Scientists have used artificial intelligence (AI) to shed light on how humans process abstract learning.
Deep Convolutional Neural Networks, or DCNNs, suggest human knowledge stems from experience, a school of thought known as empiricism, said Cameron Buckner, an assistant professor at the University of Houston in the US.
These neural networks -- multi-layered artificial neural networks, with nodes replicating how neurons process and pass along information in the brain -- demonstrate how abstract knowledge is acquired, making the networks a useful tool for fields including neuroscience and psychology.
According to the research, published in the journal Synthese, the success of these networks at complex tasks involving perception and discrimination has at times outpaced the ability of scientists to understand how they work.
Researchers used AI for abstract reasoning, ranging from strategy games to visual recognition of chairs, artwork and animals, tasks that are surprisingly complex considering the many potential variations in vantage point, colour, style and other detail.
"Computer vision and machine learning researchers have recently noted that triangle, chair, cat, and other everyday categories are so difficult to recognise because they can be encountered in a variety of different poses or orientations that are not mutually similar in terms of their low-level perceptual properties," Buckner said.
"A chair seen from the front does not look much like the same chair seen from behind or above; we must somehow unify all these diverse perspectives to build a reliable chair-detector," he said.
To overcome the challenges, the systems have to control for so-called nuisance variation, or the range of differences that commonly affect a system's ability to identify objects, sounds and other tasks -- size and position, for example, or pitch and tone.
The ability to account for and digest that diversity of possibilities is a hallmark of abstract reasoning.
The DCNNs have also answered another lingering question about abstract reasoning, Buckner said.
Empiricists have appealed to a faculty of abstraction to complete their explanations of how the mind works, but until now, there hasn't been a good explanation for how that works.
"For the first time, DCNNs help us to understand how this faculty actually works," Buckner said.
Now that machines are beating humans at strategic games, driverless cars are being tested around the world and facial recognition systems are deployed everywhere from cell phones to airports, finding answers has become more urgent.
"These systems succeed where others failed, because they can acquire the kind of subtle, abstract, intuitive knowledge of the world that comes automatically to humans but has until now proven impossible to program into computers," Buckner said.