TY - GEN
T1 - Evaluating Causal Hypotheses
T2 - 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
AU - Rehder, Bob
AU - Davis, Zachary
N1 - Publisher Copyright:
© 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.
PY - 2016
Y1 - 2016
N2 - Although the causal graphical model framework has achieved considerable success accounting for causal learning data, application of that formalism to multi-cause situations assumes that people are insensitive to the statistical properties of the causes themselves. The present experiment tests this assumption by first instructing subjects on a causal model consisting of two independent and generative causes and then requesting them to make data likelihood judgments, that is, to estimate the probability of some data given the model. The correlation between the causes in the data was either positive, zero, or negative. The data was judged as most likely in the positive condition and least likely in the negative condition, a finding that obtained even though all other statistical properties of the data (e.g., causal strengths, outcome density) were controlled. These results pose a problem for current models of causal learning.
AB - Although the causal graphical model framework has achieved considerable success accounting for causal learning data, application of that formalism to multi-cause situations assumes that people are insensitive to the statistical properties of the causes themselves. The present experiment tests this assumption by first instructing subjects on a causal model consisting of two independent and generative causes and then requesting them to make data likelihood judgments, that is, to estimate the probability of some data given the model. The correlation between the causes in the data was either positive, zero, or negative. The data was judged as most likely in the positive condition and least likely in the negative condition, a finding that obtained even though all other statistical properties of the data (e.g., causal strengths, outcome density) were controlled. These results pose a problem for current models of causal learning.
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M3 - Conference contribution
AN - SCOPUS:85085000221
T3 - Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
SP - 1002
EP - 1007
BT - Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
A2 - Papafragou, Anna
A2 - Grodner, Daniel
A2 - Mirman, Daniel
A2 - Trueswell, John C.
PB - The Cognitive Science Society
Y2 - 10 August 2016 through 13 August 2016
ER -