When correlation equals causation: A behavioral and computational account of second-order correlation learning in children.

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ID: 204869
2020
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Abstract
We examined 2- and 3-year-old children's ability to use second-order correlation learning-in which a learned correlation between two pairs of features (e.g., A and B, A and C) is generalized to the noncontiguous features (i.e., B and C)-to make causal inferences. Previous findings showed that 20- and 26-month-old children can use second-order correlation learning to learn about static and dynamic features in category and noncategory contexts. The current behavioral study and computational model extend these findings to show that 2- and 3-year-olds can detect the second-order correlation between an object's surface feature and its capacity to activate a novel machine, but only if the children had encoded the first-order correlations on which the second-order correlation was based. These results have implications for children's developing information-processing capacities on their ability to use second-order correlations to infer causal relations in the world.
Reference Key
benton2020whenjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Benton, Deon T;Rakison, David H;Sobel, David M;
Journal journal of experimental child psychology
Year 2020
DOI
S0022-0965(20)30462-8
URL
Keywords
neural networks computational modeling causal learning causal reasoning causal representations second-order correlation learning

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