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The process of evaluating risks and benefits involves a complex neural network that includes the dorsolateral prefrontal cortex (DLPFC). It has been proposed that in conflict and reward situations, theta-band (4–8 Hz) oscillatory activity in the frontal cortex may reflect an electrophysiological mechanism for coordinating neural networks monitoring behavior, as well as facilitating task-specific adaptive changes. The goal of the present study was to investigate the hypothesis that theta-band oscillatory balance between right and left frontal and prefrontal regions, with a predominance role to the right hemisphere (RH), is crucial for regulatory control during decision-making under risk. In order to explore this hypothesis, we used transcranial alternating current stimulation, a novel technique that provides the opportunity to explore the functional role of neuronal oscillatory activities and to establish a causal link between specific oscillations and functional lateralization in risky decision-making situations. For this aim, healthy participants were randomly allocated to one of three stimulation groups (LH stimulation/RH stimulation/Sham stimulation), with active AC stimulation delivered in a frequency-dependent manner (at 6.5 Hz; 1 mA peak-to-peak). During the AC stimulation, participants performed the Balloon Analog Risk Task. This experiment revealed that participants receiving LH stimulation displayed riskier decision-making style compared to sham and RH stimulation groups. However, there was no difference in decision-making behaviors between sham and RH stimulation groups. The current study extends the notion that DLPFC activity is critical for adaptive decision-making in the context of risk-taking and emphasis the role of theta-band oscillatory activity during risky decision-making situations.

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Hunger is a powerful driver of human behavior, and is therefore of great interest to the study of psychology, economics, and consumer behavior. Assessing hunger levels in experiments is often biased, when using self-report methods, or complex, when using blood tests. We propose a novel way of objectively measuring subjects’ levels of hunger by identifying levels of alpha-amylase (AA) enzyme in their saliva samples. We used this measure to uncover the effect of hunger on different types of choice behaviors. We found that hunger increases risk-seeking behavior in a lottery-choice task, modifies levels of vindictiveness in a social decision-making task, but does not have a detectible effect on economic inconsistency in a budget-set choice task. Importantly, these findings were moderated by AA levels and not by self-report measures. We demonstrate the effects hunger has on choice behavior and the problematic nature of subjective measures of physiological states, and propose to use reliable and valid biologically based methods to overcome these problems.

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.

Significant differences in insight held by the two patient groups might be related to age disparities between patient groups. Earlier studies did not adequately address these age differences, their cause and their potential effects on findings. These issues are explored with regard to the findings of the current study, as well as earlier studies, emphasizing the need for further research of the relationship between age and insight.