Information Search in an Autocorrelated Causal Learning Environment

Benjamin RottmanUniversity of Pittsburgh

Abstract

When trying to determine which of two causes produces a more desirable outcome, if the outcome is autocorrelated (goes through higher and lower periods) it is critical to switch back and forth between the causes. If one first tries Cause 1, and then tries Cause 2, it is likely that an autocorrelated outcome would appear to change with the second cause even though it is merely undergoing normal change over time. Experiment 1 found that people tend to perseverate rather than alternate when testing the effectiveness of causes, and perseveration is associated with substantial errors in judgment. Experiment 2 found that forcing people to alternate improves judgment. This research suggests that a debiasing approach to teach people when to alternate may be warranted to improve causal learning.

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Information Search in an Autocorrelated Causal Learning Environment (402 KB)



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