Assessing Consumer Demand with Noisy Neural Measurements
42 Pages Posted: 24 May 2018 Last revised: 9 Oct 2020
Date Written: February 10, 2019
Recent studies have used the random utility framework to examine whether neural data can assess and predict demand for consumer products, both within and across individuals. However the effectiveness of this methodology has been limited by the large degree of measurement error in neural data. The resulting “error-in-variables” problem severely biases the estimates of the relationship between neural measurements and choice behaviour, thus limiting the role such data can play in assessing marginal contributions to utility. In this article, we propose a method for controlling for this large degree of measurement error in value regions of the brain. We propose that additional neural variables from areas of the brain that are unrelated to valuation can serve as “proxies” for the measurement error in value regions, substantially alleviating the bias in model estimates. We also demonstrate that standard methods for dealing with the error-in-variables problem (instrumental variables) are limited in the context of neural data. We demonstrate the feasibility of our proposed method on an existing dataset of fMRI measurements and consumer choices. After controlling for measurement error, we find a considerable reduction in the variation of estimates across consumers.
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