AI helps reveal how people process abstract thought

​Houston: 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.

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.