Brain activity of professional investors signals future stock performance.

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 MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found 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.

A multivariate brain signature for reward.

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.

Cognitive control increases honesty in cheaters but cheating in those who are honest.

Every day, we are faced with the conflict between the temptation to cheat for financial gains and maintaining a positive image of ourselves as being a "good person." While it has been proposed that cognitive control is needed to mediate this conflict between reward and our moral self-image, the exact role of cognitive control in (dis)honesty remains elusive. Here we identify this role, by investigating the neural mechanism underlying cheating. We developed a task which allows for inconspicuously measuring spontaneous cheating on a trial-by-trial basis in the MRI scanner. We found that activity in the nucleus accumbens promotes cheating, particularly for individuals who cheat a lot, while a network consisting of posterior cingulate cortex, temporoparietal junction, and medial prefrontal cortex promotes honesty, particularly in individuals who are generally honest. Finally, activity in areas associated with cognitive control (anterior cingulate cortex and inferior frontal gyrus) helped dishonest participants to be honest, whereas it enabled cheating for honest participants. Thus, our results suggest that cognitive control is not needed to be honest or dishonest per se but that it depends on an individual's moral default.

Cognitive Control Promotes Either Honesty or Dishonesty, Depending on One's Moral Default.

Cognitive control is crucially involved in making (dis)honest decisions. However, the precise nature of this role has been hotly debated. Is honesty an intuitive response, or is will power needed to override an intuitive inclination to cheat? A reconciliation of these conflicting views proposes that cognitive control enables dishonest participants to be honest, whereas it allows those who are generally honest to cheat. Thus, cognitive control does not promote (dis)honesty per se; it depends on one's moral default. In the present study, we tested this proposal using electroencephalograms in humans (males and females) in combination with an independent localizer (Stroop task) to mitigate the problem of reverse inference. Our analysis revealed that the neural signature evoked by cognitive control demands in the Stroop task can be used to estimate (dis)honest choices in an independent cheating task, providing converging evidence that cognitive control can indeed help honest participants to cheat, whereas it facilitates honesty for cheaters.SIGNIFICANCE STATEMENT Dishonesty causes enormous economic losses. To target dishonesty with interventions, a rigorous understanding of the underlying cognitive mechanisms is required. A recent study found that cognitive control enables honest participants to cheat, whereas it helps cheaters to be honest. However, it is evident that a single study does not suffice as support for a novel hypothesis. Therefore, we tested the replicability of this finding using a different modality (EEG instead of fMRI) together with an independent localizer task to avoid reverse inference. We find that the same neural signature evoked by cognitive control demands in the localizer task can be used to estimate (dis)honesty in an independent cheating task, establishing converging evidence that the effect of cognitive control indeed depends on a person's moral default.

Individual differences in (dis)honesty are represented in the brain's functional connectivity at rest.

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.

A role of serotonin and the insula in vigor: Tracking environmental and physiological resources.

We describe a neural monitor of environmental and physiological resources that informs effort expenditure. Depending on resources and environmental stability, serotonergic and dopaminergic neuromodulations favor different behavioral controls that are organized in corticostriatal loops. This broader perspective produces some suggestions and questions that may not be covered by the foraging approach to vigor of Shadmehr and Ahmed (2020).

Decoding dynamic affective responses to naturalistic videos with shared neural patterns.

This study explored the feasibility of using shared neural patterns from brief affective episodes (viewing affective pictures) to decode extended, dynamic affective sequences in a naturalistic experience (watching movie-trailers). Twenty-eight participants viewed pictures from the International Affective Picture System (IAPS) and, in a separate session, watched various movie-trailers. We first located voxels at bilateral occipital cortex (LOC) responsive to affective picture categories by GLM analysis, then performed between-subject hyperalignment on the LOC voxels based on their responses during movie-trailer watching. After hyperalignment, we trained between-subject machine learning classifiers on the affective pictures, and used the classifiers to decode affective states of an out-of-sample participant both during picture viewing and during movie-trailer watching. Within participants, neural classifiers identified valence and arousal categories of pictures, and tracked self-reported valence and arousal during video watching. In aggregate, neural classifiers produced valence and arousal time series that tracked the dynamic ratings of the movie-trailers obtained from a separate sample. Our findings provide further support for the possibility of using pre-trained neural representations to decode dynamic affective responses during a naturalistic experience.

