title: Bayes nets and babies: infants' developing statistical reasoning abilities and their representation of causal knowledge.
abstract: 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.
authors: Department of Cognitive and Linguistic Sciences, Brown University, RI 02912, USA. sobel@cog.brown.edu
authors: David M
authors: DM
authors: Sobel
authors: Natasha Z
authors: NZ
authors: Kirkham
mesh_terms: D001245:Association Learning / Q000502:physiology*; D001499:Bayes Theorem; D002657:Child Development / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D004193:Discrimination Learning / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D008390:Markov Chains; D008960:Models, Psychological; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation