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14 results for “Color discrimination”
Categorical facilitation with equally discriminable colors
<p>This data supplements the study of:</p> <p>Witzel, C., & Gegenfurtner, K. R. (2015). Categorical facilitation with equally discriminable colors. Journal of Vision, 15(8), 22. doi:10.1167/15.8.22, http://jov.arvojournals.org/article.aspx?articleid=2381517</p> <p>The Excell-file provides the data shown in Figure 4 of the above article, which shows the main results. The first sheet (trained) provides the data for the first, experienced group of participants, the second sheet (naive) the data for the naive, untrained group of participants.</p> <p>Rows refer to the 20 stimulus pairs.</p> <p>Columns:</p> <p>sti_ctg = category membership of each colour in a pair.</p> <p>sti_type = type of colour pair: 1 = centre pair, 2 = boundary, 3 & 4 = transitional pairs</p> <p>sti_azi = Hue (azimuth) in DKL-space</p> <p>rt = response times, one column for each observer</p> <p>er = error rates, one column for each observer</p>
Data used in "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition"
<p>Datasets from the paper "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition", By D. T. Blewett, T. X. Choi, Y.-C. Zheng, and E. A. Cloutis, to be published in the journal <em>Earth and Space Science</em>.</p> <p>Reflectance spectra for <em>Apollo</em> lunar samples 14003, 15601, 70011, 14310, and 65015 were published by Wagner et al. (1987), <em>Icarus 69</em>, 14–28. The spectra were digitized by Amanda Hendrix and Faith Vilas (see Hendrix and Vilas (2006), <em>Astron. J. 132</em>, 1396–1404). I took the spectra that Hendrix and Vilas supplied to me and resampled them to RELAB wavelengths. The spectra are in a tab-delimited text file.</p> <p>The responsivities of the <em>Chang'E-3</em> PCAM RGB channels were published by X. Ren et al. (2014), <em>Res. Astron. Astrophys. 14</em>, 1557–1566. We digitized the RGB curves from Fig. 2 of the Ren paper. The curves are in tab-delimited text files.</p> <p> </p>
Dataset: Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks
<p><strong>Associated publication</strong></p> <p>[1] Samuel Ponting*, <strong>Takuma Morimoto</strong>*, Hannah E. Smithson, “Modelling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks”, *equal contribution, bioRxiv, <a href="https://www.google.com/url?q=https%3A%2F%2Fdoi.org%2F10.1101%2F2022.11.02.514864&sa=D&sntz=1&usg=AOvVaw3KwSo7KmqPzBR1UMc1MHmk">https://doi.org/10.1101/2022.11.02.514864</a></p> <p>[2] Takuma Morimoto, and Hannah E. Smithson, “Discrimination of spectral reflectance under complex environmental illumination,” Journal of the Optical Society of America A, 35, 4, B244-B255 (2018) https://doi.org/10.1364/JOSAA.35.00B244</p> <p> </p> <p>Datasets contain 2 folders and 1 mat file.</p> <p> </p> <p><strong>(Folder 1) Stimuli</strong></p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p> </p> <p>Details are described below.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 1) Stimuli</strong></p> <p> </p> <p><strong>Overview of datasets</strong></p> <p>This Image dataset includes 57,600 images (2 gloss levels * 3 environments * 100 stimulus magnitudes * 8 hue directions * 12 camera angles from 0 to 330 degree in 30 degree step) in .mat format.</p> <p> </p> <p>The half of images were used in psychophysical experiment (camera angles: 0, 60, 120, 180, 240, 300 degrees).</p> <p>Other half images were used for testing chromatic statistics models and CNN-based models [1] (camera angles: 30, 90, 150, 210, 270, 330 degrees).</p> <p> </p> <p><strong>Each image file</strong></p> <p>Filename denotes a condition name and the camera angle as formatted in a following way.</p> <p> </p> <p>stim_”environment” _”glossiness”_”hueAngle”_”magnitude”_”cameraAngle”.mat</p> <p>e.g. “stim_en1_glossy_hue45_n45_cameraAngle90.mat”</p> <p> </p> <p>Stimulus magnitude 100 is a maximum saturation, and 1 corresponds to equal energy white (which was used as a distractor object).</p> <p> </p> <p>Each image file contains two valuables : MacLeodBoynton, XYZ</p> <p> </p> <p>Each variable contains an image of 128*128*3 pixels (height*width*channel).</p> <p> </p> <p>MacLeod-Boynton: MacLeod-Boynton chromaticity image (1st channel: L/(L+M), 2nd channel: S/(L+M), and 3rd channel L+M)</p> <p>XYZ: XYZ coordinates calculated based on 2-degree CIE 1931 xyz color matching function (1st channel: X, 2nd channel: Y, and 3rd channel Z)</p> <p> </p> <p>Luminance and L+M are both relative (normalised by the maximum luminance across all 57,600 images).