Comparing human metalearning to AI

Researchers from the University of Tsukuba have compared the metarecognition of artificial intelligence to that of humans.

‘Metarecognition’ is the ability to objectively monitor, control and improve one’s learning ability. The researchers demonstrated that the human brain exhibits metacognitive abilities that regulate implicit motor learning to maximise monetary rewards.

Artificial intelligence differs in that it is perpetually optimal, while human metacognition exhibits an asymmetric bias in managing rewards (monetary gain) and punishments (monetary loss).

Monitoring and controlling one’s own learning process objectively is essential for improving one’s learning abilities. This ability, often referred to as “learning to learn” or “metacognition”, has been studied in educational psychology. Owing to the tight coupling between the higher meta-level and the lower object-level cognitive systems, a conventional reduction approach has difficulty understanding the neural basis of metacognition. To overcome this limitation, the researchers employed a novel research approach where they compared the metacognition of artificial intelligence (AI) to that of humans.

First, they demonstrated that the metacognitive system of AI, which aims to maximise rewards and minimise punishments, can effectively regulate learning speed and memory retention in response to the environment and task. Second, they demonstrated the metacognitive behaviour of human motor learning, which demonstrates that providing monetary feedback as a function of memory can either promote or suppress motor learning and memory retention.

Notably, while AI exhibited equal metacognitive abilities for reward and punishment, humans exhibited an asymmetric response to monetary gain and loss; humans adjust their memory retention in response to gain and their learning speed in response to loss. This asymmetric property may provide valuable insights into the neural mechanisms underlying human metacognition.

Researchers anticipate that these findings could be effectively applied to enhance the learning abilities of individuals engaging in new sports or motor-related activities, such as post-stroke rehabilitation training.

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