In a debut effort, British researchers delve into the working of the human visual system using computer-based artificial intelligence.
Scientists at Queen Mary, a college of the University of London, have found that when it comes to searching for a target in pictures, people don't have two special mechanisms in the brain - one for easy searches and one for hard - as has been previously suggested.
AdvertisementWriting in the journal Vision Research, Professor Peter McOwan and Milan Verma say that people rather have a single brain mechanism that just finds it harder to complete the task as it becomes more difficult.
The team based a "genetic algorithm" on a simple model of evolution that could can breed a range of images and visual stimuli, which were then used to test people's brain performance.
The researchers used artificial intelligence to design the test patterns, and removed any likelihood of predetermining the results that could have occurred they had designed the test pictures themselves.
The AI generated a picture where a grid of small computer-created characters contains a small "pop out" region of a different character.
Professor Peter McOwan, who led the project, explains: "A 'pop out' is when you can almost instantly recognise the'different' part of a picture, for example, a block of Xs against a background of Os. If it's a block of letter Ls against a background of Ts that's far harder for people to find.
It was thought that we had two different brain mechanisms to cope with these sorts of cases, but our new approach shows we can get the AI to create new sorts of patterns where we can predictably set the level of difficulty of the 'spot the difference' task."
Milan Verma added: "Our AI system creates a unique range of different shapes that run from easy to spot differences, to hard to spot differences, through all points in between. When we then get people to actually perform the search task, we find that the time they take to perform the task varies in the way we would expect."
The researchers say that their new artificial intelligence-based experimental approach may also be applied to other experiments in the future, providing vision scientists with new ways to generate custom images for their experiments.
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