Running head : Eyetracking and Selective Attention Eyetracking and Selective Attention in Category Learning

An eyetracking version of the classic Shepard, Hovland and Jenkins (1961) experiment was conducted. Forty years of research has assumed that category learning includes learning how to selectively attend to only those stimulus dimensions useful for classification. We confirmed that participants learned to allocate their attention optimally. However, we also found that neither associationist accounts of gradual learning nor hypothesis-testing accounts accurately predicted the pattern of eye movements leading up to successful learning. The implication of these results, and the use of eyetracking technology more generally, for categorization theory are discussed. Eyetracking and Selective Attention 3 Eyetracking and Selective Attention in Category Learning Selective attention has played a prominent role in categorization theory ever since Shepard, Hovland and Jenkins (1961) demonstrated that a simple stimulus generalization account of category learning is untenable. The stimulus generalization account took category learning as the formation of associations between stimuli and category labels. This account predicted that it should be easy to associate stimuli that share many features with one category label, and difficult to associate such stimuli with different labels. Unexpectedly, one important determiner of difficulty was the number of stimulus dimensions needed for correct classification. This pattern of results is best understood in terms of learners optimally allocating their selective attention to those dimensions diagnostic of category membership (Medin & Schaffer, 1978) Currently, selective attention is an integral component of all major categorization theories. In exemplar models (Medin & Shaffer, 1978; Nosofsky, 1986) prototype models (Nosofsky, 1992; Smith & Minda, 1998) and rule-based models (Smith, Patalano, & Jonides, 1992), selective attention is formalized either in terms of explicit attention weights on the stimulus dimensions or in terms of which stimulus dimensions are referred to by the current hypothesis (i.e., rule) being tested. Moreover, in more recent years, these theories include mechanisms by which selective attention changes with learning. One prominent example is Kruschke’s (1992) ALCOVE, a connectionist exemplar model that changes attention weights as a function of error feedback. Another is Nosofksy, Palmeri, & McKinley’s (1994) rule-plusexception (RULEX) model, which first performs hypothesis (rule) testing on single dimensions then inceases the number dimensions as needed. Despite its prominence in modern categorization theory, evidence for the operation of selective attention has always amounted to demonstrations that dimensions vary in their influence on explicit categorization judgments, but not on the Eyetracking and Selective Attention 4 operation of selective attention per se (Lamberts, 1998). Accordingly, this study had two main goals. The first was to determine if eyetracking data would support the claim that learners allocate their attention to optimize classification performance. The second was to determine whether the changes in eye movements during the course of learning was well described by ALCOVE or RULEX. To these ends, we replicated the Shepard et al. (1961) category learning experiment with an eyetracker. The Shepard et al. (1961) Study Shepard et al. (1961) constructed stimuli with three binary-valued dimensions, resulting in eight stimuli split into two categories. There were six unique divisions of stimuli into categories, four of which are shown in Figure 1. Here, the dimensions have been arbitrarily instantiated by shape, color, and size. Type I is the most basic category structure, requiring information from a single dimension for classification (shape in Fig. 1). The Type II structure is an exclusive-or problem along two relevant dimensions (size and shape in Fig. 1). Type IV is a singledimension-plus-exception structure in which all three dimensions are relevant although not equally so. Finally, in the Type VI structure, all three dimensions are equally relevant. Shepard et al.’s central finding was that the ordering among the category structures from least to most difficult was Type I < II < IV < VI. Because this ordering mirrors the number of dimensions for correct classification, it was taken as evidence for selective attention in category learning. We tested participants wearing an eyetracker on the four category structures shown in Figure 1. Our first question was whether, as predicted by current theories, participants would limit their attention (measured by eye movements) to only those stimulus dimensions needed to classify each structure: 1, 2, 3, and 3 dimensions. Our second question was whether the changes in eye movements during learning would support a gradual or rule-based learning account. According to ALCOVE, participants Eyetracking and Selective Attention 5 should begin by examining