A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches.
Hannah Solby1, Mia Radovanovic1, Jessica A Sommerville1
1Department of Psychology, University of Toronto, Toronto, ON, Canada.
Frontiers in Psychology
|November 25, 2021
Summary
Infants explore more after failing, not after watching adults. This exploration, linked to physical effort, predicts future problem-solving success.
Area of Science:
- Developmental Psychology
- Cognitive Science
- Behavioral Science
Background:
- Infants learn through exploration and imitation.
- Factors influencing infant choice between exploration and imitation are understudied.
- Imitation and exploration may exist on a continuum.
Purpose of the Study:
- Investigate how adult model effort/success and infant failure experience influence infant problem-solving approach.
- Examine the relationship between exploration, physical effort (force, trying time), and problem-solving success.
Main Methods:
- Applied DeepLabCut software to archival infant problem-solving data.
- Analyzed infant responses to modeled solutions and firsthand failure experiences.
Main Results:
- Infant exploration was not significantly influenced by adult model information.
- Exploration increased after infants experienced failure.
- Increased maximum force and decreased trying time correlated with greater exploration.
- Exploration predicted subsequent problem-solving success.
Conclusions:
- Infant exploration is primarily driven by firsthand failure, not adult models.
- Exploration is part of a motivational framework involving physical effort and success expectations.
- Exploration is a key factor in successful problem-solving development.
Keywords:
DeepLabCutautomated behavioral analysiscognitive developmentexplorationinfant developmentmotion capture technologyproblem solvingMore Related Videos
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