Observational Category Learning as a Path to More Robust Generative Knowledge

Observational Category Learning as a Path to More Robust Generative Knowledge Kimery R. Levering (kleveri1@binghamton.edu) Kenneth J. Kurtz (kkurtz@binghamton.edu) Department of Psychology, Binghamton University Binghamton, NY 13902-6000 USA Abstract to account for the flexibility with which categories are used in the real world. One example of this is the ability to perform tasks requiring knowledge that was not needed while learning to distinguish between classes. Models and theories of category learning may exaggerate the extent to which people adopt discriminative strategies because of a reliance on the traditional supervised classification task. In the present experiment, this task is contrasted with supervised observational learning as a way of exploring differences between discriminative and generative learning. Categories were defined by a simple unidimensional rule with a second dimension that was either less diagnostic (than the simple rule on the first dimension) or non-diagnostic. When the second dimension was less diagnostic, observational learners were more sensitive to its distributional properties compared to classification learners (though classification accuracy at test did not differ). Observational learners were also consistently more sensitive to distributional information about the highly diagnostic dimension. When the second dimension was non-diagnostic, neither learning group showed sensitivity to the distributional properties of this dimension. Generative and Discriminative Methods A useful framework for understanding this shortcoming in leading accounts of category learning can be found in the distinction between generative and discriminative classifiers in the machine learning literature (Ng & Jordan, 2001). Formally, a discriminative model (e.g., linear regression) learns to classify examples by optimizing a function to partition the space and correctly segregate category members. A generative model (e.g., naive Bayes classifier) learns the distributional properties within each category. It classifies by determining the likelihood of each category generating the given input. Both types of models can learn to correctly categorize, but the generative model does so by modeling more of the data than the task requires per se. A generative framework may be more appropriate to apply to the psychology of human categorization. According to the dichotomy, purely discriminative category learning would result in good classification performance, but not much knowledge beyond the ability to distinguish the categories. Purely generative category learning would result in complete statistical models of each category that are sensitive not only to diagnostic properties, but also to the internal structure and distributional characteristics of its features. As suggested earlier, the latter may be closer to the type of category learning that occurs in the real world. The nature of laboratory materials and tasks may induce discriminative and generative learning to different extents. However, the task that is most commonly used, modeled, and accounted for in category learning experiments – the traditional supervised classification task – may foster learning that is especially discriminative. In a traditional supervised classification task, a series of objects or feature sets are displayed one at a time and learners respond by choosing one of the (typically two) mutually exclusive categories. Corrective feedback is given on each trial and participants learn to assign each member to the appropriate category. In addition to other factors (binary-valued stimulus dimensions, mutually exclusive categories, small number of categories and examples), this task itself invites the learner to engage in discriminative learning behavior. Guess-and- correct learning about category membership supports hypothesis testing that is specific to distinguishing between categories and may encourage focusing on individual Keywords: concepts and categories; category learning; supervised classification task; representation; Introduction Representations of concepts and category knowledge must be robust enough to be applied across a broad range of psychological tasks. Some of the most important functions of such knowledge include being able to make inferences about unknown properties, satisfy goals, communicate ideas, and generate new instances. In short, we need to have comprehensive and flexible mental models of information corresponding to categories in the real world (Markman & Ross, 2003; Solomon, Medin, & Lynch, 2003). Some well-known models of human category learning (e.g. ALCOVE, RULEX) do not address the robustness and flexibility of real psychological concepts. Instead, they focus on the time course of learning to assign items to one category or another. This neglects the learner’s ability to use the resulting representation to do anything else (though see Kurtz, 2007; Love, Medin, & Gureckis, 2004). Theoretical claims about the mechanisms behind category learning can be similarly focused on the core task of artificial classification learning. One example of this is the prevalence of selective attention as the proposed primary mechanism by which category learning occurs. In light of evidence such as Shepard, Hovland, & Jenkins’ (1961) classic study, researchers have tended toward the view that people learn by allocating attention (either initially or gradually) exclusively to dimensions that are diagnostic of category membership (Nosofsky, 1984). This type of explanation has been successful in explaining classification learning as studied in the lab, but it may not go far enough