Neural similarity at temporal lobe and cerebellum predicts out-of-sample preference and recall for video stimuli.

The extent to which brains respond similarly to a specific stimulus, across a small group of individuals, has been previously found to predict out-of-sample aggregate preference for that stimulus. However, the location in the brain where neural similarity predicts out-of-sample preference remains unclear. In this article, we attempt to identify the neural substrates in three functional magnetic resonance imaging (fMRI) studies. Two fMRI studies (N = 40 and 20), using previously broadcasted TV commercials, show that spatiotemporal neural similarity at temporal lobe and cerebellum predict out-of-sample preference and recall. A follow-up fMRI study (N = 28) with previously unseen movie-trailers replicated the predictive effect of neural similarity. Moreover, neural similarity provided unique information on out-of-sample preference above and beyond in-sample preference. Overall, the findings suggest that neural similarity at temporal lobe and cerebellum - traditionally associated with sensory integration and emotional processing - may reflect the level of engagement with video stimuli.

Integration of negative experiences: A neuropsychological framework for human resilience.

We propose that the fundamental mechanism underlying resilience is the integration of novel or negative experiences into internal schemata. This process requires a switch from reactive to predictive control modes, from the brain's salience network to the default mode network. Reappraisal, among other mechanisms, is suggested to facilitate this process.

Neural mechanisms underlying context-dependent shifts in risk preferences.

Studies of risky decision-making have demonstrated that humans typically prefer risky options after incurring a financial loss, while generally preferring safer options after a monetary gain. Here, we examined the neural processes underlying these inconsistent risk preferences by investigating the evaluation of gains and losses, and demonstrating how these responses can impact subsequent preference for either risky or safe choice options. Participants performed a task while undergoing fMRI in which they experienced both gains and losses. Immediately following a gain or loss, participants decided to either play or pass on a "double-or-quits" gamble. The outcome of the gamble could either double or eliminate their initial gain (from the time-estimation task) or redeem or double their initial loss. If they chose not to play this gamble, they retained the initial gain or loss. We demonstrate a shift in risk-taking preferences for identical sets of gambles as a function of previous gains or losses, with participants showing a greater preference towards riskier decisions in the context of a prior loss. An interaction between evaluating gain/loss contexts and subsequent behavioral risk pattern revealed an increased BOLD response in the ventromedial prefrontal cortex (vmPFC), with stronger responses for both gambling in a loss context and safety in a gain context. This suggests that the vmPFC is responsible for integrating these contextual effects, with these processes impacting on subsequent risky choice.

Testosterone inhibits trust but promotes reciprocity.

The steroid hormone testosterone has been associated with behavior intended to obtain or maintain high social status. Although such behavior is typically characterized as aggressive and competitive, it is clear that high social status is achieved and maintained not only through antisocial behavior but also through prosocial behavior. In the present experiment, we investigated the impact of testosterone administration on trust and reciprocity using a double-blind randomized control design. We found that a single dose of 0.5 mg of testosterone decreased trust but increased generosity when repaying trust. These findings suggest that testosterone may mediate different types of status-seeking behavior. It may increase competitive, potentially aggressive, and antisocial behavior when social challenges and threats (i.e., abuse of trust and betrayal) need to be considered; however, it may promote prosocial behavior in the absence of these threats, when high status and good reputation may be best served by prosocial behavior.

Subjective effort derives from a neurological monitor of performance costs and physiological resources.

Kurzban et al.'s expectancy-value mechanism of effort allocation seems relevant in situations when familiar tasks are initiated. However, we think additional mechanisms are important when people continue with a task for a prolonged time. These mechanisms, which are particularly relevant for performance of novel or urgent tasks, involve neural systems that track performance costs and resources.

The acute effects of stress on dishonesty are moderated by individual differences in moral default.

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.

Cognitive control and dishonesty.

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.

Resting-state BOLD signal variability is associated with individual differences in metacontrol.

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.

The importance of failure: feedback-related negativity predicts motor learning efficiency.

