When humans learn to categorize multidimensional stimuli, they learn which stimulus dimensions are relevant or irrelevant for distinguishing the categories. Results of a category learning experiment are presented, which show that categories defined by a single dimension are much easier to learn than categories defined by the combination of two dimensions. Three models are fit to the data, ALCOVE (Kruschke 1990a,b, in press), standard back propagation (Rumelhart, Hinton & Wilhams 1986), and the configural-cue model (Gluck & Bower 1988). It is found that alcove, with its dimensional attention learning mechanism, can capture the trends in the data, whereas back propagation and the configural-cue model cannot. Implications for other models of human category learning are discussed.

Theorists of human learning, in domains as various as category learning and language acquisition, have grappled with the issue of whether learners induce rules or remember exemplars, or both. In this article we present new dau that reflect both rule induction and exemplar encoding, and we present a new connectionist model that specifies one way in which rule-based and exemplar-based mechanisms might interact Our empirical study was motivated by analogy to past tense acquisition, and specifically by the previous work of Palermo and Howe (1970). Human subjects learned to categorize items, most of which could be classified by a simple rule, except for a few frequently recurring exceptions. The modeling was motivated by the idea of combining an exemplar-based module (ALCOVE, Kruschke, 1992) and a rule-based module in a connectionist architecture, and allowing the system to learn which module should be responsible for which instances, using the competitive gating mechanism introduced by Jacobs, Jordan, Nowlan, and Hinton (1991). We report quantitative fits of the model to the learning data.

When one billiard ball strikes and launches another, most observers report seeing the first ball cause the second ball to move. Michotte (1963) argued that the essence of phenomenal causality is "ampliation" of movement, in which the motion of the first object is perceptually transferred to the second object. Michotte provided only phenomenological evidence, however. We extend the reviewing paradigm of Kahneman, Treisman, and Gibbs (1992) to Michotte-style launching events and report response-time data consistent with Michotte's notion of ampliation. We discuss how contemporary theories of feature binding can extend to the domain of interacting objects and address our results. We also suggest that our treatment of ampliation helps clarify controversies regarding whether perceived causality is direct or interpreted and whether it is innate or learned.