</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Folder 2) Psychophysics_data</strong></p> <p>Filename denotes the condition and observers formatted in a following way.</p> <p> </p> <p>data_”environment” _”specularities”_”sessionNumber”_”obsever”.mat</p> <p>e.g. data_en2_matte_session4_JH.mat or .csv</p> <p> </p> <p>Each file includes following variables:</p> <p> </p> <p>(Variable 1) threshold</p> <p>Thresholds are stored in MacLeod-Boynton (MB) chromaticity coordinates for all 8 hue directions (from 0 to 315 degree in 45 degree step).</p> <p> </p> <p>MacLeod-Boynton chromaticity coordinates were calculated in a following way. </p> <p>These scalings are in accordance with description in CVRL main site (Chromaticity coordinates tab ).</p> <p> </p> <p>First of all, L, M, and S cone signals were calculated based on Stockman & Sharpe cone fundamentals (energy in linear scale available at at http://www.cvrl.org).</p> <p>Each sensitivity curve was normalised to have 1.0 at the peak.</p> <p> </p> <p>Then, MB coordinates were calculated using equation (1-3).</p> <p> </p> <p>L/(L+M) = Lw*L/(Lw*L+Mw*M) - (1)</p> <p>S/(L+M) = Sw*S/(Lw*L+Mw*M) - (2)</p> <p>L+M = Lw*L+Mw*M - (3)</p> <p> </p> <p>where Lw = 0.689903; Mw = 0.348322;Sw = 1.93540.</p> <p> </p> <p>L, M and S denote L-cone, M-cone, S-cone excitations, respectively.</p> <p> </p> <p>Under this calculation, equal energy white becomes L/(L+M) = 0.7078 and S/(L+M) = 1.</p> <p> </p> <p>(Variable 2) staircase</p> <p> </p> <p>Since we ran 8 interleaved staircase (for 8 hue angles), information about 8 staircases are stored in this single variable.</p> <p>(staircase(1) corresponds to 0 degree, and staircase(8) corresponds to 315 degree)</p> <p> </p> <p>There are 5 fields:</p> <p>(i) groundtruth, (ii) response, (iii) correct, (iv) magnitude, (v) cameraAngle</p> <p> </p> <p>For each trial, the location of objects was defined in a following way.</p> <p>| 1 3 |</p> <p>| 2 4 |</p> <p> </p> <p>And each field stores following information for all trials in the staircase.</p> <p> </p> <p>(i) groundtruth</p> <p>Location of the target object</p> <p> </p> <p>(ii) response</p> <p>Location that the participant chose</p> <p> </p> <p>(iii) correct</p> <p>If the response was correct (1) or incorrect (0)</p> <p> </p> <p>(iv) magnitude</p> <p>Stimulus magnitude of target object in each trial from 1 to 100 (1 for equal energy white and 100 for maximum saturation).</p> <p> </p> <p>(v) Camera angle</p> <p>Camera angles assigned for four objects in each trial.</p> <p>This data and (i) groundtruth allow reconstruct of the exact image for each trial.</p> <p> </p> <p>----------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>(Mat file) stimulusMagnitudeToMacLeodBoynton.mat</strong></p> <p>This file stores a variable ‘stimulusMagnitudeToMacLeodBoynton’ (8*100*2) which describes correspondence map between stimulus magnitude and MacLeod-Boynton chromaticity.</p> <p> </p> <p>1st channel: hue direction from 0 degree to 315 degree, 45 degree step</p> <p>2nd channel: magnitude from 1 to 100</p> <p>3rd channel: MacLeod-Boynton coordinate, 1 being L/(L+M) and 2 being S/(L+M)</p> <p> </p>
Ultraviolet vision in anemonefish improves color discrimination
<p><span>In many animals, ultraviolet (UV) vision guides navigation, foraging, and communication, but few studies have addressed the contribution of UV vision to color discrimination, or behaviorally assessed UV discrimination thresholds. Here, we tested UV-color vision in an anemonefish (<em>Amphiprion</em> <em>ocellaris</em>) using a novel five-channel (RGB-V-UV) LED display designed to test UV perception. We first determined that the maximal sensitivity of the <em>A</em>. <em>ocellaris</em> UV cone was at ~386 nm using microspectrophotometry. Three additional cone spectral sensitivities had maxima at ~497, 515, and ~535 nm, which together informed the modelling of the fish's color vision. Anemonefish behavioral discrimination thresholds for nine sets of colors were determined from their ability to distinguish a colored target pixel from grey distractor pixels of varying intensity. We found that <em>A</em>. <em>ocellaris</em> used all four cones to process color information and is therefore tetrachromatic, and fish were better at discriminating colors (i.e., color discrimination thresholds were lower, or more acute) when targets had UV chromatic contrast elicited by greater stimulation of the UV cone relative to other cone types. These findings imply that a UV component of color signals and cues improves their detectability, which likely increases the salience of anemonefish body patterns used in communication and the silhouette of zooplankton prey.</span></p>
