Simplicity and informativeness in semantic category systems release_pdsiemnqlbeo5bro5msbhakn74

Abstract

Recent research has shown that semantic category systems, such as color and kinship terms, find an optimal balance between the considerations of simplicity and informativeness. We argue that this situation arises through a pressure for simplicity from learning and a pressure for informativeness from communicative interaction, two distinct pressures that pull in (often but not always) opposite directions. An alternative account suggests that learning might also act as a pressure for informativeness—that learners might be biased toward inferring informative systems. This results in two competing hypotheses about the human inductive bias. We formalize these competing hypotheses in a Bayesian iterated learning model and test them in two experiments with human participants. Specifically, we investigate whether learners' inductive biases, isolated from any communicative task, are better characterized as favoring simplicity or informativeness. We find strong evidence to support the simplicity account. Furthermore, we show how the application of a simplicity principle in learning can give the impression of a bias for informativeness, even when no such bias is present. Our findings suggest that semantic categories are learned through domain-general principles, negating the need to posit a domain-specific inductive bias.
In application/xml+jats format

Archived Files and Locations