Corrigendum: Picking your brains: where and how neuroscience tools can enhance marketing research.

[This corrects the article DOI: 10.3389/fnins.2020.577666.].

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research.

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

A simulation-based procedure to estimate base rates from Covid-19 antibody test results I: Deterministic test reliabilities

We design a procedure (the complete Python code may be obtained at https://github.com/abhishta91/antibody_montecarlo) using Monte Carlo (MC) simulation to establish the point estimators described below and confidence intervals for the base rate of occurence of an attribute (e.g., antibodies against Covid-19) in an aggregate population (e.g., medical care workers) based on a test. The requirements for the procedure are the tests sample size (N) and total number of positives (X), and the data on tests reliability. The modus is the prior which generates the largest frequency of observations in the MC simulation with precisely the number of test positives (maximum-likelihood estimator). The median is the upper bound of the set of priors accounting for half of the total relevant observations in the MC simulation with numbers of positives identical to the tests number of positives. O_LSTOur rather preliminary findings areC_LSTO_LIThe median and the confidence intervals suffice universally. C_LIO_LIThe estimator [Formula] may be outside of the two-sided 95% confidence interval. C_LIO_LIConditions such that the modus, the median and another promising estimator which takes the reliability of the test into account, are quite close. C_LIO_LIConditions such that the modus and the latter estimator must be regarded as logically inconsistent. C_LIO_LIConditions inducing rankings among various estimators relevant for issues concerning over-or underestimation. C_LI JEL-codes: C11, C13, C63