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Chromatic and achromatic vision : Parameter choice and limitations for reliable model predictions

Olsson, Peter LU ; Lind, Olle LU and Kelber, Almut LU (2018) In Behavioral Ecology 29(2). p.273-282
Abstract

Many animals use vision to detect, discriminate, or recognize important objects such as prey, predators, homes, or mates. These objects may differ in color and brightness-having chromatic and achromatic contrast to the background or to other objects. Visual models are powerful tools to investigate contrast detection, but need to be calibrated by experimental data to provide robust predictions. The most critical parameter of current models-receptor noise-is usually estimated from a small number of behavioral tests on chromatic contrast thresholds, while equivalent tests of achromatic thresholds in a wide range of animals have often been ignored. We suggest that both chromatic and achromatic contrasts in studies of visual ecology should... (More)

Many animals use vision to detect, discriminate, or recognize important objects such as prey, predators, homes, or mates. These objects may differ in color and brightness-having chromatic and achromatic contrast to the background or to other objects. Visual models are powerful tools to investigate contrast detection, but need to be calibrated by experimental data to provide robust predictions. The most critical parameter of current models-receptor noise-is usually estimated from a small number of behavioral tests on chromatic contrast thresholds, while equivalent tests of achromatic thresholds in a wide range of animals have often been ignored. We suggest that both chromatic and achromatic contrasts in studies of visual ecology should be examined using calibrated model parameters, and we provide a compilation of what is currently known on visual thresholds and corresponding noise estimates. Besides the need for careful parameter estimation, we discuss how the robustness of model predictions depends on assumptions about overall light intensity, background color and brightness, object size, and behavioral context.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
achromatic contrast sensitivity, color contrast, intensity contrast, visual modeling, Weber fraction
in
Behavioral Ecology
volume
29
issue
2
pages
10 pages
publisher
Oxford University Press
external identifiers
  • scopus:85043457988
ISSN
1045-2249
DOI
10.1093/beheco/arx133
language
English
LU publication?
yes
id
2863ef37-112c-4749-9e4d-c8bd4724d74f
date added to LUP
2018-05-07 14:07:08
date last changed
2018-09-16 04:55:07
@article{2863ef37-112c-4749-9e4d-c8bd4724d74f,
  abstract     = {<p>Many animals use vision to detect, discriminate, or recognize important objects such as prey, predators, homes, or mates. These objects may differ in color and brightness-having chromatic and achromatic contrast to the background or to other objects. Visual models are powerful tools to investigate contrast detection, but need to be calibrated by experimental data to provide robust predictions. The most critical parameter of current models-receptor noise-is usually estimated from a small number of behavioral tests on chromatic contrast thresholds, while equivalent tests of achromatic thresholds in a wide range of animals have often been ignored. We suggest that both chromatic and achromatic contrasts in studies of visual ecology should be examined using calibrated model parameters, and we provide a compilation of what is currently known on visual thresholds and corresponding noise estimates. Besides the need for careful parameter estimation, we discuss how the robustness of model predictions depends on assumptions about overall light intensity, background color and brightness, object size, and behavioral context.</p>},
  author       = {Olsson, Peter and Lind, Olle and Kelber, Almut},
  issn         = {1045-2249},
  keyword      = {achromatic contrast sensitivity,color contrast,intensity contrast,visual modeling,Weber fraction},
  language     = {eng},
  month        = {03},
  number       = {2},
  pages        = {273--282},
  publisher    = {Oxford University Press},
  series       = {Behavioral Ecology},
  title        = {Chromatic and achromatic vision : Parameter choice and limitations for reliable model predictions},
  url          = {http://dx.doi.org/10.1093/beheco/arx133},
  volume       = {29},
  year         = {2018},
}