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Colour vision and background adaptation in a passerine bird, the zebra finch (Taeniopygia guttata)

Today, there is good knowledge of the physiological basis of bird colour vision and how mathematical models can be used to predict visual thresholds. However, we still know only little about how colour vision changes between different viewing conditions. This limits the understanding of how colour s...

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Detalles Bibliográficos
Autor principal: Lind, Olle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5043321/
https://www.ncbi.nlm.nih.gov/pubmed/27703702
http://dx.doi.org/10.1098/rsos.160383
Descripción
Sumario:Today, there is good knowledge of the physiological basis of bird colour vision and how mathematical models can be used to predict visual thresholds. However, we still know only little about how colour vision changes between different viewing conditions. This limits the understanding of how colour signalling is configured in habitats where the light of the illumination and the background may shift dramatically. I examined how colour discrimination in zebra finch (Taeniopygia guttata) is affected by adaptation to different backgrounds. I trained finches in a two-alternative choice task, to choose between red discs displayed on backgrounds with different colours. I found that discrimination thresholds correlate with stimulus contrast to the background. Thresholds are low, and in agreement with model predictions, for a background with a red colour similar to the discs. For the most contrasting green background, thresholds are about five times higher than this. Subsequently, I trained the finches for the detection of single discs on a grey background. Detection thresholds are about 2.5 to 3 times higher than discrimination thresholds. This study demonstrates close similarities in human and bird colour vision, and the quantitative data offer a new possibility to account for shifting viewing conditions in colour vision models.