Prior knowledge and correlational structure in unsupervised learning.

Abstract Prior knowledge has been shown to facilitate both supervised and unsupervised category learning, but questions remain about how this facilitation occurs. This article describes two experiments that investigate the effects of prior knowledge on unsupervised learning, using the exemplar-memory task of Clapper and Bower (2002). Experiment 1 demonstrates that prior knowledge facilitates learning in this task, as expected, and that this facilitation extends to both knowledge-relevant and knowledge-irrelevant features of the new categories. Experiment 2 shows that knowledge facilitates learning not only by increasing the probability that people will discover separate categories, but also by making the features of different categories seem less interchangeable, thereby reducing interference and confusion among them. Taken together, these experiments demonstrate that prior knowledge has multiple effects on unsupervised learning and suggests that the exemplar-memory task may provide a useful procedure for disentangling and investigating these effects. The ability to learn new categories has long been recognized as an essential feature of human cognition. In recent decades, it has become increasingly clear that pre-existing knowledge, commonsense theories, and causal reasoning play an extremely important role in this fundamental human ability (see, e.g., Murphy, 2002 for a review), and that prior knowledge can facilitate category learning in a wide variety of tasks and situations (e.g., Ahn, 1990, 1999; Kaplan & Murphy, 1999; Murphy & Allopenna, 1994; Murphy & Kaplan, 2000; Pazzani, 1991; Spalding & Murphy, 1996; Wattenmaker, Dewey, D.T. Murphy, & Medin, 1986; Wisniewski, 1995). The research described in this article investigates the role of knowledge in observational or "unsupervised" learning, in which people discover new categories without feedback or other assistance from an external "trainer." Unsupervised learning is theoretically interesting and pervasive in everyday life, but it has received much less empirical study than traditional (supervised) classification tasks (e.g., Bruner, Goodnow, & Austin, 1956; Medin & Schaffer, 1978; Posner & Keele, 1968, 1970). Most demonstrations of knowledge effects on unsupervised learning have occurred in the context of free sorting or "category construction" experiments. This task involves presenting participants with a set of pictures or verbal descriptions (artificial stimuli) printed on cards, and asking them to sort the cards into two or more groups corresponding to their subjectively "natural" categorization. The consistent result is that people prefer to sort the stimuli into categories based on a single attribute dimension, while ignoring their overall similarity or family resemblance structure (e.g., Medin, Wattenmaker, & Hampson, 1987; Regehr & Brooks, 1995). However, people can be induced to sort on the basis of family resemblance if the categories so defined are related to prior knowledge or causal theories. For example, Ahn (1990, 1999) showed that providing learners with an explicit theory that explained why certain combinations of features occurred together in the same category increased the likelihood that they would sort according to family resemblance (overall similarity) rather than based on a single dimension. Spalding and Murphy (1996) demonstrated that people were more likely to sort by family resemblance when the features of the categories were related to coherent themes (e.g., Arctic vehicles vs. jungle vehicles). Kaplan and Murphy (1999) showed that relatedness to a coherent theme would promote learning of category structure even if only one dimension of the stimuli was related to that theme, and that this benefit extended to learning both theme-relevant and theme-irrelevant features of the new category. Obviously, understanding such knowledge effects should be a major priority for research on unsupervised learning. …