Anticipated variability increases generalization of predictive learning.

Abstract

We show that learners generalized more broadly around the learned stimulus when they expected more variability between the learning set and the generalization set, as well as within the generalization set. Experiments 1 and 3 used a predictive learning task and demonstrated border perceptual generalization both when expected variability was manipulated explicitly via instructions (Experiment 1), and implicitly by increasing temporal distance to the anticipated application of learning (Experiment 3). Experiment 2 showed that expecting to apply learning in the more distant future increases expected variability in the generalization set. We explain the relation between expected variability and generalization as an accuracy-applicability trade-off: when learners anticipate more variable generalization targets, they "cast a wider net" during learning, by attributing the outcome to a broader range of stimuli. The use of more abstract, broader categories when anticipating a more distant future application aligns with Construal Level Theory of psychological distance.