Publications

Many controversies arise from differences in how people resolve moral dilemmas by following deontological moral rules versus consequentialist cost-benefit reasoning (CBR). This article explores whether and, if so, how these seemingly intractable differences may arise from experience and whether they can be overcome through moral learning. We designed a new experimental paradigm to investigate moral learning from consequences of previous decisions. Our participants (N=387) faced a series of realistic moral dilemmas between two conflicting choices: one prescribed by a moral rule and the other favored by CBR. Critically, we let them observe the consequences of each of their decisions before making the next one. In one condition, CBR-based decisions consistently led to good outcomes, whereas rule-based decisions consistently led to bad outcomes. In the other condition, this contingency was reversed. We observed systematic, experience-dependent changes in people's moral rightness ratings and moral decisions over the course of just 13 decisions. Without being aware of it, participants adjusted how much moral weight they gave to CBR versus moral rules according to which approach produced better consequences in their respective experimental condition. These learning effects transferred to their subsequent responses to the Oxford Utilitarianism Scale, indicating genuine moral learning rather than task-specific effects. Our findings demonstrate the existence of rapid adaptive moral learning from the consequences of previous decisions. Individual differences in morality may thus be more malleable than previously thought.

Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission.

Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control – benefitting users to successfully achieve their long-term goals.