Evidence for the size principle in semantic and perceptual domains
This work addresses a foundational problem in cognitive science for understanding generalization mechanisms, but it is incremental as it builds on existing rational Bayesian models.
The authors tackled the challenge of evaluating the size principle, which posits that hypotheses with fewer objects contribute more to generalization, by developing a more direct method and applying it to diverse datasets, providing evidence for its broad applicability.
Shepard's Universal Law of Generalization offered a compelling case for the first physics-like law in cognitive science that should hold for all intelligent agents in the universe. Shepard's account is based on a rational Bayesian model of generalization, providing an answer to the question of why such a law should emerge. Extending this account to explain how humans use multiple examples to make better generalizations requires an additional assumption, called the size principle: hypotheses that pick out fewer objects should make a larger contribution to generalization. The degree to which this principle warrants similarly law-like status is far from conclusive. Typically, evaluating this principle has not been straightforward, requiring additional assumptions. We present a new method for evaluating the size principle that is more direct, and apply this method to a diverse array of datasets. Our results provide support for the broad applicability of the size principle.