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In the last decade, the field of consumer neuroscience, or neuromarketing, has been flourishing, with numerous publications, academic programs, initiatives, and companies. The demand for objective neural measures to quantify consumers' preferences and predict responses to marketing campaigns is ever on the rise, particularly due to the limitations of traditional marketing techniques, such as questionnaires, focus groups, and interviews. However, research has yet to converge on a unified methodology or conclusive results that can be applied in the industry. In this review, we present the potential of electroencephalography (EEG)‐based preference prediction. We summarize previous EEG research and propose features which have shown promise in capturing the consumers' evaluation process, including components acquired from an event‐related potential design, inter‐subject correlations, hemispheric asymmetry, and various spectral band powers. Next, we review the latest findings on attempts to predict preferences based on various features of the EEG signal. Finally, we conclude with several recommended guidelines for prediction. Chiefly, we stress the need to demonstrate that neural measures contribute to preference prediction beyond what traditional measures already provide. Second, prediction studies in neuromarketing should adopt the standard practices and methodology used in data science and prediction modeling that is common in other fields such as computer science and engineering. This article is categorized under: Economics > Interactive Decision‐Making Economics > Individual Decision‐Making Psychology > Prediction Neuroscience > Cognition

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BackgroundCaenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis.ResultsWe examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation.ConclusionsWorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a “quick and easy,” convenient, high-throughput, and automated solution for nematode research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-017-0477-0) contains supplementary material, which is available to authorized users.

A basic aim of marketing research is to predict consumers' preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects' individual future preferences over the products and the commercials' population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials' effectiveness using a standard questionnaire commonly used in marketing research.We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials' population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects' preferences and marketing campaigns' success.