A new camera system has been developed that allows researchers and filmmakers to capture animal-view videos of moving objects under natural lighting conditions.
The camera simultaneously records video in four color channels: blue, green, red and UV. This data can be processed into perceptual units to produce an accurate video of how those colors are perceived by animals based on existing knowledge of the photoreceptors in their eyes.
A new camera system has been developed that allows researchers and filmmakers to capture animal-view videos of moving objects under natural lighting conditions. The camera simultaneously records video in four color channels: blue, green, red and UV. This data can be processed into perceptual units to produce an accurate video of how those colors are perceived by animals based on existing knowledge of the photoreceptors in their eyes. The team tested the system against a traditional method that uses spectrophotometry and found that it predicted perceived colors with an accuracy of over 92 percent.
The camera system captures natural animal-view moving images with over 90 percent accuracy.
Different animal species possess unique sets of photoreceptors that are sensitive to a wide range of wavelengths, from ultraviolet to the infrared, dependent on each animal's specific ecological needs. Some animals can even detect polarized light.
Honeybees and birds are sensitive to UV light, which isn't visible to human eyes.
Current techniques for producing false color imagery can't quantify the colors animals see while in motion, an important factor since movement is crucial to how different animals communicate and navigate the world around them via color appearance and signal detection. Traditional spectrophotometry relies on object-reflected light to estimate how a given animal's photoreceptors will process that light, but it's a time-consuming method, and much spatial and temporal information is lost.
The team set out to develop a camera system capable of producing high-precision animal-view videos that capture the full complexity of visual signals as they would be perceived by an animal in a natural setting. They combined existing methods of multispectral photography with new hardware and software designs. The camera records video in four color channels simultaneously (blue, green, red, and UV). Once that data has been processed into 'perceptual units,' the result is an accurate video of how a colorful scene would be perceived by various animals.
The team's system predicts the perceived colors with 92 percent accuracy. The cameras are commercially available, and the software is open source so that others can freely use and build on it.
Accuracy
No Contradictions at Time
Of
Publication
Deception
(100%)
None Found At Time Of
Publication
Fallacies
(85%)
The article contains several fallacies. The author uses an appeal to authority by stating that the camera system is faster and more flexible than existing systems without providing any evidence or citation for this claim. Additionally, the author makes a false dilemma by implying that animals only make crucial decisions on moving targets when they are hunting for food or evaluating potential mates. This oversimplifies the complexity of animal behavior and perception. The article also contains an example of inflammatory rhetoric with the phrase 'false color imagery is powerful and compelling'. Finally, there is a lack of diversity in examples provided as all examples given are related to honeybees and birds.
The camera system predicts the perceived colors with 92 percent accuracy.
Bias
(100%)
None Found At Time Of
Publication
Site
Conflicts
Of
Interest (50%)
Jennifer Ouellette has a conflict of interest on the topic of honeybees as she is an author for PLoS Biology.
Author
Conflicts
Of
Interest (50%)
Jennifer Ouellette has a conflict of interest on the topics of novel camera system and animal-view videos as she is an author for PLoS Biology.
The new technology uses a combination of novel hardware and software to produce images and videos that match the accuracy of conventional methods used to capture an animal's color vision.
Honeybees are sensitive to UV light, which isn't visible to human eyes.
Different animal species possess unique sets of photoreceptors that are sensitive to a wide range of wavelengths, from ultraviolet to infrared, dependent on each animal's specific ecological needs.
The team set out to develop a camera system capable of producing high-precision animal-view videos that capture the full complexity of visual signals as they would be perceived by an animal in a natural setting.
Current techniques for producing false color imagery can't quantify the colors animals see while in motion, an important factor since movement is crucial to how different animals communicate and navigate the world around them via color appearance and signal detection.
The team predicts the perceived colors with 92 percent accuracy. The cameras are commercially available, and the software is open source so that others can freely use and build on it.
Accuracy
The camera system captures natural animal-view moving images with over 90 percent accuracy.
Different animal species possess unique sets of photoreceptors that are sensitive to a wide range of wavelengths, from ultraviolet to the infrared, dependent on each animal's specific ecological needs. Some animals can even detect polarized light.
Deception
(50%)
The article is deceptive in that it claims the camera system can capture light simultaneously from four distinct wavelength regions: ultraviolet, blue, green and red. However this is not true as the image shows only two colors (blue and orange) which are a result of combining UV sensitive images with visible light images.
The inner box shows what a typical person would see.
Fallacies
(85%)
The article contains several examples of informal fallacies. The author uses an appeal to authority by stating that the team's work has been funded by the National Geographic Society and including award-winning nature photographer and filmmaker Neil Losin as a member of the team. This implies that their research is trustworthy because it was supported by reputable organizations, but this does not necessarily mean that their findings are accurate or reliable. The author also uses inflammatory rhetoric when stating that
The system works by splitting light between two cameras,
Bias
(85%)
The author of the article has a clear bias towards promoting their own research and technology. The language used in the article is overly positive and enthusiastic about the new camera system they have developed, with phrases such as 'nearly matches', 'more versatile', 'dynamic technique' being used to describe it.
The team found that its innovation nearly matches the accuracy of conventional, yet more limiting, methods used to capture an animal’s color vision.
Site
Conflicts
Of
Interest (100%)
None Found At Time Of
Publication
Author
Conflicts
Of
Interest (50%)
Ed Cara has a conflict of interest on the topics of animals and color vision as he is an author for Gizmodo.com which may have financial ties to companies that produce products related to these topics.
Scientists have developed a new video system that reveals how animals see color.
Many birds can see ultraviolet light while humans cannot.
The human eye contains three types of cone cells that allow us to see blue, green and red light.
