Age-dependent predictors of effective reinforcement motor learning across childhood.

Abstract

Across development, children must learn motor skills such as eating with a spoon and drawing with a crayon. Reinforcement learning, driven by success and failure, is fundamental to such sensorimotor learning. It typically requires a child to explore movement options along a continuum (grip location on a crayon) and learn from probabilistic rewards (whether the crayon draws or breaks). Here, we studied the development of reinforcement motor learning using online motor tasks to engage children aged 3 to 17 and adults (cross-sectional sample, N=385). Participants moved a cartoon penguin across a scene and were rewarded (animated cartoon clip) based on their final movement position. Learning followed a clear developmental trajectory when participants could choose to move anywhere along a continuum and the reward probability depended on final movement position. Learning was incomplete or absent in 3 to 8-year-olds and gradually improved to adult-like levels by adolescence. A reinforcement learning model fit to each participant identified three age-dependent factors underlying improvement: amount of exploration after a failed movement, learning rate, and level of motor noise. We predicted, and confirmed, that switching to discrete targets and deterministic reward would improve 3 to 8-year-olds' learning to adult-like levels by increasing exploration after failed movements. Overall, we show a robust developmental trajectory of reinforcement motor learning abilities under ecologically relevant conditions i.e., continuous movement options mapped to probabilistic reward. This learning appears to be limited by immature spatial processing and probabilistic reasoning abilities in young children and can be rescued by reducing the demands in these domains.