invariant across all humans. From as early as 1965, studies of eye movements have consistently revealed a systematic triangular sequence of fixations over the eyes and the mouth, suggesting that faces elicit a universal, biologically-determined information extraction pattern.Methodology/Principal FindingsHere we monitored the eye movements of Western Caucasian and East Asian observers while they learned, recognized, and categorized by race Western Caucasian and East Asian faces. Western Caucasian observers reproduced a scattered triangular pattern of fixations for faces of both races and across tasks. Contrary to intuition, East Asian observers focused more on the central region of the face.Conclusions/SignificanceThese results demonstrate that face processing can no longer be considered as arising from a universal series of perceptual events. The strategy employed to extract visual information from faces differs across cultures.

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The determination of the visual features mediating letter identification has a long-standing history in cognitive science. Researchers have proposed many sets of letter features as important for letter identification, but no such sets have yet been derived directly from empirical data. In the study reported here, we applied the Bubbles technique to reveal directly which areas at five different spatial scales are efficient for the identification of lowercase and uppercase Arial letters. We provide the first empirical evidence that line terminations are the most important features for letter identification. We propose that these small features, represented at several spatial scales, help readers to discriminate among visually similar letters.