Using domain-general principles to explain children's causal reasoning abilities.
A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed 'causal properties' and is capable of making several types of inferences that 4-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.
McClelland
James L
JL
Department of Psychology, Carnegie Mellon University, Pittsburgh , USA. jlm@psych.stanford.edu
Thompson
Richard M
RM
D001245:Association Learning / Q000502:physiology*; D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000502:physiology; D002675:Child, Preschool; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D008960:Models, Psychological