Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
Overview
Affiliations
Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants' leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.
Sargent B, Havens K, Kubo M, Wisnowski J, Wu T, Fetters L Phys Ther. 2021; 102(2).
PMID: 34935956 PMC: 8869361. DOI: 10.1093/ptj/pzab265.
Making the World Behave: A New Embodied Account on Mobile Paradigm.
Sen U, Gredeback G Front Syst Neurosci. 2021; 15:643526.
PMID: 33732116 PMC: 7956955. DOI: 10.3389/fnsys.2021.643526.
Sargent B, Havens K, Wisnowski J, Wu T, Kubo M, Fetters L Phys Ther. 2020; 100(12):2217-2226.
PMID: 32936921 PMC: 7720641. DOI: 10.1093/ptj/pzaa174.
Sensorimotor Contingencies as a Key Drive of Development: From Babies to Robots.
Jacquey L, Baldassarre G, Santucci V, ORegan J Front Neurorobot. 2019; 13:98.
PMID: 31866848 PMC: 6904889. DOI: 10.3389/fnbot.2019.00098.