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This study provides an overview of the evolution of the body of knowledge, current research streams, potential new domains, and theoretical models of interest for neuromarketing research developed in the field of communication. Neuromarketing is defined here as research applying psychophysiological or neuroscience methods in communication. We particularly implemented a comprehensive analysis of 861 publications using keyword cooccurrence analysis and science mapping tools. The performance analysis shows a strong growth in the number of publications over the last decade, with a slight declining impact in terms of citations after reaching a peak around the turn of the century. Contributions published in journals in business and communication fields (and not psychology or neuroimaging) are those with the largest average citations per year. The evolution diagrams highlight that the use of brain imaging tools to study ad persuasion in virtual environments and social marketing contexts (e.g., health or sustainable communication), as well as the employment of novel metrics (neural synchronization) and deep learning methods to analyze data, constitute fruitful research streams in the application of neuromarketing tools in communication research. These results offer communication scholars an accurate insight on recent scientific research applying neuromarketing that can shape further studies.

What drives the liking of video advertisements? The authors analyzed neural signals during ad exposure from three functional magnetic resonance imaging (fMRI) data sets (113 participants from two countries watching 85 video ads) with automated meta-analytic decoding (Neurosynth). These brain-based measures of psychological processes—including perception and language (information processing), executive function and memory (cognitive functions), and social cognition and emotion (social-affective response)—predicted subsequent self-report ad liking, with emotion and memory being the earliest predictors after the first three seconds. Over the span of ad exposure, while the predictiveness of emotion peaked early and fell, that of social cognition had a peak-and-stable pattern, followed by a late peak of predictiveness in perception and executive function. At the aggregate level, neural signals—especially those associated with social-affective response—improved the prediction of out-of-sample ad liking compared with traditional anatomically based neuroimaging analysis and self-report liking. Finally, earlyonset social-affective response predicted population ad liking in a behavioral replication. Overall, this study helps delineate the psychological mechanisms underlying ad processing and ad liking and proposes a novel neuroscience-based approach for generating psychological insights and improving out-of-sample predictions.

In daily life we regularly must decide whether to act dishonestly for personal gain or to be honest and maintain a positive image of ourselves. While evidence suggests that acute stress influences moral decisions, it is unclear whether stress increases or decreases immoral behavior. Here, we hypothesize that stress, through its effects on cognitive control, has different effects on moral decision making for different individuals, depending on their moral default. We test this hypothesis by combining a task which allows for inconspicuously measuring spontaneous cheating with a well-established stress induction task. Our findings confirm our hypothesis, revealing that effects of stress on dishonesty are not uniform, but instead depend on the individual: for those who are relatively dishonest, stress increases dishonesty, whereas for participants who are relatively honest stress makes them more honest. These findings go a long way in resolving the conflicting findings in the literature on the effects of stress on moral decisions, suggesting that stress affects dishonesty differently for different individuals, depending on their moral default.

The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.

Self-control is of vital importance for human wellbeing. Hare et al. (2009) were among the first to provide empirical evidence on the neural correlates of self-control. This seminal study profoundly impacted theory and empirical work across multiple fields. To solidify the empirical evidence supporting self-control theory, we conducted a preregistered replication of this work. Further, we tested the robustness of the findings across analytic strategies. Participants underwent functional magnetic resonance imaging while rating 50 food items on healthiness and tastiness and making choices about food consumption. We closely replicated the original analysis pipeline and supplemented it with additional exploratory analyses to follow-up on unexpected findings and to test the sensitivity of results to key analytical choices. Our replication data provide support for the notion that decisions are associated with a value signal in ventromedial prefrontal cortex (vmPFC), which integrates relevant choice attributes to inform a final decision. We found that vmPFC activity was correlated with goal values regardless of the amount of self-control and it correlated with both taste and health in self-controllers but only taste in non-self-controllers. We did not find strong support for the hypothesized role of left dorsolateral prefrontal cortex (dlPFC) in self-control. The absence of statistically significant group differences in dlPFC activity during successful self-control in our sample contrasts with the notion that dlPFC involvement is required in order to effectively integrate longer-term goals into subjective value judgments. Exploratory analyses highlight the sensitivity of results (in terms of effect size) to the analytical strategy, for instance, concerning the approach to region-of-interest analysis.

