Individual Differences in Exemplar-Based Interference During Instructed Category Learning
Individual Differences in Exemplar-Based Interference During Instructed Category Learning David C. Noelle ( NOELLE @ CNBC . CMU . EDU ) Garrison W. Cottrell ( GARY @ CS . UCSD . EDU ) Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 USA Department of Computer Science & Engineering University of California, San Diego La Jolla, CA 92093 USA Abstract Instructed category learning studies have shown that catego- rization practice on a fixed set of labeled training exemplars can cause learners to violate explicitly provided categorization instructions. We have previously proposed a connectionist ac- count of this exemplar-based interference effect — an account which predicts that individuals who display initial difficulty in the application of a categorization rule will exhibit greater exemplar-based interference than good rule-followers. In this paper, we report on a study of human instructed category learn- ing performance intended to test this prediction of the model, and we provide the results of additional connectionist simula- tions which are fit to the human experimental data. Introduction Instructed category learning studies have revealed that expe- rience with labeled examples can sometimes cause learners to deviate from previously provided categorization instruc- tions, even when the category labels on the training items are perfectly consistent with the given instructions (Allen and Brooks, 1991; Brooks et al., 1991). Such experiments typi- cally begin with the presentation of an explicit rule for catego- rizing stimuli. These initial instructions are followed by a se- quence of trials in which stimuli are presented to the learner, one at a time. The learner is asked to make a categoriza- tion judgment for each stimulus, and this judgment is imme- diately followed by performance feedback, providing the cor- rect category label for the object. After a substantial period of such training, the learner is presented with novel stimuli and is asked to provide category labels for these novel items. Pre- vious studies have discovered that learners may sometimes violate the instructions that they were given when faced with a novel stimulus, assigning the category label of a similar training set exemplar to the novel item in lieu of accurately applying the given explicit rule. We have previously presented a connectionist model of instructed category learning which explains this exemplar- based interference effect as emerging from the use of an error- correcting learning rule when learning from examples (Noelle and Cottrell, 1996). This model posits the existence of a working memory network which actively maintains a dis- tributed pattern of activity encoding the explicitly provided categorization rule. The model also includes a categorization network — a system which assigns category labels based on stimulus features. The behavior of the categorization network is modulated by activity in the working memory network, allowing explicit instructions to shape categorization perfor- mance. Exemplar-based interference appears when connec- tion weights in the categorization network are modified, by an error-correcting learning rule, as the result of performance feedback on the training exemplars. This mechanism makes a general prediction concerning exemplar-based interference: difficulty in rule-following should result in larger amounts of interference. If the network exhibits substantial residual error when applying a given explicit categorization rule, this er- ror will produce large weight changes during exemplar-based training, and significant interference will arise. This predic- tion has a number of corollaries. First, the complexity of the categorization rule should impact interference, with more complex rules producing more interference. Second, a corre- sponding trend should be seen across individual learners, with individuals who are error prone at rule application exhibiting more interference than those who find rule application easy. In this paper, we investigate these predictions. We report on a human instructed category learning study which inves- tigates individual differences in exemplar-based interference, and we provide the results of detailed connectionist simula- tions which model the observed human learning performance. Individual Differences in Interference Method Undergraduate students were asked to learn to categorize a set of simple geometric line drawings into two categories. Each geometric stimulus involved a circle with a radial line, drawn in green on a black computer screen. The circle stimuli varied along two continuous dimensions: size and orientation. Four different sized used, with radii of approximately circles were mm, mm, mm, and mm. The number of distinct orientations was also four, with the radial line of the circle rotated from the right-pointing vector counterclockwise by , , , or . Each of the four angles of rotation could be paired with each of the four sizes, producing a set of different stimulus items. These stimuli may be graph- ically depicted as points in a two dimensional feature space, as shown on the right side of Figure 1. Of the possible circle stimuli, seven were distinguished as training set items. These items are marked with boxes in Figure 1. Four training stimuli were to be placed in one category, called the “black” category here for convenience, and the remaining three were to be placed in the other, called the “white” category. A typical experimental trial involved the presentation of a circle in the middle of a blank computer screen. Participants were expected to identify the appropriate category for each stimulus, communicating their judgment by depressing the appropriate key on the computer keyboard. No time pressure