Flexibility and generativity are fundamental aspects of functional behavior that begin in infancy and improve with experience. How do infants learn to tailor their real-time solutions to variations in local conditions? On a nativist view, the developmental process begins with innate prescribed solutions, and experience elaborates on those solutions to suit variations in the body and the environment. On an emergentist view, infants begin by generating a variety of strategies indiscriminately, and experience teaches them to select solutions tailored to the current relations between their body and the environment. To disentangle these accounts, we observed coordination patterns in 11-month-old pre-walking infants with a range of cruising (moving sideways in an upright posture while holding onto a support) and crawling experience as they cruised over variable distances between two handrails they held for support. We identified infants' coordination patterns using a novel combination of computer-vision, machine-learning, and time-series analyses. As predicted by the emergentist view, the least experienced infants generated multiple coordination patterns inconsistently regardless of body size and handrail distance, whereas the most experienced infants tailored their coordination patterns to body-environment relations and switched solutions only when necessary. Moreover, the beneficial effects of experience were specific to cruising and not crawling, although both skills involve anti-phase coordination among the four limbs. Thus, findings support an emergentist view and suggest that everyday experience with the target skill may promote "learning to learn," where infants learn to assemble the appropriate solution for new problems on the fly.
Keywords: artificial intelligence; behavioral flexibility; computer vision; cruising; infants; limb coordination; locomotion; machine learning; motor development; problem solving.
Published by Elsevier Inc.