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Licensed Unlicensed Requires Authentication Published by De Gruyter November 19, 2015

Are Consumers as Constrained as Hens are Confined? Brain Activations and Behavioral Choices after Informational Influence

Alex J. Francisco, Amanda S. Bruce, John M. Crespi, Jayson L. Lusk, Brandon McFadden, Jared M. Bruce, Robin L. Aupperle and Seung-Lark Lim


In 2008, California passed Proposition 2, specifying confinement space for certain farm animals. Proposition 2 went into full effect January 2015 and has significant implications for egg production in California and possibly even interstate commerce. We examined the influence of promotional videos aired during the campaign on consumers’ willingness-to-pay for eggs produced in a more open production system (i.e., cage-free, free range) and corresponding neurofunctional activations during decisions. Forty-six participants (24 females), aged 18–55 years (M=29.65), were enrolled and performed a food decision-making task during fMRI scanning. In each decision, two options of identical one dozen cartons of eggs were presented simultaneously. Below each option were two attributes, describing price and production method. Cage free and free-range eggs were more expensive, at varying degrees. Participants were randomized to one of three 30-second video groups: pro-Proposition 2, anti-Proposition 2, and a Neutral flowing stream. Based on a whole brain analysis, participants in the pro-Proposition 2 video group (N=16) demonstrated significantly greater activations post-video compared to pre-video in left insular cortex and right occipital cortex. This change in insula activity may be indicative of increased social risk involved with the purchase of closed production method eggs, driving participants to increase their percentage of decisions to purchase the higher priced, open-method eggs. It is possible that the insula activation indicates that consumers are constrained to choosing the eggs produced under open-cage production methods, after viewing advertisements advocating for Proposition 2.

Funding statement: Funding United States Department of Agriculture (Grant/Award Number: 2011-67023-30047)


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fMRI Data Acquisition

Functional scans took place at the University of Kansas Medical Center’s Hoglund Brain Imaging Center on a 3-Tesla Siemens Skyra (Siemens, Erlangen, Germany) scanner. A structural T1-weighted, three-dimensional, magnetization-prepared rapid acquisition with gradient echo (MPRAGE) structural images were acquired (repetition time/echo time [TR/TE]=23/4 ms, flip angle=8°, field of view [FOV]=256 mm, matrix=256×192, slice thickness=1 mm) was carried out following automated scout image acquisition and shimming procedures performed to optimize field homogeneity. Two gradient-echo blood-oxygen-level-dependent (BOLD) functional scans were acquired in fifty contiguous, oblique, 40° axial slices (TR/TE=3,000/25 ms, flip angle=90°, FOV=232 mm, matrix=80×80, slice thickness=3 mm, in-plane resolution=2.9×2.9 mm, 176 data points). Participants were positioned in a manner so that the anterior commissure-posterior commissure (AC-PC) plane fell between 17° and 22° in scanner coordinate space. Using this procedure, assured the 40° acquisition angle was applied uniformly for all subjects, in order to minimize artifacts while standardizing the head positions of participants

fMRI data were analyzed using BrainVoyager QX, version 2.4 (Brain Innovation, Maastricht, Netherlands, 2012). Preprocessing steps included trilinear, three-dimensional motion correction, sinc-interpolated slice scan time correction, two-dimensional spatial smoothing with a four-millimeter Gaussian filter, and high-pass filter temporal smoothing. Functional images were realigned to fit structural images obtained during each scanning session, then normalized to the BrainVoyager template image, which corresponds to Talairach and Tournoux’s (1988) stereotaxic atlas. Neural activation maps were analyzed using statistical parametric methods (Friston et al. 1995) inside the BrainVoyager QX package. Contrasts of neural activity in the experimental conditions of interest were conducted using multiple-regression analysis. Regressors representing neural activation in these conditions, as well as regressors of non-interest, were modeled with a hemodynamic response filter. Finally, a group analysis was performed by entering data into the multiple-regression analysis using a random effects model.

Published Online: 2015-11-19
Published in Print: 2015-1-1

©2015 by De Gruyter

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