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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 STATEMENTDishonesty 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.

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 modelling (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 behaviour. 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.

Many of the economically most costly forms of unethical behavior such as tax evasion, stock manipulations or movie and music piracy relate to the moral domain of (dis)honesty, in which unethical behavior is not targeted at a clearly identifiable victim. While large individual differences in (dis)honesty are evident, the neurocognitive determinants of this heterogeneity remain elusive. We combined connectome-based predictive modelling (CPM) on resting state functional connectivity patterns with a novel experimental task, which measures spontaneous and voluntary cheating inconspicuously, to investigate how these task-independent neural patterns shape our (dis)honest choices. Our analyses revealed that functional connectivity in a network of regions, including the dorsolateral prefrontal cortex and the inferior frontal gyrus, commonly linked to cognitive control processes, but also the medial prefrontal cortex and temporal pole, associated with self-referential thinking, and the caudate nucleus, linked to reward processing, are of central importance in promoting honesty. In a leave-one-out cross-validation analysis, we show that this neural model can reliably and accurately predict how much an unseen participant will cheat on our task. Participants who cheated the most, also scored highest on several impulsivity measures, which highlights the ecological validity of our task. Notably, when comparing neural and self-report measures, the neural measures were found to be significantly more important in predicting cheating. Our findings suggest that a person’s dis(honest) decisions depend on how well the self-referential thinking network is functionally connected to the control and reward networks.

Contains fulltext : 220312.pdf (Publisher’s version ) (Open Access) 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. 10 p.

Contains fulltext : 219546.pdf (Publisher’s version ) (Open Access) 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. 12 p.

Despite the large body of research that has investigated the effect of ad appeals of television advertisements on consumers' internal responses and behavior, our understanding of how different ad appeals are processed remains limited. Complementing existing literature with novel insights from neuroimaging techniques can be valuable, providing more immediate insights into implicit mental processes. The present study explores the neural responses to functional and experiential executional elements in television advertisements by using functional magnetic resonance imaging (fMRI). Comparing a unique set of different commercials for the same brand enabled examination of the influence of differences in ad appeal on brain responses and subsequent advertisement effectiveness. Findings show that functional and experiential executional elements engage different brain areas, associated with lower- and higher-level cognitive processes, and that the extent to which these particular brain areas are activated is associated with higher ad effectiveness.

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.

People are particularly sensitive to injustice. Accordingly, deeper knowledge regarding the processes that underlie the perception of injustice, and the subsequent decisions to either punish transgressors or compensate victims, is of important social value. By combining a novel decision-making paradigm with functional neuroimaging, we identified specific brain networks that are involved with both the perception of, and response to, social injustice, with reward-related regions preferentially involved in punishment compared with compensation. Developing a computational model of punishment allowed for disentangling the neural mechanisms and psychological motives underlying decisions of whether to punish and, subsequently, of how severely to punish. Results show that the neural mechanisms underlying punishment differ depending on whether one is directly affected by the injustice, or whether one is a third-party observer of a violation occurring to another. Specifically, the anterior insula was involved in decisions to punish following harm, whereas, in third-party scenarios, we found amygdala activity associated with punishment severity. Additionally, we used a pharmacological intervention using oxytocin, and found that oxytocin influenced participants' fairness expectations, and in particular enhanced the frequency of low punishments. Together, these results not only provide more insight into the fundamental brain mechanisms underlying punishment and compensation, but also illustrate the importance of taking an explorative, multimethod approach when unraveling the complex components of everyday decision-making.SIGNIFICANCE STATEMENTThe perception of injustice is a fundamental precursor to many disagreements, from small struggles at the dinner table to wasteful conflict between cultures and countries. Despite its clear importance, relatively little is known about how the brain processes these violations. Taking an interdisciplinary approach, we combine methods from neuroscience, psychology, and economics to explore the neurobiological mechanisms involved in both the perception of injustice as well as the punishment and compensation decisions that follow. Using a novel behavioral paradigm, we identified specific brain networks, developed a computational model of punishment, and found that administrating the neuropeptide oxytocin increases the administration of low punishments of norm violations in particular. Results provide valuable insights into the fundamental neurobiological mechanisms underlying social injustice.

