One of the main purposes of learning from experience is to enable prediction, as past experiences are a powerful source for anticipating future events. For example, if we notice that gray clouds are often followed by rain, we learn to predict rain when we see gray clouds again. These predictions are useful because they help us prepare for future situations—like taking an umbrella to stay dry. However, for predictions to be useful, they must extend beyond exact repetitions of past events . In other words, we need to generalize—responding to new but similar stimuli as if they were the original. But what counts as “similar”? How broadly should we generalize?

In our lab, we explore how people generalize by examining several psychological factors that shape this process, including probabilistic associations, psychological distance, and valence. Our work aims to uncover how these factors influence the breadth of predictions drawn from past experiences.