Search

Abstract 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.

Abstract 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 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.

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.

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.

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.

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.

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.

Although many studies revealed that emotions and their dynamics have a profound impact on cognition and behavior, it has proven difficult to unobtrusively measure emotions. In the current study, our objective was to distinguish different experiences elicited by audiovisual stimuli designed to evoke particularly happy, sad, fear and disgust emotions, using electroencephalography (EEG) and a multivariate approach. We show that we were able to classify these emotional experiences well above chance level. Importantly, we retained all the information (frequency and topography) present in the data. This allowed us to interpret the differences between emotional experiences in terms of component psychological processes such as attention and arousal that are known to be associated with the observed activation patterns. In addition, we illustrate how this method of classifying emotional experiences can be applied on a moment-by-moment basis in order to track dynamic changes in the emotional response over time. The application of our approach may be of value in many contexts in which the experience of a given stimulus or situation changes over time, ranging from clinical to consumption settings.