Stimulus Generalization in Category Learning

Stimulus Generalization in Category Learning Matt Jones, W. Todd Maddox, and Bradley C. Love [mattj,maddox,love]@psy.utexas. edu University of Texas, Department of Psychology, 1 University Station A8000 Austin, TX 78712 USA representations and the distinction between integral and separable dimensions. We conclude by discussing the broader applicability of this new methodology as well as its implications for the nature of perceptual and category representations, attentional learning, and the roles of short- and long-term memory in categorization. Abstract Stimulus generalization is often regarded as a fundamental component of category learning, yet it has not been directly studied in this context. Here we develop a technique for measuring generalization based on sequential effects in subjects’ responses. We find that patterns of generalization can adapt to global properties of the task, but only when the category structure is defined by perceptually primitive and separable dimensions. Implications are discussed for attentional learning and the nature of both perceptual and category representations. Recency effects and stimulus generalization Perhaps the most fundamental task facing the brain is to use past experience to determine useful behavior in novel situations. For example, in deciding whether a particular snake is poisonous, one might draw on knowledge of other, similar snakes whose toxicity was known. The details of this process can be critical: Basing one’s response on other snakes of similar color and markings may be effective, but relying on irrelevant properties such as length could have disastrous consequences. In other words, successful generalization depends critically on knowledge of which variables are relevant to the current prediction. One task in which stimulus generalization is believed to play an important role is category learning (Medin & Schaffer, 1978). However, in contrast to the rich body of data in conditioning (see Shepard, 1987), stimulus generalization in category learning has yet to be directly investigated. Often it is assumed that generalization operates the same in these two tasks, and generalization functions that have been empirically supported in conditioning studies are incorporated into the similarity functions of categorization models (Kruschke, 1992; Love, Medin, & Gureckis, 2004; Nosofsky, 1986). However, the richer nature of representations involved in category learning (e.g., Maddox & Ashby, 1993; Rosch et al., 1976; Sloman, Love, & Ahn, 1998) suggests that generalization in this domain may be far more complex than is currently assumed. The primary aim of this paper is to develop and explore a method for directly assessing stimulus generalization in category learning. The technique, described in more detail below, is based on a close connection between generalization and recency effects (Jones & Sieck, 2003). Here we present two experiments designed to validate this approach and to relate it to previous findings on attentional learning. Our results show good support for the approach and illustrate how it can provide insight into perceptual Proportion “A” responses Introduction Recency effects are a robust phenomenon in repeated judgment tasks. For example, in studies of probability learning (repeated uncued forced-choice tasks), it has been regularly found that subjects are biased to select whichever response was reinforced on the previous trial (see Myers, 1970, for a review). Jones and Sieck (2003) found that this same effect occurs in cued categorization: Once the identity of the current stimulus is controlled for, subjects tend to choose the category that was correct on the previous trial. This marginal effect of learning from the previous trial can be interpreted as generalization from one stimulus to the next, because it reflects the belief that the current stimulus is likely to belong to the same category as the previous stimulus. Consistent with this interpretation, Jones and Sieck found that the magnitude of the recency effect depends on the similarity between the present and previous stimuli, as shown in Figure 1. Stimuli in these experiments were hypothetical medical patients varying in the presence or absence of three symptoms. The recency effect was greatest when successive stimuli were identical and decreased with each cue mismatch, fully disappearing for cases of complete mismatch. The approximately exponential decrease is similar to the functional form of generalization commonly found in conditioning (Shepard, 1987). Previous Category A B Number of M ismatching Cues Figure 1: Recency effects as a function of number of mismatching cues between present and previous stimuli. (From Jones and Sieck, 2003, Expt. 2, control condition.)