Conventional wisdom tells us that large objects appear smaller as they get farther from us, but this fundamental law of classical physics is reversed when we observe the distant universe.

Astrophysicists at the University of Kent simulated the development of the biggest objects in the universe to help explain how galaxies and other cosmic bodies were formed. By looking at the distant universe, it is possible to observe it in a past state, when it was still at a formative stage. At that time, galaxies were growing and supermassive black holes were violently expelling enormous amounts of gas and energy. This matter accumulated into pairs of reservoirs, which formed the biggest objects in the universe, so-called giant radio galaxies. These giant radio galaxies stretch across a large part of the Universe. Even moving at the speed of light, it would take several million years to cross one. {module In-article}

Professor Michael D. Smith of the Centre for Astrophysics and Planetary Science and student Justin Donohoe collaborated on the research. They expected to find that as they simulated objects farther into the distant universe, they would appear smaller, but in fact, they found the opposite.

Professor Smith said: 'When we look far into the distant universe, we are observing objects way in the past - when they were young. We expected to find that these distant giants would appear as a comparatively small pair of vague lobes. To our surprise, we found that these giants still appear enormous even though they are so far away.'

Radio galaxies have long been known to be powered by twin jets which inflate their lobes and create giant cavities. The team performed simulations using the Forge supercomputer, generating three-dimensional hydrodynamics that recreated the effects of these jets. They then compared the resulting images to observations of the distant galaxies. Differences were assessed using a new classification index, the Limb Brightening Index (LB Index), which measures changes to the orientation and size of the objects.

Professor Smith said: 'We already know that once you are far enough away, the Universe acts like a magnifying glass and objects start to increase in size in the sky. Because of the distance, the objects we observed are extremely faint, which means we can only see the brightest parts of them, the hot spots. These hot spots occur at the outer edges of the radio galaxy and so they appear to be larger than ever, confounding our initial expectations.'

Findings could have implications for the understanding of human vision and diagnosis of vision anomalies

Vision researchers at York University in Toronto, Canada, have disproved a long-standing theory of how the human visual system processes images, using computational models and human experiments.

A team led by John Tsotsos, a professor in the Department of Electrical Engineering and Computer Science at the Lassonde School of Engineering, found that the human brain does not select interesting portions of an image to process preferentially, as the highly influential 1958 theory of Donald Broadbent proposed.

For psychologist Broadbent, the interesting image portions are those that have relevance to why you are looking at a scene in the first place, or are novel items that immediately grab our attention. Broadbent's Theory of Early Selection, which has a modern counterpart in the Saliency Map Theory of Christof Koch and Shimon Ullman published in 1985, claims that these interesting regions are processed by the brain one at a time, in order of their salience, which is a numerical score of how interesting a region is. There are now hundreds of saliency algorithms, rooted in the work of Koch and Ullman, to accomplish such a ranking. {module In-article}

Tsotsos' team found, however, that salience is not needed at all for the simple task of quickly deciding what an image depicts. Moreover, none of the current algorithms within artificial intelligence (AI) for this task come close to matching human performance, which is remarkably good. On the other hand, salience computation does play a primary role in determining where humans move their eyes, and it is eye movement that selects portions of a scene to process next.

"Our study looks at this for vision and tests the leading algorithms that compute the saliency measure and asks the question 'are those algorithms performing at the same level as humans do on these images'? For example, if the task is to determine if there is a cat in a scene, does the saliency algorithm pick out the cat correctly? The study showed that these algorithms are far from doing as well as humans," said Tsotsos.

To further test existing algorithms, the team conducted additional experiments with 17 subjects from 25 to 34 years old. In one of the replicated experiments, participants were shown 2000 color images. The subjects were not familiar with the images and viewed each image with and without animals, only once. The images were then manipulated in such a way so that only the most central parts of the retina that have the highest resolution would see what was in the image and see nothing in the periphery. Participants were asked to look at the center of each photograph for 20 seconds before it disappeared. The participants were able to correctly identify if an animal was present in the picture or not.

Tsotsos says this finding has important ramifications for our understanding of human vision and human visual processing especially for diagnosing vision pathologies, such as aspects of autism.

"When you want to diagnose issues in vision, you're basing on it how the healthy visual processing system should work. What we've done with this study is added a piece of the puzzle to how the 'healthy' system works which then would change how you compare an anomaly in order to be able to diagnose it."

Tsotsos adds that this piece of the puzzle could also be useful in building new models and improving current ones for autonomous driving or security applications.