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Causal LearningPsychology, Philosophy, and Computation$
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Alison Gopnik and Laura Schulz

Print publication date: 2007

Print ISBN-13: 9780195176803

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195176803.001.0001

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Dynamic Interpretations of Covariation Data

Dynamic Interpretations of Covariation Data

(p.280) 17 Dynamic Interpretations of Covariation Data
Causal Learning

Woo kyoung Ahn

Jessecae K. Marsh

Christian C. Luhmann

Oxford University Press

Many models of causal induction are based on covariation information, which depicts whether the presence or absence of an event co-occurs with the presence or absence of another event. In all covariation-based models of causal induction, events that are classified as the same type play an identical role throughout learning. This chapter reviews three sets of studies demonstrating that people treat the same type of evidence differently depending on at what point during learning the evidence is presented. The major thesis is that people develop a hypothesis about causal relations based on a few pieces of initial evidence and interpret the subsequent data in light of this hypothesis. Thus, depending on what the initial hypothesis is and when the data are presented, the identical data can play different roles. Such dynamic interpretations of data result in the primacy effect, varying inferences about unobserved, alternative causes, and the context-dependent interpretations of ambiguous stimuli.

Keywords:   causal induction, covariation, hidden cause, ambiguous information, reasoning, causal reasoning, primacy effect, unobserved cause

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