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

Gawryszewski, Felipe M.

doi:10.1002/ece3.4288

Cited by 32 publications

(37 citation statements)

References 58 publications

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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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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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show abstract

“…), possibilitou o desenvolvimento de modelos de visão para as abelhas. Estes modelos nos permitem inferir a maneira como as abelhas enxergam as cores/padrões das flores (Gawryszewski 2018). Esses modelos são essenciais para, entre outras coisas, determinar se o estímulo utilizado em experimentos de aprendizado e memória é compatível com a cor da flor na natureza.…”

Section: Cores E Fragrâncias: Os Principais Sinais Floraisunclassified

“…Esses modelos são essenciais para, entre outras coisas, determinar se o estímulo utilizado em experimentos de aprendizado e memória é compatível com a cor da flor na natureza. De modo geral, estes modelos necessitam de quatro inputs básicos: a reflectância espectral do objeto que se deseja modelar a cor, as sensibilidades espectrais da espécie da qual se deseja modelar a visão (i.e., de cada tipo de fotorreceptor), a reflectância do plano fundo no qual o objeto está contrastado, e a iluminação do ambiente (Gawryszewski 2018).…”

Section: Cores E Fragrâncias: Os Principais Sinais Floraisunclassified

“…Apesar dos modelos darem uma noção da percepção das cores pelas abelhas, é importante notar que o condicionamento experimental também pode melhorar a discriminação de cores pelas abelhas (Giurfa 2004). Além disso, como cada tipo de modelo visual diverge em suas premissas, o uso de um ou outro modelo deve ser cuidadosamente aplicado e justificado pelo pesquisador (Kemp et al 2015, Telles & Rodríguez-Gironés 2015, Gawryszewski 2018. Portanto, estes modelos servem como uma primeira abordagem das capacidades visuais dos polinizadores.…”

Section: Cores E Fragrâncias: Os Principais Sinais Floraisunclassified

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Como Treinar Sua Abelha: Métodos Aplicados À Biologia Cognitiva Da Polinização

et al. 2020

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Resumo:As abelhas são os principais vetores bióticos da polinização, fenômeno extremamente importante para a manutenção dos sistemas naturais e também para a produção de alimentos. Durante décadas, estudos mostraram como as abelhas são notáveis quanto às suas capacidades cognitivas através de protocolos desenvolvidos para se entender o comportamento, aprendizado e memória desses insetos. O objetivo desta revisão é apresentar as bases dos experimentos de cognição em abelhas, descrevendo protocolos clássicos, novas abordagens e estudos empíricos mostrando como o treinamento de abelhas pode ser aplicado à diferentes questões na biologia da polinização.Palavras-chave: aprendizado; cognição; comportamento; memória; percepção. HOW TO TRAIN YOUR BEE: METHODS APPLIED TO THE COGNITIVE BIOLOGY OF POLLINATION.Bees are the major biotic pollination agents, an extremely important phenomenon to the maintenance of the natural systems and food production. For decades, studies have shown the remarkable cognition system of bees, and several protocols were developed to understand behavior, learning and memory of these insects. The aim of this review is to present the basis of cognitive experiments using bees as a model, describing classical protocols, new approaches and empiric studies showing how bee training can be applied to different questions on pollination biology.

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“…For NaturePatternMatch see Stoddard et al (2014). For Colourvision see Gawryszewski (2018). We would also like to point out an approach by Pike (2018) which operates on a similar principle to NaturePatternMatch.…”

mentioning

confidence: 99%

Quantitative Colour Pattern Analysis (QCPA): A Comprehensive Framework for the Analysis of Colour Patterns in Nature

Berg

Troscianko

Endler

et al. 2019

Preprint

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11 1. To understand the function of colour signals in nature, we require robust quantitative 12 analytical frameworks to enable us to estimate how animal and plant colour patterns appear 13 against their natural background as viewed by ecologically relevant species. Due to the 14 quantitative limitations of existing methods, colour and pattern are rarely analysed in 15 conjunction with one another, despite a large body of literature and decades of research on 16 the importance of spatiochromatic colour pattern analyses. Furthermore, key physiological 17 limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor 18 abundances and receptor noise levels are rarely considered together in colour pattern 19 analyses. 20 2. Here, we present a novel analytical framework, called the 'Quantitative Colour Pattern 21 Analysis' (QCPA). We have overcome many quantitative and qualitative limitations of existing 22 image segmentation, which we call 'Receptor Noise Limited Clustering', used here for the first 30 time. Furthermore, QCPA provides a novel psycho-physiological approach to the modelling of 31 spatial acuity using convolution in the spatial or frequency domains, followed by 'Receptor 32 Noise Limited Ranked Filtering' to eliminate intermediate edge artefacts and recover sharp 33 boundaries following smoothing. We also present a new type of colour pattern analysis, the 34 'Local Edge Intensity Analysis' (LEIA) as well as a range of novel psycho-physiological 35 approaches to the visualisation of spatiochromatic data.36 4. QCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, 37 user-friendly, free and open source toolbox and introduce a range of novel analytical and data-38 visualisation approaches. These analyses and tools have been seamlessly integrated into the 39 MICA toolbox providing a dynamic and user-friendly workflow. 40 5. QCPA is a framework for the empirical investigation of key theories underlying the design, 41 function and evolution of colour patterns in nature. We believe that it is compatible with, but 42 more thorough than, other existing colour pattern analyses. 43 44 Keywords: colour pattern analysis, colour perception, image analysis, visual modelling, receptor noise 45 limited model, animal colouration, colour space 46 47 65

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

Cited by 32 publications

References 58 publications

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

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

Como Treinar Sua Abelha: Métodos Aplicados À Biologia Cognitiva Da Polinização

Quantitative Colour Pattern Analysis (QCPA): A Comprehensive Framework for the Analysis of Colour Patterns in Nature

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