Linking Traits and Behavior Through Computational Modeling of Neuroethical Decisions

Individual differences in psychology are often measured through self-report questionnaires or behavioral performance, yet these approaches capture different constructs and rarely align. Computational modeling provides a framework for connecting both measures, as it extracts a process-level description of cognition from observable behavior in the form of latent variables. These latent variables represent repeated patterns of decision-making that should relate to psychological traits. We applied this approach to neuroethical decision-making, where evidence shows that both behavioral performance and self-reported psychological traits are related to how individuals weigh uncertain risks against potential benefits of emerging brain technologies. Participants completed a neuromodulation task in which risk and benefit probabilities were systematically varied. To capture how decisions unfolded, we used a temporally sensitive diffusion decision model that estimates when and how risks and benefits are evaluated. These model parameters described general trends in task behavior: individuals considered risks earlier and weighed risks more highly than benefits. Additionally, model parameters described how individuals modified their behavior: individuals who considered benefits first according to the model tended to endorse neuromodulation, but shifted towards rejection when risks were evaluated earlier. Importantly, self-reported psychological traits related to these computational parameters. Cognitive factors such as risk propensity and numeracy related to risk sensitivity and evaluation timing, while prosocial traits were also related to evaluation timing and benefit sensitivity. By contrast, Religion & Spirituality and Moral Commitment measures showed lower than expected associations across parameters. By capturing process-level mechanisms, computational modeling served as a bridge between self-reported traits and observed behavior, showing how stable individual differences shape the dynamics of decision-making in neuroethical contexts.