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A major aspiration of investors is to better forecast stock performance. Interestingly, emerging ‘neuroforecasting’ research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional magnetic resonance imaging (fMRI), we sampled investors’ neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We find that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors’ predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.Significance StatementThe investors’ dream of forecasting the stock market is typically considered to be just that: An unrealistic aspiration. However, we find that forecasting stock performance may in fact not be completely unattainable. Results of our neuroimaging experiment reveal that professional investors fail to accurately predict long-term stock performance. However, while processing complex information pertaining to investment cases, brain activity in a region associated with reward anticipation was increased for stocks that would end up overperforming in the future market. Remarkably, this effect held after controlling for the stock information presented in the investment cases. Our findings add to recent work in ‘neuroforecasting’, demonstrating that market behavior can be forecasted by brain activity of a small sample, here of professional investors.

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.

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.

The processing of rewards and losses are crucial for learning to adapt to an ever changing environment. Dysregulated reward processes are 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 much larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, from now on termed the Brain Reward Signature (BRS), we trained a LASSO-PCR model to predict the signed magnitude of monetary rewards and losses on the Monetary Incentive Delay task (MID; N = 39) and achieved a high 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 for rewards versus losses; N = 12) and on a gambling task from a large sample (73% decoding accuracy for rewards versus losses, N = 1084) from the Human Connectome Project. Lastly, we also provided preliminary evidence for specificity to rewarding outcomes 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). We thus created a BRS that can be used to make specific, generalizable and reproducible predictions about brain responses to rewards and losses.