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Receptor noise as a determinant of colour thresholds

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Vorobyev, M., Osorio, D.·1998·Proceedings of the Royal Society of London. Series B: Biological Sciences

Abstract

Inferences about mechanisms at one particular stage of a visual pathway may be made from psychophysical thresholds only if the noise at the stage in question dominates that in the others. Spectral sensitivities, measured under bright conditions, for di-, tri-, and tetrachromatic eyes from a range of animals can be modelled by assuming that thresholds are set by colour opponency mechanisms whose performance is limited by photoreceptor noise, the achromatic signal being disregarded. Noise in the opponency channels themselves is therefore not statistically independent, and it is not possible to infer anything more about the channels from psychophysical thresholds. As well as giving insight into mechanisms of vision, the model predicts the performance of colour vision in animals where physiological and anatomical data on the eye are available, but there are no direct measurements of perceptual thresholds. The model, therefore, is widely applicable to comparative studies of eye design and visual ecology.

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Metadata

Authors
Vorobyev, M., & Osorio, D.
Venue
Proceedings of the Royal Society of London. Series B: Biological Sciences
Published
1998-03-07
Volume
265
Issue
1394
Pages
351-358
ISSN
  • 0080-4649 (ISSN-L)
  • 0962-8452
  • 1471-2954
OpenAlex
W2092084985
Cited by
1,266
References
0
FWCI
5.27
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Receptor noise as a determinant of colour thresholds. https://app.cochat.ai/papers/10.1098/rspb.1998.0302
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Vorobyev, M., & Osorio, D. (1998). Receptor noise as a determinant of colour thresholds. Proceedings of the Royal Society of London. Series B: Biological Sciences. https://doi.org/10.1098/rspb.1998.0302

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.