Non-bayesian inference: causal structure trumps correlation. Bes, B., Sloman, S., Lucas, C. G., & Raufaste, E. Cogn Sci, 36(7):1178–1203, 2012. doi abstract bibtex The study tests the hypothesis that conditional probability judgments can be influenced by causal links between the target event and the evidence even when the statistical relations among variables are held constant. Three experiments varied the causal structure relating three variables and found that (a) the target event was perceived as more probable when it was linked to evidence by a causal chain than when both variables shared a common cause; (b) predictive chains in which evidence is a cause of the hypothesis gave rise to higher judgments than diagnostic chains in which evidence is an effect of the hypothesis; and (c) direct chains gave rise to higher judgments than indirect chains. A Bayesian learning model was applied to our data but failed to explain them. An explanation-based hypothesis stating that statistical information will affect judgments only to the extent that it changes beliefs about causal structure is consistent with the results.
@Article{Bes2012,
author = {Bes, B\'en\'edicte and Sloman, Steven and Lucas, Christopher G. and Raufaste, Eric},
journal = {Cogn Sci},
title = {Non-bayesian inference: causal structure trumps correlation.},
year = {2012},
number = {7},
pages = {1178--1203},
volume = {36},
abstract = {The study tests the hypothesis that conditional probability judgments
can be influenced by causal links between the target event and the
evidence even when the statistical relations among variables are
held constant. Three experiments varied the causal structure relating
three variables and found that (a) the target event was perceived
as more probable when it was linked to evidence by a causal chain
than when both variables shared a common cause; (b) predictive chains
in which evidence is a cause of the hypothesis gave rise to higher
judgments than diagnostic chains in which evidence is an effect of
the hypothesis; and (c) direct chains gave rise to higher judgments
than indirect chains. A Bayesian learning model was applied to our
data but failed to explain them. An explanation-based hypothesis
stating that statistical information will affect judgments only to
the extent that it changes beliefs about causal structure is consistent
with the results.},
doi = {10.1111/j.1551-6709.2012.01262.x},
language = {eng},
medline-pst = {ppublish},
pmid = {22734828},
school = {Laboratoire CLLE-LTC, Universit\'e de Toulouse, Pittsburgh.},
timestamp = {2012.11.19},
}
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