Causal Learning from Biased Sequences
نویسندگان
چکیده
Multiple psychological theories of causal learning provide case-by-case updating rules: given my current causal beliefs about the world and a novel case, how should I change those beliefs? Most of these theories predict some type of order effect: biased and unbiased sequences of cases will lead to different final causal beliefs, even if the overall statistics are identical. This paper describes an experiment that (i) finds only small order effects that (ii) are not dependent on the number of observed cases, and in which (iii) observed patterns of belief change during the sequences are not explained by various proposed algorithmic theories.
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تاریخ انتشار 2005