Kayser, A. | bioRxiv Science

bioRxiv2024

Nothing but lies: improving the validity of neural predictors of deception

Deception is a universal human behavior. Yet longstanding skepticism about the validity of measures used to understand the biological mechanisms underlying deceptive behavior has relegated such studies to the scientific periphery. Here we address these fundamental questions by applying novel machine learning methods and functional neuroimaging to signaling games capturing motivated deception in human participants. First, we develop an approach to test for the presence of confounding processes and thereby validate past skepticism by showing that much of the predictive power of neural predictors trained on deception data comes from confounding processes. Second, we show that the presence of confounding signals need not be fatal, and we improve the validity of our neural predictor via a novel machine learning procedure that identifies and removes these confounding signals. Together, these findings point to a scientific approach for studying a neglected class of behavior, with important methodological and societal implications.

neuroscience↗

bioRxiv2019

Dopamine and risky decision-making in pathological and problem gamblers.

Gambling disorder is a behavioral addiction that is associated with impairments in value-based decision-making such as increased temporal discounting and reduced risk-aversion. Dopamine regulates learning and decision-making by modulating information processing throughout fronto-striatal circuits. Although the role of alterations in dopamine neurotransmission in the etiology of gambling disorder is controversial, preliminary evidence suggests that specifically increasing frontal dopamine levels might improve cognitive functioning in pathological and problem gamblers. We therefore examined whether increasing frontal dopamine levels via the catechol-O-methyltransferase (COMT) inhibitor tolcapone would reduce risky choice in a group of pathological and problem gamblers (n=14) in a repeated-measures counter-balanced placebo-controlled double-blind study. Choice data were fit using hierarchical Bayesian parameter estimation and a modeling scheme that combined a risky choice model with the drift diffusion model to account for both choices and response time distributions. Model comparison revealed that the data were best accounted for by a variant of the drift diffusion model with a non-linear modulation of trial-wise drift rates by value differences, confirming recent findings. Contrary to our hypothesis, risk-taking was slightly increased under tolcapone vs. placebo (Cohens d = -.281). Examination of drug effects on diffusion model parameters revealed an increase in the value-dependency of the drift rate (Cohens d = .932) with a simultaneous reduction in the maximum drift rate (Cohens d = -1.84). These results add to previous work on the potential role of COMT inhibitors in behavioral addictions, and show no consistent beneficial effect of tolcapone on risky choice in gambling disorder. Modeling results add to mounting evidence for the applicability of diffusion models in value-based decision-making. Future work should focus on individual genetic, clinical and cognitive factors that might account for the heterogeneity in the effects of COMT inhibition.

neuroscience↗