Both humans and other animals are good at learning by inference, using information we do have to figure out things we cannot observe directly. New research from the Center for Mind and Brain at the University of California, Davis, shows how our brains achieve this by constructing cognitive maps.
“The work suggests a new framework for learning in structured environments that goes beyond incremental, experiential learning of associations,” said Erie Boorman, assistant professor in the UC Davis Department of Psychology and Center for Mind and Brain and senior author on the paper.
In structured environments, individual elements are systematically related to each other as they often are in the real world. The study’s insights could be harnessed to improve educational strategies that promote the use of a cognitive map for accelerated learning through inferences, and potentially, approaches to hasten transfer of learning in machine learning in artificial intelligence, Boorman said.
Learning by inference versus association
Most studies of learning have focused on learning by association — how animals learn to associate one thing with another, through trial and error. The difference between what was expected and what actually happened drives learning in such cases.
When there is a hidden structure behind those associations, you can use direct observations to infer indirect, unseen outcomes, leaping ahead of the chain of direct association.
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