A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches

Frontiers in Psychology

Jessica A. Sommerville¹  Mia Radovanovic¹  Hannah Solby² 

[1] Department of Psychology, University of Toronto, Toronto, ON, Canada;null;

关键词: cognitive development;    exploration;    infant development;    motion capture technology;    automated behavioral analysis;    problem solving;    DeepLabCut;   

DOI  :  10.3389/fpsyg.2021.705108

来源: Frontiers

【 摘 要 】

When confronted with novel problems, problem-solvers must decide whether to copy a modeled solution or to explore their own unique solutions. While past work has established that infants can learn to solve problems both through their own exploration and through imitation, little work has explored the factors that influence which of these approaches infants select to solve a given problem. Moreover, past work has treated imitation and exploration as qualitatively distinct, although these two possibilities may exist along a continuum. Here, we apply a program novel to developmental psychology (DeepLabCut) to archival data (Lucca et al., 2020) to investigate the influence of the effort and success of an adult’s modeled solution, and infants’ firsthand experience with failure, on infants’ imitative versus exploratory problem-solving approaches. Our results reveal that tendencies toward exploration are relatively immune to the information from the adult model, but that exploration generally increased in response to firsthand experience with failure. In addition, we found that increases in maximum force and decreases in trying time were associated with greater exploration, and that exploration subsequently predicted problem-solving success on a new iteration of the task. Thus, our results demonstrate that infants increase exploration in response to failure and that exploration may operate in a larger motivational framework with force, trying time, and expectations of task success.

【 授权许可】

CC BY