Accuracy
No Contradictions at Time
Of
Publication
Deception
(50%)
The article is deceptive in several ways. Firstly, the title implies that the video system will give a human perspective on how animals see color. However, it only shows how other creatures perceive colors and not humans. Secondly, the author claims that many birds have cone cells sensitive to ultraviolet light which allows them to see a broader spectrum of colors than people can. This is incorrect as most birds do not have such cone cells.
The title implies that the video system will give a human perspective on how animals see color, but it only shows how other creatures perceive colors and not humans.
Fallacies
(85%)
The article contains several examples of informal fallacies. The author uses anecdotes to illustrate points without providing evidence or context for their significance. For example, the author mentions that a butterfly might appear entirely different to an avian observer but does not provide any data or research on this claim.
Their sky will be essentially an ultraviolet sky,
Bias
(80%)
The article contains several examples of bias. The author uses language that dehumanizes humans by implying they are unable to see the world in its true colors due to their lack of ultraviolet sensitivity. This is a form of religious bias as it implies that animals have superior vision abilities and are more attuned to nature than humans.
Humans can't see ultraviolet light, but many birds can.
In addition to blue, green and red cone cells, many birds have cone cells that are sensitive to ultraviolet light
Many animals have different combinations of photoreceptors, which means that they see the world in their own distinct color palettes.
Mice have just two types of cones — one sensitive to green and another sensitive to ultraviolet
Their sky will be essentially an ultraviolet sky
Site
Conflicts
Of
Interest (50%)
Emily Anthes has a conflict of interest on the topic of animal vision as she is an author and journalist for The New York Times. She also mentions her own research in the article which could be seen as promoting her work.
Author
Conflicts
Of
Interest (50%)
Emily Anthes has a conflict of interest on the topic of animal vision as she is an author for The New York Times.
A new camera system was developed by Mason scientist Daniel Hanley and team that can accurately replicate the colors animals see in natural settings
The camera records video in four color channels: blue, green, red and UV
This new system predicted perceived colors with an accuracy of over 92 percent when tested against a traditional method that uses spectrophotometry.
Different animal species possess unique sets of photoreceptors that are sensitive to a wide range of wavelengths, from ultraviolet to the infrared, dependent on each animal's specific ecological needs. Some animals can even detect polarized light.
Accuracy
The camera system captures natural animal-view moving images with over 90 percent accuracy.
Deception
(50%)
The article is deceptive in several ways. Firstly, the author claims that they have developed a new camera system that captures animal-view videos with over 92% accuracy. However, this claim is not supported by any evidence presented in the article and it's unclear how accurate these results are.
The sentence 'We were very surprised by how accurate the method was for quantifying colors on moving targets,' suggests that they expected a lower level of accuracy.
Fallacies
(85%)
The article contains an appeal to authority fallacy by stating that the new camera system was developed by a team of researchers at George Mason University and the University of Sussex. The author also uses inflammatory rhetoric when they describe how animals perceive colors differently than humans, which can be seen as sensationalizing or exaggerating the importance of this research.
The article states that 'Mason scientist Daniel Hanley' developed a new camera system with colleagues from the University of Sussex. This is an appeal to authority fallacy because it implies that Mr. Hanley has expertise in developing such systems and his credibility should be trusted without question.
The author uses inflammatory rhetoric when they describe how animals perceive colors differently than humans, stating 'animals often make crucial decisions on moving targets like detecting food items and evaluating a potential mate's display'. This can be seen as sensationalizing or exaggerating the importance of this research.
Scientists have developed a new video camera system that captures natural animal-view moving images with over 92% accuracy.
The team set out to develop a camera system capable of producing high-precision animal-view videos that capture the full complexity of visual signals as they would be perceived by an animal in a natural setting. They combined existing methods of multispectral photography with new hardware and software designs.
Accuracy
The clip shows a zebra swallowtail butterfly foraging on flowers as a honeybee would see it.
Honeybees and birds are sensitive to UV light, which isn't visible to human eyes.
Deception
(50%)
The article is deceptive in that it presents the footage as if it shows how animals see the world. However, this is not entirely accurate because each animal's vision has been enhanced or altered to make them appear more similar to humans. The video2vision software used by researchers applies a set of transformation functions based on existing knowledge of an animal's photoreceptors, but these are still approximations and do not fully capture the complexity of how animals perceive colors in their environment.
The article states that the footage is 92% accurate based on testing against conventional spectrophotometry techniques. However, this does not mean that the footage accurately represents how animals see colors in their environment.
The article claims that it shows footage from different animals' perspectives. However, this is misleading because each animal has been given enhanced or altered vision to make them appear more similar to humans. For example, the honeybee clip shows a zebra swallowtail butterfly foraging on flowers as if a honeybee were seeing it.
Fallacies
(85%)
The article contains several examples of informal fallacies. The author uses an appeal to authority by stating that the scientists who created this technology are experts in their field and have published a paper on it. This is not enough evidence to establish their expertise or credibility without further information about the researchers' qualifications, experience, and methodology.
The article mentions that 'scientists generated several clips', but there is no indication of who these scientists are or what their credentials are. This creates a hazy appeal to authority.
Bias
(85%)
The article is about a new camera system that generates stunning video clips of the world as different animals see it. The author uses language that dehumanizes humans by implying they are not capable of seeing the world in the same way as animals. This is an example of religious bias.
From more intense reds to streaks of ultraviolet, the footage shows various settings in and around a garden environment, with some colors accentuated and others dulled depending on which animal's vision is being emulated.
Scientists have combined a new camera system with open-source software to generate stunning video clips of the world as different animals see it
The clip shows a zebra swallowtail butterfly (Protographium marcellus) foraging on flowers as a honeybee (Apis mellifera) would see it.
