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Rich analysis and rational models: inferring individual behavior from infant looking data
Author(s) -
Piantadosi Steven T.,
Kidd Celeste,
Aslin Richard
Publication year - 2014
Publication title -
developmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.801
H-Index - 127
eISSN - 1467-7687
pISSN - 1363-755X
DOI - 10.1111/desc.12083
Subject(s) - psychology , cognition , probabilistic logic , bayesian probability , rational analysis , cognitive psychology , stimulus (psychology) , parametric statistics , bayesian inference , statistical model , machine learning , artificial intelligence , developmental psychology , computer science , statistics , mathematics , neuroscience
Studies of infant looking times over the past 50 years have provided profound insights about cognitive development, but their dependent measures and analytic techniques are quite limited. In the context of infants' attention to discrete sequential events, we show how a Bayesian data analysis approach can be combined with a rational cognitive model to create a rich data analysis framework for infant looking times. We formalize (i) a statistical learning model, (ii) a parametric linking between the learning model's beliefs and infants' looking behavior, and (iii) a data analysis approach and model that infers parameters of the cognitive model and linking function for groups and individuals. Using this approach, we show that recent findings from Kidd, Piantadosi and Aslin ([Kidd, C., 2012]) of a U‐shaped relationship between look‐away probability and stimulus complexity even holds within infants and is not due to averaging subjects with different types of behavior. Our results indicate that individual infants prefer stimuli of intermediate complexity, reserving attention for events that are moderately predictable given their probabilistic expectations about the world.