ERIC Number: EJ849127
Record Type: Journal
Publication Date: 2007-May
Abstractor: As Provided
Reference Count: 54
Bayes Nets and Babies: Infants' Developing Statistical Reasoning Abilities and Their Representation of Causal Knowledge
Sobel, David M.; Kirkham, Natasha Z.
Developmental Science, v10 n3 p298-306 May 2007
A fundamental assumption of the causal graphical model framework is the Markov assumption, which posits that learners can discriminate between two events that are dependent because of a direct causal relation between them and two events that are independent conditional on the value of another event(s). Sobel and Kirkham (2006) demonstrated that 8-month-old infants registered conditional independence information among a sequence of events; infants responded according to the Markov assumption in such a way that was inconsistent with models that rely on simple calculations of associative strength. The present experiment extends these findings to younger infants, and demonstrates that such responses potentially develop during the second half of the first year of life. These data are discussed in terms of a developmental trajectory between associative mechanisms and causal graphical models as representations of infants' causal and statistical learning.
Descriptors: Markov Processes, Infants, Metacognition, Thinking Skills, Probability, Statistical Analysis, Models, Experiments
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
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