Research on language abstraction has primarily been focused on the language that is used to describe (intergroup) behaviors, while limited attention has been given to communication about objects. This article aims to fill this gap and studies biases in language abstraction in the descriptions of interactions with objects. Study 1 demonstrated a linguistic expectancy bias in this setting: People described expectancy-congruent experiences with objects more abstractly than incongruent experiences. Study 2 examined a mediator, and provides data suggesting that expectancy-congruent (vs. incongruent) experiences were more likely attributed to the object (vs. the situation/user), and this causal inference mediated the effect of expectancy-(in)congruence on language use. Study 3 examined the impact of communication goals, and found that the goal to convince a receiver of the high quality of a product (vs. no persuasion goal) led to more abstract descriptions of positive experiences with objects and to more concrete language for negative experiences.

The present research examines language abstraction as a method to systematically analyse word of mouth. First, we build on previous research on the systematic bias in the use of language abstraction in communication about human behavior, by revealing biased language use about objects. The first studies demonstrate that a negative word of mouth experience is described more abstractly by a consumer with an unfavorable (vs. favorable) product opinion. Second, we reveal how language abstraction influences a receiver¿s inferences about the product attitude of the consumer who is sending word of mouth, and how language abstraction influences the impact of word of mouth on the receiving consumer. Third, we extend previous research by ruling out the valence of the language as a possible alternative explanation for the use of abstract versus concrete language, and for the impact of this language on receivers.

Canonical models of rational choice fail to account for many forms of motivated adaptive behaviors, specifically in domains such as food selections. To describe behavior in such emotion- and reward-laden scenarios, researchers have proposed dual-process models that posit competition between a slower, analytic faculty and a fast, impulsive, emotional faculty. In this paper, we examine the assumptions and limitations of these approaches to modeling motivated choice. We argue that models of this form, though intuitively attractive, are biologically implausible. We describe an approach to motivated choice based on sequential sampling process models that can form a solid theoretical bridge between what is known about brain function and environmental influences upon choice. We further suggest that the complex and dynamic relationships between biology, behavior, and environment affecting choice at the individual level must inform aggregate models of consumer choice. Models using agent-based complex systems may further provide a principled way to relate individual and aggregate consumer choices to the aggregate choices made by businesses and social institutions. We coin the term "brain-to-society systems" choice model for this broad integrative approach.

Human behavior is affected by various forms of persuasion. The general persuasive effect of high expertise of the communicator, often referred to as "expert power", is well documented. We found that a single exposure to a combination of an expert and an object leads to a long-lasting positive effect on memory for and attitude towards the object. Using functional magnetic resonance imaging (fMRI), we probed the neural processes predicting these behavioral effects. Expert context was associated with distributed left-lateralized brain activity in prefrontal and temporal cortices related to active semantic elaboration. Furthermore, experts enhanced subsequent memory effects in the medial temporal lobe (i.e. in hippocampus and parahippocampal gyrus) involved in memory formation. Experts also affected subsequent attitude effects in the caudate nucleus involved in trustful behavior, reward processing and learning. These results may suggest that the persuasive effect of experts is mediated by modulation of caudate activity resulting in a re-evaluation of the object in terms of its perceived value. Results extend our view of the functional role of the dorsal striatum in social interaction and enable us to make the first steps toward a neuroscientific model of persuasion.

Although much progress has been made in relating brain activations to choice behaviour, evidence that neural measures could actually be useful for predicting the successfulness of marketing actions remains limited. To be of added value, neural measures should significantly increase predictive power, above and beyond conventional measures. In the present study, the authors obtained both stated preference measures and neural measures (electroencephalography; EEG) in response to advertisements for commercially released movies (i.e. movie-trailers), to probe its potential to provide insight into individual preferences in our subjects, as well as movie sales in the population at large. The results show that EEG measures (beta and gamma oscillations) provide unique information regarding individual and population-wide preference, above and beyond stated preference measures, and can thus in principle be used as a neural marker for commercial success. As such, these results provide the first evidence that EEG measures are related to real-world outcomes, and that these neural measures can significantly add to models predicting choice behaviour compared to models that include only stated preference measures.