A generalized equation for the calculation of receptor noise limited colour distances in<i>n</i>-chromatic visual systems

Clark, R. C.; Santer, Roger D.; Brebner, J. S.

doi:10.1098/rsos.170712

Cited by 15 publications

(15 citation statements)

References 28 publications

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“…In the original study, Vorobyev and Osorio () provided a method to calculate chromaticity distances (Δ S ) independently of the matrix V , and their method is already applicable to any number of photoreceptor types (see also Clark et al. to another model extension). However, within Vorobyev and Osorio () formulation, it is possible to find a space representing RNL model color space in terms units of receptor noise (see for instance Renoult et al., and Pike, ,).…”

Section: A Generic Methods For N‐dimensional Modelsmentioning

confidence: 99%

Color vision models: Some simulations, a general n‐dimensional model, and the colourvision R package

Gawryszewski

2018

Ecology and Evolution

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The development of color vision models has allowed the appraisal of color vision independent of the human experience. These models are now widely used in ecology and evolution studies. However, in common scenarios of color measurement, color vision models may generate spurious results. Here I present a guide to color vision modeling (Chittka (1992, Journal of Comparative Physiology A, 170, 545) color hexagon, Endler & Mielke (2005, Journal Of The Linnean Society, 86, 405) model, and the linear and log‐linear receptor noise limited models (Vorobyev & Osorio 1998, Proceedings of the Royal Society B, 265, 351; Vorobyev et al. 1998, Journal of Comparative Physiology A, 183, 621)) using a series of simulations, present a unified framework that extends and generalize current models, and provide an R package to facilitate the use of color vision models. When the specific requirements of each model are met, between‐model results are qualitatively and quantitatively similar. However, under many common scenarios of color measurements, models may generate spurious values. For instance, models that log‐transform data and use relative photoreceptor outputs are prone to generate spurious outputs when the stimulus photon catch is smaller than the background photon catch; and models may generate unrealistic predictions when the background is chromatic (e.g. leaf reflectance) and the stimulus is an achromatic low reflectance spectrum. Nonetheless, despite differences, all three models are founded on a similar set of assumptions. Based on that, I provide a new formulation that accommodates and extends models to any number of photoreceptor types, offers flexibility to build user‐defined models, and allows users to easily adjust chromaticity diagram sizes to account for changes when using different number of photoreceptors.

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Section: A Generic Methods For N‐dimensional Modelsmentioning

confidence: 99%

Color vision models: Some simulations, a general n‐dimensional model, and the colourvision R package

Gawryszewski

2018

Ecology and Evolution

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“…Distances calculated in this manner correspond to the Mahalanobis Distance DM , and represent distances between points standardized by the Weber fraction; i.e. (Clark et al ., 2017). It follows that values lower than 1 JND are predicted to be indistinguishable, while values greatly above this threshold are likely distinct.…”

Section: Introductionmentioning

confidence: 99%

Comparing colours using visual models

Maia

White

2017

Preprint

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Lay Summary 13An outstanding challenge for the study of colour traits is how best to use "colour 14 spaces" to represent their visual perception, particularly when asking questions 15 of colour-difference (e.g. the (dis)similarity of males and females, mimics and 16 models, or sister species, to a given viewer). We use simulations to show that 17 existing methods fail to statistically and biologically estimate the separation of 18 groups in colour space, and we suggest a flexible, robust, alternative that avoids 19 those pitfalls. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/175992 doi: bioRxiv preprint first posted online Aug. 18, 2017; Abstract 21Colour in nature presents a striking dimension of variation, though understanding 22 its function and evolution largely depends on our ability to capture the perspec- (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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“…receptor-noise corrected) space by calling on the distances calculated in (de Ibarra et al ., 2001; Pike, 2012). Notably, these functions now accept n-dimensional data (derived independently, but see Clark et al ., 2017; Gawryszewski, 2018, for valuable discussion). This allows for the modelling of extreme (Chen et al ., 2016; Cronin & Marshall, 1989, though given the lack of support for traditional opponency in these systems, the RN model may be of limited use here) or entirely hypothetical visual systems.…”

Section: The Package Versionmentioning

confidence: 99%

pavo 2: new tools for the spectral and spatial analysis of colour in R

Maia

Gruson

Endler

et al. 2018

Preprint

104

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Biological colouration presents a canvas for the study of ecological and evolutionary processes. Enduring interest in colour-based phenotypes has driven, and been driven by, improved techniques for quantifying colour patterns in ever-more relevant ways, yet the need for flexible, open frameworks for data processing and analysis persists.Here we introduce pavo 2, the latest iteration of the R package pavo. This release represents the extensive refinement and expansion of existing methods, as well as a suite of new tools for the cohesive analysis of the spectral and (now) spatial structure of colour patterns and perception. At its core, the package retains a broad focus on (a) the organisation and processing of spectral and spatial data, and tools for the alternating (b) visualisation, and (c) analysis of data. Significantly, pavo 2 introduces image-analysis capabilities, providing a cohesive workflow for the comprehensive analysis of colour patterns.We demonstrate the utility of pavo with a brief example centred on mimicry in Heliconius butterflies. Drawing on visual modelling, adjacency, and boundary strength analyses, we show that the combined spectral (colour and luminance) and spatial (pattern element distribution and boundary salience) features of putative models and mimics are closely aligned.pavo 2 offers a flexible and reproducible environment for the analysis of colour, with renewed potential to assist researchers in answering fundamental questions in sensory ecology and evolution.

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A generalized equation for the calculation of receptor noise limited colour distances inn-chromatic visual systems

Cited by 15 publications

References 28 publications

Color vision models: Some simulations, a general n‐dimensional model, and the colourvision R package

Color vision models: Some simulations, a general n‐dimensional model, and the colourvision R package

Comparing colours using visual models

pavo 2: new tools for the spectral and spatial analysis of colour in R

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