Abstract

Children’s causal reasoning about machines and their components is crucial for acquiring knowledge about science and math. Mechanistic understanding involves knowing how parts can be effectively connected to work together as a functional mechanical system. Little is known about how children’s understanding of mechanical systems develops across the lifespan. Legare and Lombrozo (2014) developed a gears assembly task to assess children’s understanding and learning of mechanics but did not track this skill across ages. The present study adapted the gears assembly task to measure mechanistic understanding in children between the ages of 4 to 11 years and adults. The goal of the present study is to identify at what age performance on the task becomes adult-like. A total of 10 adults and 48 children across six age groups between age 4 to 11 years were tested on three phases of the task: mechanism, reconstruction and generalization. Percent accuracy scores were compared between each child age group versus adults with independent t-tests. The mechanism phase was at ceiling for all age groups. For the reconstruction task, three age groups between 4 to 7 years of age performed significantly worse than adults. For the generalization task, only the youngest children tested between ages 4 to 5 years old performed significantly worse than adults, while groups over age 5 to 11 did not differ from adults. These findings suggest that mechanical understanding is relatively adult-like by age 6 years. A questionnaire was used to explore whether children’s exposure to science toys at home influenced their performance. Results from a multiple regression showed that home STEM-toy engagement, gender, and parent education were not significant predictors of performance on the gears assembly task.

Library of Congress Subject Headings

Mechanics--Study and teaching (Early childhood)--Research; Reasoning in children; Gears

Publication Date

10-20-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Experimental Psychology (MS)

College

College of Liberal Arts

Advisor

Rain Bosworth

Advisor/Committee Member

Allison Fitch

Advisor/Committee Member

Matthew Dye

Campus

RIT – Main Campus

Plan Codes

EXPSYC-MS

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