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.

Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models.Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed EC SF ) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the centersurround inhibition windows have been adjusted by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.

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.