Real-Time Planning in Problem Solving

Problem solving is essential to functional manual actions—how to open a latch or grasp the handle of a hammer. Most developmental research focuses on the ages at which children successfully solve various problems.

This outcome-oriented approach established that problem solving begins in infancy and improves with age and experience. But it only addresses the question of when children solve particular problems, not the process of how individual children do it.

 

My work reveals the real-time mechanisms underlying the development of problem solving and the factors that foster the efficiency of these mechanisms. I recently developed a uniquely powerful setup that combines head-mounted eye-tracking, EEG, motion tracking, detailed video-coding, and AI methods (signal processing, unsupervised and supervised machine learning) to record and analyze perception, neural patterns, and motor behavior across development during tool use and object fitting. This novel setup revealed that developmental changes in problem solving stem from all phases of the planning process—starting with visual attention, proceeding to neural processing, and ending in the straightness of the path the hand makes while initiating the reaching motion. In other words, children’s planning errors propagate from their first glimpse of the object onward.

 

My child-friendly tasks, cutting-edge technologies, neural recordings, and machine-learning algorithms showed a cascade in planning that provides insights into what drives efficiency over development and cannot be found in previous practices used in cognitive science.