Data Triangulation in Consumer Neuroscience: Integrating Functional Neuroimaging With Meta-Analyses, Psychometrics, and Behavioral Data
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FRONTIERS MEDIA SACitation
Cao, C. C., & Reimann, M. (2020). Data Triangulation in Consumer Neuroscience: Integrating Functional Neuroimaging With Meta-Analyses, Psychometrics, and Behavioral Data. Frontiers in Psychology, 11.Journal
FRONTIERS IN PSYCHOLOGYRights
Copyright © 2020 Cao and Reimann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
This article reviews a wide range of functional magnetic resonance imaging (fMRI) studies conducted in the field of consumer neuroscience to (1) highlight common interpretative approaches of neuroimaging data (i.e., forward inference and reverse inference), (2) discuss potential interpretative issues associated with these approaches, and (3) provide a framework that employs a multi-method approach aimed to possibly raise the explanatory power and, thus, the validity of functional neuroimaging research in consumer neuroscience. Based on this framework, we argue that the validity of fMRI studies can be improved by the triangulation of (1) careful design of neuroimaging studies and analyses of data, (2) meta-analyses, and (3) the integration of psychometric and behavioral data with neuroimaging data. Guidelines on when and how to employ triangulation methods on neuroimaging data are included. Moreover, we also included discussions on practices and research directions that validate fMRI studies in consumer neuroscience beyond data triangulation.Note
Open access journalISSN
1664-1078PubMed ID
33224048Version
Final published versionae974a485f413a2113503eed53cd6c53
10.3389/fpsyg.2020.550204
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Except where otherwise noted, this item's license is described as Copyright © 2020 Cao and Reimann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).