Functional connectivity between the caudate and medial prefrontal cortex reflects individual honesty variations in adults and children

Behavioral dishonesty in multiscenes: Associations with trait honesty and neural patterns during (dis)honesty video-watching

Cross-situational inconsistency is common in the expression of honesty traits; yet, there is insufficient emphasis on behavioral dishonesty across multiple contexts. The current study aimed to investigate behavioral dishonesty in various contexts and reveal the associations between trait honesty, behavioral dishonesty, and neural patterns of observing others behave honestly or dishonestly in videos (abbr.: (dis)honesty video-watching). First, the results revealed limitations in using trait honesty to reflect variations in dishonest behaviors and predict behavioral dishonesty. The finding highlights the importance of considering neural patterns in understanding and predicting dishonest behaviors. Second, by comparing the predictive performance of seven types of data across three neural networks, the results showed that functional connectivity in the hypothesis-driven network during (dis)honesty video-watching provided the highest predictive power in predicting multitask behavioral dishonesty. Last, by applying the feature elimination method, the midline self-referential regions (medial prefrontal cortex, posterior cingulate cortex, and anterior cingulate cortex), anterior insula, and striatum were identified as the most informative brain regions in predicting behavioral dishonesty. In summary, the study offered insights into individual differences in deception and the intricate connections among trait honesty, behavioral dishonesty, and neural patterns during (dis)honesty video-watching.

Self-deception: Distorted metacognitive process in ambiguous contexts

As one of the commonly used folk psychological concepts, self-deception has been intensively discussed yet is short of solid ground from cognitive neuroscience. Self-deception is a biased cognitive process of information to obtain or maintain a false belief that could be both self-enhancing or self-diminishing. Study 1 (N = 152) captured self-deception by adopting a modified numerical discrimination task that provided cheating opportunities, quantifying errors in predicting future performance (via item-response theory model), and measuring the belief of how good they are at solving the task (i.e., self-efficacy belief). By examining whether self-efficacy belief is based upon actual ability (true belief) or prediction errors (false belief), Study 1 showed that self-deception occurred in the effortless (easier access to answer cues) rather than effortful (harder access to answer cues) cheating opportunity conditions, suggesting high ambiguity in attributions facilitates self-deception. Studies 2 and 3 probed the neural source of self-deception, linking self-deception with the metacognitive process. Both studies replicated behavioral results from Study 1. Study 2 (ERP study; N = 55) found that the amplitude of frontal slow wave significantly differed between participants with positive/self-enhancing and negative/self-diminishing self-deceiving tendencies in incorrect predictions while remaining similar in correct predictions. Study 3 (functional magnetic resonance imaging study; N = 33) identified self-deceiving associated activity in the anterior medial prefrontal cortex and showed that effortless cheating context increased cheating behaviors that further facilitated self-deception. Our findings suggest self-deception is a false belief associated with a distorted metacognitive mental process that requires ambiguity in attributions of behaviors.

Honesty as a trait

The purpose of this paper is to review recent research about the possibility that some people are more honest than others and about the causes of them being so. We tackle four big questions about the consistency of honest behavior, the content and breadth of trait honesty, the mechanisms underlying trait honesty, and the measurement of trait honesty. Recent research reveals we are only at the beginning states of answering these questions about honesty. Invigorated research is needed to firmly resolve whether individuals differ in honesty and if so, integrate the determining mechanisms and develop strong measurements.

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 functional connectivity of social brain regions predicts motivated dishonesty

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.