A Computational Model for Individual Differences in Nonreinforced Learning

Cue-Approach Training (CAT) is a paradigm that enhances preferences without external reinforcements, suggesting a potential role for internal learning processes. Here, we developed a novel Bayesian computational model to quantify anticipatory response patterns during the training phase of CAT. This phase includes individual items, and thus, this marker potentially reflects internal learning signals at the item level. Our model, fitted to meta-analysis data from 28 prior CAT experiments, was able to predict individual differences in nonreinforced preference changes using a key computational marker. Crucially, two new experiments manipulated the training procedure to influence the model’s predicted learning marker. As predicted and preregistered, the manipulation successfully induced differential preference changes, supporting a causal role of our model. These findings demonstrate powerful potential of our computational framework for investigating intrinsic learning processes. This framework could be used to predict preference changes and opens new avenues for understanding intrinsic motivation and decision making.