Numerous studies demonstrate that moment-to-moment neural variability is behaviorally relevant and beneficial for tasks and behaviors requiring cognitive flexibility. However, it remains unclear whether the positive effect of neural variability also holds for cognitive persistence. Moreover, different brain variability measures have been used in previous studies, yet comparisons between them are lacking. In the current study, we examined the association between resting-state BOLD signal variability and two metacontrol policies (i.e., persistence vs. flexibility). Brain variability was estimated from resting-state fMRI (rsfMRI) data using two different approaches (i.e., Standard Deviation (SD), and Mean Square Successive Difference (MSSD)) and metacontrol biases were assessed by three metacontrol-sensitive tasks. Results showed that brain variability measured by SD and MSSD was highly positively related. Critically, higher variability measured by MSSD in the attention network, parietal and frontal network, frontal and ACC network, parietal and motor network, and higher variability measured by SD in the parietal and motor network, parietal and frontal network were associated with reduced persistence (or greater flexibility) of metacontrol (i.e., larger Stroop effect or worse RAT performance). These results show that the beneficial effect of brain signal variability on cognitive control depends on the metacontrol states involved. Our study highlights the importance of temporal variability of rsfMRI activity in understanding the neural underpinnings of cognitive control.

Measurement of the determinants of socially undesirable behaviors, such as dishonesty, are complicated and obscured by social desirability biases. To circumvent these biases, we used connectome-based predictive modeling (CPM) on resting state functional connectivity patterns in combination with a novel task which inconspicuously measures voluntary cheating to gain access to the neurocognitive determinants of (dis)honesty. Specifically, we investigated whether task-independent neural patterns within the brain at rest could be used to predict a propensity for (dis)honest behavior. Our analyses revealed that functional connectivity, especially between brain networks linked to self-referential thinking (vmPFC, temporal poles, and PCC) and reward processing (caudate nucleus), reliably correlates, in an independent sample, with participants' propensity to cheat. Participants who cheated the most also scored highest on several self-report measures of impulsivity which underscores the generalizability of our results. Notably, when comparing neural and self-report measures, the neural measures were found to be more important in predicting cheating propensity.

Dishonesty is ubiquitous and imposes substantial financial and social burdens on society. Intuitively, dishonesty results from a failure of willpower to control selfish behavior. However, recent research suggests that the role of cognitive control in dishonesty is more complex. We review evidence that cognitive control is not needed to be honest or dishonest per se, but that it depends on individual differences in what we call one's 'moral default': for those who are prone to dishonesty, cognitive control indeed aids in being honest, but for those who are already generally honest, cognitive control may help them cheat to occasionally profit from small acts of dishonesty. Thus, the role of cognitive control in (dis)honesty is to override the moral default.

Self-control is of vital importance for human wellbeing. Hare et al. (2009) were among the first to provide empirical evidence on the neural correlates of self-control. This seminal study profoundly impacted theory and empirical work across multiple fields. To solidify the empirical evidence supporting self-control theory, we conducted a preregistered replication of this work. Further, we tested the robustness of the findings across analytic strategies. Participants underwent functional magnetic resonance imaging while rating 50 food items on healthiness and tastiness and making choices about food consumption. We closely replicated the original analysis pipeline and supplemented it with additional exploratory analyses to follow-up on unexpected findings and to test the sensitivity of results to key analytical choices. Our replication data provide support for the notion that decisions are associated with a value signal in ventromedial prefrontal cortex (vmPFC), which integrates relevant choice attributes to inform a final decision. We found that vmPFC activity was correlated with goal values regardless of the amount of self-control and it correlated with both taste and health in self-controllers but only taste in non-self-controllers. We did not find strong support for the hypothesized role of left dorsolateral prefrontal cortex (dlPFC) in self-control. The absence of statistically significant group differences in dlPFC activity during successful self-control in our sample contrasts with the notion that dlPFC involvement is required in order to effectively integrate longer-term goals into subjective value judgments. Exploratory analyses highlight the sensitivity of results (in terms of effect size) to the analytical strategy, for instance, concerning the approach to region-of-interest analysis.

The precise role of cognitive control in dishonesty has been debated for many years, but now important strides have been made to resolve this debate. Recently developed paradigms that allow for investigating dishonesty on the level of the choice rather than on the level of the individual have substantially improved our understanding of the adaptive role of cognitive control in (dis)honesty. These new paradigms revealed that the role of cognitive control differs across people: for cheaters, it helps them to sometimes be honest, while for those who are generally honest, it allows them to cheat on occasion. Thus, cognitive control is not required for (dis)honesty per se but is required to override one’s moral default to be either honest or to cheat. Individual differences in moral default are driven by balancing motivation for reward and upholding a moral self-image. Dishonesty is ubiquitous and imposes substantial financial and social burdens on society. Intuitively, dishonesty results from a failure of willpower to control selfish behavior. However, recent research suggests that the role of cognitive control in dishonesty is more complex. We review evidence that cognitive control is not needed to be honest or dishonest per se, but that it depends on individual differences in what we call one’s ‘moral default’: for those who are prone to dishonesty, cognitive control indeed aids in being honest, but for those who are already generally honest, cognitive control may help them cheat to occasionally profit from small acts of dishonesty. Thus, the role of cognitive control in (dis)honesty is to override the moral default.