Measuring Supernatural Belief Implicitly Using the Affect Misattribution Procedure

Robert M. Ross, Jazmin L. Brown-Iannuzzi, Will M. Gervais, Jonathan Jong, Jonathan A. Lanman, Ryan McKay, Gordon Pennycook

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Abstract

Asking about religious beliefs, or lack thereof, is a sensitive and complex issue. Due to cultural norms, people may be motivated to respond in a socially desirable way. In addition, deliberating about beliefs may yield different responses than intuition-based responses. To develop a better understanding of the relationship between intuition and self-reported belief, we developed a new implicit measure of supernatural belief. Specifically, we adapted the Affective Misattribution Procedure (AMP) to measure supernatural belief. In a preregistered online study of 404 American participants, we found that the strength of associations between supernatural entities (e.g., god, devil, heaven) and the concept “real” (as opposed to the concept “imaginary”) predicted self-reported supernatural belief and self-reported religious behavior, and these associations were of comparable magnitude to those found in studies where supernatural belief was measured implicitly using the Implicit Association Test (IAT). These results provide provisional evidence that the AMP can be used as an implicit measure of supernatural belief.
Original languageEnglish
Number of pages14
JournalReligion, Brain, and Behavior
Early online date24 Jun 2019
DOIs
Publication statusEarly online date - 24 Jun 2019

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Ross, Robert M. ; Brown-Iannuzzi, Jazmin L. ; Gervais, Will M. ; Jong, Jonathan ; Lanman, Jonathan A. ; McKay, Ryan ; Pennycook, Gordon. / Measuring Supernatural Belief Implicitly Using the Affect Misattribution Procedure. In: Religion, Brain, and Behavior. 2019.
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Measuring Supernatural Belief Implicitly Using the Affect Misattribution Procedure. / Ross, Robert M.; Brown-Iannuzzi, Jazmin L.; Gervais, Will M.; Jong, Jonathan; Lanman, Jonathan A.; McKay, Ryan; Pennycook, Gordon.

In: Religion, Brain, and Behavior, 24.06.2019.

Research output: Contribution to journalArticle

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