Active inference in concept induction UCSD MPLAB TR 2000.03 Jonathan D. Nelson Department of Cognitive Science University of California, San Diego La Jolla, CA 92093-0515 jnelson@cogsci.ucsd.edu Javier R. Movellan Institute for Neural Computation University of California, San Diego La Jolla, CA 92093-0515 movellan@inc.ucsd.edu Abstract People are active information gatherers, constantly experimenting and seeking information relevant to their goals. A reasonable approach to active information gathering is to ask questions and conduct experiments that maximize the expected information gain, given current beliefs (Lindley, 1956; Good, 1966; MacKay, 1992). In this paper we compare the behavior of human subjects with that of an optimal information-gathering agent (infomax) in a concept induction task (Tenenbaum, 1999, 2000). Results show high consistency between subjects in their choices of numbers to sample. However infomax generally fails to predict subjects’ sampling behavior. It is unclear at this time whether the failure of infomax to predict human behavior is due to problems with Tenenbaum’s concept induction model, or due to the fact that subjects use suboptimal heuristics (e.g., confirmatory sampling). 1 Introduction In scientific inquiry and in everyday life, people seek out information relevant to perceptual and cognitive tasks. Scientists perform experiments to uncover causal relationships; people saccade to informative areas of visual scenes, turn their head towards unexpected (i.e., informative) sounds, and ask questions to understand the meaning of concepts. Consider a person learning a foreign language, who notices that a particular word, “tikos,” is used for baby moose, baby penguins, and baby cheetahs. Based on those examples, he or she may attempt to discover what tikos really means. Logically, there is an infinite number of possibilities: tikos could mean baby animals, or simply animals, or even baby animals and antique telephones. In practice a few examples are enough for human learners to reduce the space of possibilities and form strong intuitions about what meanings are most likely. Suppose you can point to a baby duck, an adult duck, or an antique telephone, to inquire whether that object is “tikos”. Your goal is to figure out what “tikos” means. Which question would you ask? Why? When the goal is to learn as much as possible about a set of concepts, a reasonable strategy is to chose those questions which maximize the expected information gain about those concepts, given one’s current beliefs (Lindley 1956, Good 1966, MacKay 1992). The goal of this paper is to evaluate whether humans use such a strategy in a relatively unconstrained concept induction task.