Discovering Inductive Biases in Categorization through Iterated Learning

Abstract

Progress in studying human categorization has typically involved comparing generalization judgments made by people to those made by models for a variety of training conditions. In this paper, we explore an alternative method for understanding human category learning–iterated learning–which can directly expose the inductive biases of human learners and categorization models. Using a variety of stimulus sets, we compare the results of iterated learning experiments with human learners to results from two prominent classes of computational models: prototype models and exemplar models. Our results indicate that human learning is not perfectly captured by either type of model, lending support to the theory that people use intermediate representations between these two extremes.


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