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A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision
Published on: February 11, 2014
An Ishihara-style test of animal colour vision
Karen L Cheney1,2, Naomi F Green3, Alexander P Vibert3
1School of Biological Sciences, The University of Queensland, Brisbane, QLD 4072, Australia k.cheney@uq.edu.au john.endler@deakin.edu.au.
Abstract:
Colour vision mediates ecologically relevant tasks for many animals, such as mate choice, foraging and predator avoidance. However, our understanding of animal colour perception is largely derived from human psychophysics, and behavioural tests of non-human animals are required to understand how colour signals are perceived. Here, we introduce a novel test of colour vision in animals inspired by the Ishihara colour charts, which are widely used to identify human colour deficiencies. In our method, distractor dots have a fixed chromaticity (hue and saturation) but vary in luminance. Animals can be trained to find single target dots that differ from distractor dots in chromaticity. We provide MATLAB code for creating these stimuli, which can be modified for use with different animals. We demonstrate the success of this method with triggerfish, Rhinecanthus aculeatus, which quickly learnt to select target dots that differed from distractor dots, and highlight behavioural parameters that can be measured, including success of finding the target dot, time to detection and error rate. We calculated discrimination thresholds by testing whether target colours that were of increasing colour distances (ΔS) from distractor dots could be detected, and calculated discrimination thresholds in different directions of colour space. At least for some colours, thresholds indicated better discrimination than expected from the receptor noise limited (RNL) model assuming 5% Weber fraction for the long-wavelength cone. This methodology could be used with other animals to address questions such as luminance thresholds, sensory bias, effects of sensory noise, colour categorization and saliency.
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