all dimensions and gradually reduce the dimensions they fixate to the minimum (to one for Type I and two for Type II). According to RULEX, participants should begin by examining one dimension, and increase the dimensions they fixate as needed (to two for Type II and three for Types IV and VI). Method Participants A total of 72 New York University undergraduates were randomly assigned to conditions. Materials For eye movement analysis, the dimensions of our stimuli were separated in space (as in Experiment I of Shepard et al., 1961). The binary dimensions were realized by a pair of characters ($ and ¢, ? and ! , and x and o). An example stimulus is presented in Figure 2. The assignment of physical dimensions and location to the abstract category structure was counterbalanced. Our SMI Eyelink eyetracking system corrected for drift between trials, recording a single eye. Procedure Participants classified exemplars as belonging to either a “red” or “green” category by pressing colored buttons on a button box. Exemplars remained visible for 4s after auditory feedback. Exemplars were presented randomly in blocks of 8. The experiment ended after four consecutive errorless blocks or after a 28 block maximum. Eyetracking Dependent Variables To derive eyetracking measures, we defined rectangular areas of interest (AOIs) that encompassed the symbol dimensions on the CRT. Based on these AOIs we computed three per trial measures: Number of dimensions fixated (between 0 and 3). Eyetracking and Selective Attention 6 Proportional fixation time (between 0 and 1) provides information regarding which dimensions participants found most important. It is the time spent fixating each dimension divided by the total time spent fixating all three dimensions. Relative priority (between 0 and 1) captures the ordering of fixations. Dimensions receive greater priority the earlier it is fixated. Results We replicated the basic ordering of problem difficulty found by Shepard et al. (1961). The number of participants out of 18 that reached the learning criterion of four perfect blocks in a row was 18, 18, 16, and 10 for Types I, II, IV, and VI, respectively. We also analyzed the number of blocks to criterion (assuming conservatively, that nonlearners would have reached perfect performance by block 29). The average number of blocks to criterion was 7.1, 14.1, 18.1, and 22.9 for Types I, II, IV, and VI; F(3, 68) = 24.8, p < . 01. All pairwise comparisons (I vs. II, II vs. IV, IV vs. VI) were significant (p < .05). Finally, the total number of errors committed for the four problems types was 8.2, 31.2, 36.9, and 70.6; F(3, 68) = 23.4, p < . 01 (all pairwise comparisons p < .05, except the Type II vs. IV contrast, p < .15). Thus, this experiment indeed replicated the basic problem type ordering: Type I < II < IV < VI. Our primary goal, and a first for the categorization field, was to determine if selective attention can be measured directly from eye movements. Figure 3 presents the average number of dimensions fixated for learners in each category structure in each block. For participants who reached criterion before the 28th block, we assumed their eye movement data for the remaining blocks would have been identical to the mean of their last actual four blocks. Figure 3 illustrates that learners in this experiment indeed allocated their attention (as measured by eye movements) to only those stimulus dimensions needed to solve the classification problem. By the end of learning, the Type I group was Eyetracking and Selective Attention 7 examining one dimension; only 1 of the 18 Type I participants did not restrict eye movements to the one relevant dimension. Similarly, the Type II group was attending to two dimensions; only 2 of the 18 participants examined all three dimensions. Finally, all Type IV and VI participants generally fixated three dimensions. These results provide direct evidence that the acquisition of categories involves selective attention to only those dimensions needed for judging category membership. A second goal of the present study concerns the process by which participants learned to attend selectively. We considered two possibilities. The first, based on ALCOVE, was that attention would shift gradually to the relevant dimensions. The second, based on RULEX, was that attention would first be allocated to a single dimension (as simple 1D rules were being tested) and then shift to include more dimensions as needed. As Figure 3 indicates, the average group data clearly support an ALCOVE-like gradual learning view of selective attention as subjects in all groups examined between two and three dimensions. But Figure 3 is a result of averaging over participants. Does gradual learning hold for individuals? Learning to Attend Selectively To answer this question we examined the pattern of eye movements for each of the Type I participants. The Type I problem is ideal for this purpose because it is associated with the greatest reduction in the number of dimensions fixated--and hence the g