Related Experiment Video
Updated: Nov 16, 2025
07:31
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
Published on: February 8, 2019
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How do infants start learning object names in a sea of clutter?
Hadar Karmazyn Raz1, Drew H Abney1, David Crandall1
1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405 USA.
Summary
Infants learn object names by focusing on a few toys, creating a Zipfian distribution. This strategy helps overcome visual clutter and ambiguity, improving name-object co-occurrence learning.
Area of Science:
- Developmental Psychology
- Cognitive Science
- Machine Learning
Background:
- Infants readily learn name-object associations through distributional cues.
- Naturalistic learning environments present challenges like visual clutter and referential ambiguity.
Purpose of the Study:
- To investigate how infants learn name-object co-occurrences in cluttered, naturalistic settings.
- To explore the role of object selection in overcoming referential ambiguity.
Main Methods:
- Integrated a naturalistic behavioral study of infant-parent toy interactions.
- Applied a machine learning model trained on object image distributions.
Main Results:
- Infants and parents focused on a limited set of toys during play.
- This selective play resulted in a Zipfian frequency distribution of objects.
- A machine learning model trained on Zipf-like distributions outperformed one trained on uniform distributions.
Conclusions:
- Infants may overcome referential ambiguity by selecting a few objects.
- This selective focus facilitates learning distributional cues for name-object pairs.
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