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Parallel Inputs to Memory in Bee Colour Vision

Overview of attention for article published in Biologia Futura, December 2016
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Title
Parallel Inputs to Memory in Bee Colour Vision
Published in
Biologia Futura, December 2016
DOI 10.1556/018.67.2016.1.1
Pubmed ID
Authors

Adrian Horridge

Abstract

In the 19(th) century, it was found that attraction of bees to light was controlled by light intensity irrespective of colour, and a few critical entomologists inferred that vision of bees foraging on flowers was unlike human colour vision. Therefore, quite justly, Professor Carl von Hess concluded in his book on the Comparative Physiology of Vision (1912) that bees do not distinguish colours in the way that humans enjoy. Immediately, Karl von Frisch, an assistant in the Zoology Department of the same University of Münich, set to work to show that indeed bees have colour vision like humans, thereby initiating a new research tradition, and setting off a decade of controversy that ended only at the death of Hess in 1923. Until 1939, several researchers continued the tradition of trying to untangle the mechanism of bee vision by repeatedly testing trained bees, but made little progress, partly because von Frisch and his legacy dominated the scene. The theory of trichromatic colour vision further developed after three types of receptors sensitive to green, blue, and ultraviolet (UV), were demonstrated in 1964 in the bee. Then, until the end of the century, all data was interpreted in terms of trichromatic colour space. Anomalies were nothing new, but eventually after 1996 they led to the discovery that bees have a previously unknown type of colour vision based on a monochromatic measure and distribution of blue and measures of modulation in green and blue receptor pathways. Meanwhile, in the 20(th) century, search for a suitable rationalization, and explorations of sterile culs-de-sac had filled the literature of bee colour vision, but were based on the wrong theory.

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Geographical breakdown

Country Count As %
Australia 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 42%
Professor 1 8%
Student > Bachelor 1 8%
Student > Master 1 8%
Researcher 1 8%
Other 0 0%
Unknown 3 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 33%
Decision Sciences 1 8%
Social Sciences 1 8%
Medicine and Dentistry 1 8%
Neuroscience 1 8%
Other 0 0%
Unknown 4 33%