Ultraviolet vision in anemonefish improves color discrimination
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Data from: Egg discrimination along a gradient of natural variation in eggshell coloration
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Data from: Dietary carotenoid availability affects avian color discrimination
Carotenoid pigments are found in the retinas of many vertebrate species, where they serve a range of functions. In birds, carotenoid-containing retinal oil droplets act as optical filters, modifying the light reaching the underlying visual pigment and thereby enhancing color vision. Dietary carotenoid manipulation is known to affect the allocation of carotenoids to the retina, although the effects this has on vision are less well understood. Using dietary manipulations, in which juvenile Japanese quail (Coturnix japonica) received either a high- or a low-carotenoid diet, we tested the effects of carotenoid availability on the ability to perform a color discrimination task. Birds on both diet treatments were able to make a relatively coarse discrimination between colors that appeared to humans as yellow-orange and orange; however, only high-carotenoid diet birds were able to make a finer-scale discrimination involving intermediate colors, showing that dietary carotenoid availability can directly affect the ability of birds to make chromatic discriminations. This finding has implications for our understanding of trade-offs in carotenoid allocation between vision and other key functions such as sexual ornamentation and health maintenance, and suggests that variation in dietary carotenoid availability may affect the ability of animals to make ecologically pertinent color discriminations, such as between sexual signals or cryptic food items.
No effect of Italian blues on color discrimination
<p>This is the material used in Stratmann & Lohmann (2023). It contains the visual stimuli used in the experiments as well as the experiment files and the data that entered the final analysis. The two datasets were then further analyzed in comparison to the data from Winawer et al. (2007), which are not published here.</p>
Data from: Inaccurate color discrimination by pollinators promotes evolution of discrete color polymorphism in food-deceptive flowers
Many plant species employing food-deceptive pollination strategy show discrete or continuous floral polymorphism within their populations. Previous studies have suggested that negative frequency-dependent selection (NFDS) caused by learning behavior of pollinators was responsible for maintenance of floral polymorphism. However, NFDS alone does not explain why and when discrete or continuous polymorphism evolves. In this study, we use an evolutionary simulation model to propose that inaccurate discrimination of flower colors by pollinators results in evolution of discrete flower color polymorphism. Simulations showed that associative learning based on inaccurate discrimination in pollinators caused disruptive selection of flower colors. The degree of inaccuracy determined the number of discrete flower colors that evolved. Our results suggest that animal behavior based on inaccurate discrimination may be a general cause of disruptive selection that promotes discrete trait polymorphism.
Data from: Honeybees (Apis mellifera) learn color discriminations via differential conditioning independent of long wavelength (green) photoreceptor modulation
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Data from: Color discrimination in the tufted capuchin monkey, Sapajus spp.
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Data from: Inaccurate color discrimination by pollinators promotes evolution of discrete color polymorphism in food-deceptive flowers
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Data from: Dietary carotenoid availability affects avian color discrimination
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A Registered Cohort Study on Color Discrimination Deficit Associated With Neurodegenerative Disorders.
ClinicalTrials.gov study NCT05639140. IPD Sharing: NO. Countries: 1. Publications: 0.
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