Dynamic Interpretations of Covariation Data

Woo kyoung Ahn, Jessecae K. Marsh and Christian C. Luhmann

in Causal Learning

Published in print April 2007 | ISBN: 9780195176803
Published online April 2010 | e-ISBN: 9780199958511 | DOI:

Series: Oxford Series in Cognitive Development

Dynamic Interpretations of Covariation Data

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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

Chapter.  8482 words.  Illustrated.

Subjects: Developmental Psychology

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