Data Triangulation in Consumer Neuroscience: Integrating Functional Neuroimaging With Meta-Analyses, Psychometrics, and Behavioral Data.

C Clark Cao,Martin Reimann

doi:10.3389/fpsyg.2020.550204

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.

Highlights

Since the dawn of consumer neuroscience, researchers have employed neuroimaging techniques, especially functional magnetic resonance imaging, to explore latent mental processes underlying consumer behavior (e.g., Yoon et al, 2006; Hedgcock and Rao, 2009; Reimann et al, 2010; Meade and Craig, 2012; Plassmann and Weber, 2015)
Consumer neuroscience may hold valuable insights for the advancement of consumer research, with perhaps the most intriguing of these being its potential to (1) reveal hidden cognitive and emotional processes that have been inaccessible to traditional research methods and (2) to confirm physiological and psychological processes underlying consumer behavior
Over the years, consumer researchers have been employing functional magnetic resonance imaging (fMRI), along with other neuroimaging techniques, in an attempt to open the black boxes of consumer experience, motivation, decisionmaking, and so on

Summary

INTRODUCTION

Since the dawn of consumer neuroscience, researchers have employed neuroimaging techniques, especially functional magnetic resonance imaging (fMRI), to explore latent mental processes underlying consumer behavior (e.g., Yoon et al, 2006; Hedgcock and Rao, 2009; Reimann et al, 2010; Meade and Craig, 2012; Plassmann and Weber, 2015). The equation above translates the question of reverse inference into “When the activation of specific brain regions is observed, how likely is it that the cognitive process of interest took place?” To be able to make any meaningful argument based on such reverse inference, consumer researchers need to estimate and, if possible, systematically increase the value of P (COG|ACT) This can be done in multiple ways, as summarized below. These terms describe cognitive states (e.g., “pain,” “working memory,” or “fear”) and are used in the abstract of a paper, so they serve as a proxy of the mental process of interest Based on this database and the meta-analysis provided therein, consumer researchers are able to tell whether there tends to be a non-zero association between the usages of certain terms (e.g., “emotion”) and the activation of a given brain region (e.g., the amygdala) in the extant literature. Discussions and disputes taking place in our parent disciplines, such as cognitive neuroscience and functional neuroimaging, as this paper draws largely upon their lessons and wisdom

CONCLUSION

DATA AVAILABILITY STATEMENT