A new study in PNAS shows that bees share a capacity for automatic learning the complex statistical properties often experienced in natural environments. Previously this was thought to be a visual capacity only present in humans and higher-level species, and the discovery in bees with a miniature brain inspires further improvements in AI. The study also reports that bees and humans use fundamentally different computational methods for this kind of learning, which might be one of the key reasons why humans’ superior learning abilities emerged.
The international team led by Dr. Aurore Avarguès-Weber (University of Toulouse, France), Dr. József Fiser (Central European University, Hungary) and Dr. Adrian Dyer (RMIT University, Australia) used for the first time an identical test to compare automatic learning in humans and bees. They exposed humans and honeybees to the same multi-element scenes composed of a set of abstract shapes in an unrelated easy categorization task (Fig 1). In the following test phase, both species had to perform a number of tests by choosing between two novel multi-element scenes in each trial. The scenes in these tests were composed to measure whether the participants became spontaneously sensitive to various statistical properties of the visual scenes that they saw during the exposure phase without any dedicated training.
Dr. Avarguès-Weber says ” Learning automatically by analyzing the statistical properties of a large set of previously experienced images to identify their underlying structure is a strategy that has been demonstrated in humans and in a few higher-level species. It is also the concept behind “deep learning”, which fueled the immerse recent progress in Artificial Intelligence. Our results show that this is also the strategy used by bees, which suggests the universality and efficiency of this kind of automatic statistical learning.”
Dr. Dyer adds “People have often been amazed at wonderful navigation and recognition capabilities of honeybees, and now we know that they achieve complex tasks using a simplified version of statistical learning that is the basis of human visual problem solving, and essentially the basis of deep learning for AI.”
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