Choosing to Lie: Strategic and Pathological Deception

Humans lie to one another frequently with local and global consequences. Here we examine and operationalize the choice to lie using a mathematical model which we fit to participant behavior. This model combines perceptions of exploitability and fairness attitudes to explore the interaction between two types of utility: instrumental utility and moral utility. These utilities differed in concert with individual differences in participants’ behavioral traits, and those differences underwrote lies with distinct motivations. Strategic deception – wherein the lie benefits the individual - was common amongst people with elevated subclinical psychopathic traits in the absence of paranoia. It was driven by differences in instrumental utility. Whereas pathological deception – which has no apparent economic or strategic benefit – was common among highly paranoid participants (who also had elevated scores on dark triad personality traits) and was governed by the moral utility of the lie. They lied for little apparent benefit because they failed to modulate their fairness attitudes in line with the decision context. These data challenge accounts of deception that suggest deceiving oneself is a prerequisite to successful lying. They also oppose accounts of psychopathology that connect psychopathic traits to pathological lying, and theories that implicate strategic social decision-making dysfunction in paranoia. More broadly, these data highlight the importance of individual differences in efforts to mitigate deception, which have thus far, largely focused on incentive structures and decision architectures

Ebola could be eradicated through voluntary vaccination

Ebola virus disease (EVD) is a severe infection with an extremely high fatality rate spread through direct contact with body fluids. A promising Ebola vaccine (rVSV-ZEBOV) may soon become universally available. We constructed a gametheoretic model of Ebola incorporating individual decisions to vaccinate. We found that if a population adopts selfishly optimal vaccination strategies, then the population vaccination coverage falls negligibly short of the herd immunity level. We concluded that eradication of Ebola is feasible if voluntary vaccination programmes are coupled with focused public education efforts. We conducted uncertainty and sensitivity analysis to demonstrate that our findings do not depend on the choice of the epidemiological model parameters

Paranoia, self-deception and overconfidence

Self-deception, paranoia, and overconfidence involve misbeliefs about the self, others, and world. They are often considered mistaken. Here we explore whether they might be adaptive, and further, whether they might be explicable in Bayesian terms. We administered a difficult perceptual judgment task with and without social influence (suggestions from a cooperating or competing partner). Crucially, the social influence was uninformative. We found that participants heeded the suggestions most under the most uncertain conditions and that they did so with high confidence, particularly if they were more paranoid. Model fitting to participant behavior revealed that their prior beliefs changed depending on whether the partner was a collaborator or competitor, however, those beliefs did not differ as a function of paranoia. Instead, paranoia, self-deception, and overconfidence were associated with participants’ perceived instability of their own performance. These data are consistent with the idea that self-deception, paranoia, and overconfidence flourish under uncertainty, and have their roots in low self-esteem, rather than excessive social concern. The model suggests that spurious beliefs can have value–self-deception is irrational yet can facilitate optimal behavior. This occurs even at the expense of monetary rewards, perhaps explaining why self-deception and paranoia contribute to costly decisions which can spark financial crashes and devastating wars.</p

Delusional unreality and predictive processing

In their recent correspondence Feyearts and colleagues highlighted some similarities and differences between their phenomenological approach to delusions and the predictive processing explanation. In the present work we home in on some key points in the exchange and present data that show predictive processing can indeed explain the pervasive sense of unreality that characterizes some delusions (but by no means all of them). Indeed, our data show that some people with delusions and schizophrenia do not report a sense of unreality, and, furthermore, that some people with extreme esoteric beliefs (but no psychotic illness) also report a pervasive sense of unreality. Notably, no healthy control endorsed a sense of unreality (despite endorsing other delusion-like beliefs). By leveraging computational psychiatry, applied to behavioral data gathered during causal learning, we show that unreality experiences in clinical delusions and non-clinical delusion-like beliefs are associated with different types of aberrant prediction errors, prediction error weightings, and learning rates. Taken together, these data suggest that claims of distinction between predictive processing and phenomenological accounts, and that predictive processing cannot explain the sense of unreality, may have been premature. Furthermore, sense of unreality is not pathognomonic of delusion. Finally, these data suggest again that different patterns of prediction error dysfunction are associated with delusions and delusion-like beliefs with different contents, extending recent work to a belief unexamined through predictive processing: delusional unreality

Different learning aberrations relate to delusion-like beliefs with different contents

The prediction error account of delusions has had success. However, its explanation of delusions with different contents has been lacking. Persecutory delusions and paranoia are the common unfounded beliefs that others have harmful intentions towards us. Other delusions include believing that one’s thoughts or actions are under external control, or that events in the world have specific personal meaning. We compare learning on two different cognitive tasks, probabilistic reversal learning (PRL) and Kamin blocking, that have relationships to paranoid and non-paranoid delusion-like beliefs, respectively. We find that Clinical High-Risk status alone does not result in different behavioral results on the PRL task but that an individual’s level of paranoia is associated with excessive switching behavior. During the Kamin blocking task, paranoid individuals learned inappropriately about the blocked cue. However, they also had decreased learning about the control cue, suggesting more general learning impairments. Non-paranoid delusion-like belief conviction (but not paranoia) was associated with aberrant learning about the blocked cue but intact learning about the control cue, suggesting specific impairments in learning related to cue combination. We fit task-specific computational models separately to behavioral data to explore how latent parameters vary within individuals between tasks, and how they can explain symptom-specific effects. We find that paranoia is associated with low learning rates on the PRL task as well as the blocking task. Non-paranoid delusion-like belief conviction was instead related to parameters controlling the degree and direction of similarity between cue updating during simultaneous cue presentation. These results suggest that paranoia and other delusion-like beliefs involve dissociable deficits in learning and belief updating, which – given the transdiagnostic status of paranoia – may have differential utility in predicting psychosis