Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance

Taisei Sugiyama, Nicolas Schweighofer and Jun Izawa ()
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Taisei Sugiyama: University of Tsukuba
Nicolas Schweighofer: University of Southern California
Jun Izawa: University of Tsukuba

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Humans and animals develop learning-to-learn strategies throughout their lives to accelerate learning. One theory suggests that this is achieved by a metacognitive process of controlling and monitoring learning. Although such learning-to-learn is also observed in motor learning, the metacognitive aspect of learning regulation has not been considered in classical theories of motor learning. Here, we formulated a minimal mechanism of this process as reinforcement learning of motor learning properties, which regulates a policy for memory update in response to sensory prediction error while monitoring its performance. This theory was confirmed in human motor learning experiments, in which the subjective sense of learning-outcome association determined the direction of up- and down-regulation of both learning speed and memory retention. Thus, it provides a simple, unifying account for variations in learning speeds, where the reinforcement learning mechanism monitors and controls the motor learning process.

Date: 2023
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DOI: 10.1038/s41467-023-39536-9

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