Learning from past mistakes is of prominent importance for successful future behavior. In the present study, we tested whether reinforcement learning signals in the brain are predictive of adequate learning of a sequence of motor actions. We recorded event-related potentials (ERPs) while subjects engaged in a sequence learning task. The results showed that brain responses to feedback (the feedback-related negativity [FRN]) predicted whether subjects learned to avoid an erroneous response the next time this action had to be performed. Our findings add to a growing literature on feedback-based performance adjustment, by showing that FRN amplitudes may reflect the acquisition of motor skill and the consolidation of contingencies between stimuli or cues and their associated responses, providing evidence that learning efficiency and future performance can be predicted by the neural response to current feedback: FRN amplitude associated with a mistake is predictive of whether this mistake will be repeated, or learned from.

Different Neural Mechanisms Underlie Non-habitual Honesty and Non-habitual Cheating.

There is a long-standing debate regarding the cognitive nature of (dis)honesty: Is honesty an automatic response or does it require willpower in the form of cognitive control in order to override an automatic dishonest response. In a recent study (Speer et al., 2020), we proposed a reconciliation of these opposing views by showing that activity in areas associated with cognitive control, particularly the inferior frontal gyrus (IFG), helped dishonest participants to be honest, whereas it enabled cheating for honest participants. These findings suggest that cognitive control is not needed to be honest or dishonest per se but that it depends on an individual's moral default. However, while our findings provided insights into the role of cognitive control in overriding a moral default, they did not reveal whether overriding honest default behavior (non-habitual dishonesty) is the same as overriding dishonest default behavior (non-habitual honesty) at the neural level. This speaks to the question as to whether cognitive control mechanisms are domain-general or may be context specific. To address this, we applied multivariate pattern analysis to compare neural patterns of non-habitual honesty to non-habitual dishonesty. We found that these choices are differently encoded in the IFG, suggesting that engaging cognitive control to follow the norm (that cheating is wrong) fundamentally differs from applying control to violate this norm.

Absorbed in the task: Personality measures predict engagement during task performance as tracked by error negativity and asymmetrical frontal activity.

We hypothesized that interactions between traits and context predict task engagement, as measured by the amplitude of the error-related negativity (ERN), performance, and relative frontal activity asymmetry (RFA). In Study 1, we found that drive for reward, absorption, and constraint independently predicted self-reported persistence. We hypothesized that, during a prolonged monotonous task, absorption would predict initial ERN amplitudes, constraint would delay declines in ERN amplitudes and deterioration of performance, and drive for reward would predict left RFA when a reward could be obtained. Study 2, employing EEG recordings, confirmed our predictions. The results showed that most traits that have in previous research been related to ERN amplitudes have a relationship with the motivational trait persistence in common. In addition, trait-context combinations that are likely associated with increased engagement predict larger ERN amplitudes and RFA. Together, these results support the hypothesis that engagement may be a common underlying factor predicting ERN amplitude.

Measuring Neural Arousal for Advertisements and Its Relationship With Advertising Success.

Abundant research has established the important role of ad-evoked feelings on consumers' reaction to advertising. However, measurement of feelings through explicit self-report is not without its limitations. The current study adds to previous work by showing a sophisticated way of first estimating how arousal is represented in the brain via an independent task (using EEG), and thereafter using this representation to measure arousal in response to advertisements. We then estimate the relationship between the identified process (arousal) and external measures of ad effectiveness (as measured by notability and attitude toward the ad). The results show that the neural measure of arousal is positively associated with notability of ads in the population at large, but may be negatively associated with attitude toward these ads. The implications for the application of EEG in ad testing and for understanding the relationship between arousal and effective advertising are discussed.

Neural Mechanisms of Choice Diversification.

When asked to select several options at once, people tend to choose a greater diversity of items than when they are asked to make these selections one at a time. Using functional magnetic resonance imaging (fMRI), we provide novel insight into the neural mechanisms underlying diversification in portfolio choices. We found that, as participants made multiple selections from a menu of different options, the current state of their choice portfolio (i.e., the previously selected options) dynamically modulates activity in the neural valuation system in response to the options under evaluation. More specifically, we found that activity in the ventral striatum (VS) decreases when the option has already been selected ("satiation"), while activity in the ventromedial prefrontal cortex increases when other options have previously been selected ("novelty-seeking"). Our findings reveal two processes that drive diversification in portfolio choices, and suggest that the context of previous selections strongly impacts how the brain evaluates current choice options.