Error-Reduction and Simplicity: Opposing Goals in Classification Learning

Studies of real world experts show that they use different and subtler regularities than novices to make effective classifications. In laboratory studies of learning however, participants have a strong preference for simple cue sets, even at the expense of accuracy. The present experiments investigate participants’ ability to use subtle stimulus dimensions in order to eliminate category exceptions. Results show that some participants were able to use the optimal 3cue set, but many could not. When there were two optimal cue sets, one with 2 dimensions and one with 3, participants favored the simpler set, even though it meant ignoring an obvious and diagnostic cue. Overall there were wide individual differences, with almost every cue set adopted by some participants. Current theories of attention that posit rapid shifts of learned attention offer promise in accounting for the results. For any organism to adapt successfully it must become sensitive to meaningful regularities in the environment. Humans have developed flexible learning systems, allowing them to rapidly adjust to changing environments. Learning concepts, representations of classes of stimuli that 4require an equivalent response, conserves resources by reducing the amount of information that needs to be processed from the environment, and also allows for generalization to related, novel circumstances. Representing a complex environment, with abundant interdependencies and subtle regularities requires a rich set of concepts. To perform this function, the human perceptual system can attend selectively, become sensitized to highly complex stimulus dimensions, and even create novel functional features. These processes affect the perception of a stimulus and therefore alter the representation of that stimulus. Differences in cue use and representation across experts and novices appear in many areas such as biology (Boster & Johnson, 1989), physics (Chi, Feltovich, & Glaser, 1981), computer programming (Davies, 1994), wine tasting (Solomon, 1997), bird identification (Johnson & Mervis, 1997) and pocket billiards (Blair & McBeath, 2001). Despite an abundance of differences between expert and novice differences in cue use, laboratory studies of learning (specifically “knowledge restructuring”) have shown that participants have had a strong resistance to using new information and more complicated cue sets, even though they would afford better performance (Lewandowsky, Kalish & Griffiths, 2000). To the extent that learning is error driven, exceptions in the cue set provide a powerful motivator to incorporate new dimensions, however, there is also a pressure toward simplicity. Additional dimensions, which may a space in which the categories separate, can be expensive to represent. Completely altering the dimensions used for categorization can require more energy than using unique stimulus-level elements to classify exceptions. This leads to the memorization of exceptions, rather than a refining of the cue set. It is clear that category learning is influenced by opposing forces; one to enlarge, and one to reduce the dimensionality of the cue set. The present research examines the complementary processes of the expansion and reduction of the cue set toward effective and efficient representation. The goal of the present study is to verify that participants can and do optimize their cue sets using both expansion and reduction when learning categories with subtle dimensions. Previous research has demonstrated these complimentary processes by manipulating the stimulus set to provide a new dimension (Blair & Homa, 2003b). These studies produced wide individual differences, with many participants incorporating new dimensions to eliminate category exceptions, and many others choosing to rely on simple cue sets which result in many exceptions and significant error. The Blair and Homa (2003b) studies also showed that participants can shift to optimal spaces if they are less complex. Overall, in these studies participants demonstrated both the flexibility found in studies of expertise and the insistence on simplicity found in studies of knowledge restructuring. Many real world category learning problems do not involve learning new information never experienced before, but rather involve learning to be sensitive to stimulus features that have existed all along, but may have been overlooked for more salient dimensions. For example in bird identification, color is an obvious perceptual cue, used by experts and novices alike. To tell the difference between a Hepatic Tanager and a Summer Tanager, both of which are predominantly red, one must notice the color of the bill and whether the bird has a gray ear patch or not. These are features that novices are prone to miss. The present studies use two obvious dimensions as well as a subtle third dimension as a direct analogy to those cases.