Disentangling Model-Based and Model-Free Moral Learning

To resolve moral dilemmas, people often rely on one of two decision strategies: cost-benefit reasoning versus following moral rules. Previous studies on moral learning show that people learn to use the decision-making strategy that led to the best outcomes in the past. Do they learn this by constructing a mental model of what outcomes would result from using either strategy (i.e., model-based learning) or by assigning value directly to each strategy (i.e., model-free learning)? To answer this question, we adapted the two-step task to a trolley-type dilemma between following moral rules (saving a colleague) versus cost-benefit reasoning (protecting a larger group). In each of the 125 trials, participants’ choices led to either a common or a rare transition, which probabilistically led to good versus bad outcomes. Computational modeling and pre-registered analysis of behavioral data provided converging evidence that participants apply both model-based and model-free learning.