In understanding how visual signals function, quantifying the components of those patterns is vital. With the ever‐increasing power and availability of digital photography, many studies are utilizing this technique to study the content of animal colour signals. Digital photography has many advantages over other techniques, such as spectrometry, for measuring chromatic information, particularly in terms of the speed of data acquisition and its relatively cheap cost. Not only do digital photographs provide a method of quantifying the chromatic and achromatic content of spatially complex markings, but also they can be incorporated into powerful models of animal vision. Unfortunately, many studies utilizing digital photography appear to be unaware of several crucial issues involved in the acquisition of images, notably the nonlinearity of many cameras’ responses to light intensity, and biases in a camera’s processing of the images towards particular wavebands. In the present study, we set out step‐by‐step guidelines for the use of digital photography to obtain accurate data, either independent of any particular visual system (such as reflection values), or for particular models of nonhuman visual processing (such as that of a passerine bird). These guidelines include how to: (1) linearize the camera’s response to changes in light intensity; (2) equalize the different colour channels to obtain reflectance information; and (3) produce a mapping from camera colour space to that of another colour space (such as photon catches for the cone types of a specific animal species). © 2007 The Linnean Society of London, Biological Journal of the Linnean Society, 2007, 90, 211–237.

Effective camouflage renders a target indistinguishable from irrelevant background objects. Two interrelated but logically distinct mechanisms for this are background pattern matching (crypsis) and disruptive coloration: in the former, the animal's colours are a random sample of the background; in the latter, bold contrasting colours on the animal's periphery break up its outline. The latter has long been proposed as an explanation for some apparently conspicuous coloration in animals, and is standard textbook material. Surprisingly, only one quantitative test of the theory exists, and one experimental test of its effectiveness against non-human predators. Here we test two key predictions: that patterns on the body's outline should be particularly effective in promoting concealment and that highly contrasting colours should enhance this disruptive effect. Artificial moth-like targets were exposed to bird predation in the field, with the experimental colour patterns on the 'wings' and a dead mealworm as the edible 'body'. Survival analysis supported the predictions, indicating that disruptive coloration is an effective means of camouflage, above and beyond background pattern matching.

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The human visual system shows a relatively greater response to low spatial frequencies of chromatic spatial modulation than to luminance spatial modulation. However, previous work has shown that this differential sensitivity to low spatial frequencies is not reflected in any differential luminance and chromatic content of general natural scenes. This is contrary to the prevailing assumption that the spatial properties of human vision ought to reflect the structure of natural scenes. Now, colorimetric measures of scenes suggest that red-green (and perhaps blue-yellow) color discrimination in primates is particularly suited to the encoding of specific scenes: reddish or yellowish objects on a background of leaves. We therefore ask whether the spatial, as well as chromatic, properties of such scenes are matched to the different spatial-encoding properties of color and luminance modulation in human vision. We show that the spatiochromatic properties of a wide class of scenes, which contain reddish objects (e.g., fruit) on a background of leaves, correspond well to the properties of the red-green (but not blue-yellow) systems in human vision, at viewing distances commensurate with typical grasping distance. This implies that the red-green system is particularly suited to encoding both the spatial and the chromatic structure of such scenes.

For many, colours convey affective meaning. Popular opinion assumes that perception of colour is crucial to influence emotions. However, scientific studies test colour-emotion relationships by presenting colours as patches or terms. When using patches, researchers put great effort into colour presentation. When using terms, researchers have much less control over the colour participants think of. In this between-subjects study, we tested whether emotion associations with colour differ between terms and patches. Participants associated 20 emotion concepts, loading on valence, arousal, and power dimensions, with 12 colours presented as patches (n ¼ 54) or terms (n ¼ 78). We report high similarity in the pattern of associations of specific emotion concepts with terms and patches (r ¼.82), for all colours except purple (r ¼.À23). We also observed differences for black, which is associated with more negative emotions and of higher intensity when presented as a term than a patch. Terms and patches differed little in terms of valence, arousal, and power dimensions. Thus, results from studies on colour-emotion relationships using colour terms or patches should be largely comparable. It is possible that emotions are associated with colour concepts rather than particular perceptions or words of colour.

The spatial filtering applied by the human visual system appears to be low-pass for chromatic stimuli and band-pass for luminance stimuli. Here we explore whether this observed difference in contrast sensitivity reflects a real difference in the components of chrominance and luminance in natural scenes. For this purpose a digital set of 29 hyper-spectral images of natural scenes has been acquired and its spatial frequency content analyzed in terms of chrominance and luminance defined according to existing models of the human cone responses and visual signal processing. The statistical 1/f amplitude spatial frequency distribution is confirmed for a variety of chromatic conditions across the visible spectrum. Our analysis suggests that natural scenes are relatively rich in high spatial-frequency chrominance information which does not appear to be transmitted by the human visual system. This result is unlikely to have arisen from errors in the original measurements. Several reasons may combine to explain a failure to transmit high spatial-frequency chrominance: (a) its minor importance for primate visual tasks, (b) its removal by filtering applied to compensate for chromatic aberration of the eye's optics, or (c) a biological bottleneck blocking its transmission. In addition, we graphically compare the ratios of luminance to chrominance measured by our hyperspectral camera and those measured psychophysically over an equivalent spatial frequency range.1

A fundamental tenet of visual science is that the detailed properties of visual systems are not capricious accidents, but are closely matched by evolution and neonatal experience to the environments and lifestyles in which those visual systems must work. This has been shown most convincingly for fish and insects. For mammalian vision, however, this tenet is based more upon theoretical arguments than upon direct observations. Here, we describe experiments that require human observers to discriminate between pictures of slightly different faces or objects. These are produced by a morphing technique that allows small, quantifiable changes to be made in the stimulus images. The independent variable is designed to give increasing deviation from natural visual scenes, and is a measure of the Fourier composition of the image (its second-order statistics). Performance in these tests was best when the pictures had natural second-order spatial statistics, and degraded when the images were made less natural. Furthermore, performance can be explained with a simple model of contrast coding, based upon the properties of simple cells in the mammalian visual cortex. The findings thus provide direct empirical support for the notion that human spatial vision is optimised to the second-order statistics of the optical environment.

Illumination varies greatly both across parts of a natural scene and as a function of time, whereas the spectral reflectance function of surfaces remains more stable and is of much greater relevance when searching for specific targets. This study investigates the functional properties of postreceptoral opponent-channel responses, in particular regarding their stability against spatial and temporal variation in illumination. We studied images of natural scenes obtained in UK and Uganda with digital cameras calibrated to produce estimated L-, M-, and S-cone responses of trichromatic primates (human) and birds (starling). For both primates and birds we calculated luminance and red-green opponent (RG) responses. We also calculated a primate blue-yellow-opponent (BY) response. The BY response varies with changes in illumination, both across time and across the image, rendering this factor less invariant. The RG response is much more stable than the BY response across such changes in illumination for primates, less so for birds. These differences between species are due to the greater separation of bird L and M cones in wavelength and the narrower bandwidth of the cone action spectra. This greater separation also produces a larger chromatic signal for a given change in spectral reflectance. Thus bird vision seems to suffer a greater degree of spatiotemporal "clutter" than primate vision, but also enhances differences between targets and background. Therefore, there may be a trade-off between the degree of chromatic clutter in a visual system versus the degree of chromatic difference between a target and its background. Primate and bird visual systems have found different solutions to this trade-off.