The Role of Category Structure in Category Learning

The Role of Category Structure in Category Learning Hyungwook Yim (yim.31@osu.edu) Department of Psychology, The Ohio State University 267 Psychology Building, 1835 Neil Avenue Columbus, OH 43210, USA Vladimir M. Sloutsky (sloutsky.1@osu.edu) Department of Psychology, The Ohio State University 239 Psychology Building, 1835 Neil Avenue Columbus, OH 43210, USA Abstract Two category-learning experiments were conducted to examine the role of category structure and learning regime in category learning. We particularly focused on effects of these factors on selective attention, which was measured by eye- tracking methods. Results show that even though supervision was weaker than in previous studies, attention optimization and cost of attention were observed during category learning (Experiment 1). Moreover, there were faster learning and stronger attention optimization when statistically denser categories were learned (Experiment 2). At the same time, there were weaker costs of selective attention when learning denser categories than when learning sparser categories. Results are discussed in relation to theories of category learning. Keywords: category learning, cost of selective attention, category structure, eye tracking Introduction Selective attention is one of the key components in category learning (Kruschke, 1992; Nosofsky, 1986; Shepard, Hovland, & Jenkins, 1961). The ability to selectively attend to category-relevant dimensions aids the learner to ignore category-irrelevant information and makes learning more efficient. For example, when learning how to distinguish Siberian Huskies from Alaskan Malamutes, which look very similar, the color of the eyes is one of the relevant features one should look for (most Huskies have blue eyes and Malamutes have brown eyes). Therefore, learning to focus on the color of the eyes while ignoring other irrelevant features (e.g. color of the fur or markings) would aid learning the two categories. Selective attention could be captured in category learning tasks that involve eye-tracking as attention optimization, where looking to category- relevant information increases and looking to irrelevant information decreases (Hoffman & Rehder, 2010). However, optimizing one’s attention to the current category-relevant dimension may result in learning to ignore the category-irrelevant dimension, which results in learned inattention to the irrelevant dimension (Kruschke & Blair, 2000). Therefore, if a new to-be-learned category has a category-relevant dimension that was previously irrelevant, learning may become more difficult, which represents a cost of selective attention. For example, when learning to distinguish meerkats from prairie dogs, which again look very similar, the shape of the ears is one of the good dimensions to look. However, if one has previously learned how to distinguish Huskies from Malamutes, where eyes were attended and ears were ignored, learning to attend to the once-ignored ears would be hindered. The close link between attention optimization and the cost of selective attention has been demonstrated in previous research (e.g., Hoffman & Rehder, 2010). In their study, participants were given either a supervised classification task (e.g. classifying a stimulus into category A or B) or a supervised inference task (e.g. inferring the missing feature of a stimulus that belongs to a certain category) and their eye movements were recorded. Since the classification task (e.g. focusing on the color of the eyes to classify Huskies and Malamutes) required attention optimization to the relevant dimension, results showed cost of selective attention when learning a new category. On the other hand, since the inference task (e.g. figuring out whether a Malamute has blue eyes or brown eyes) does not require attention optimization, the cost did not occur when learning the next category. Therefore, the study showed that (a) the characteristics of the task affect allocation of attention and (b) when attention optimization occurred, the cost of selective attention also followed. Although attention may be affected by the characteristics of the task (i.e., classification vs. inference) it can also be affected by category structure. Categories that have multiple correlated dimensions (or statistically dense categories) may be learned without selective attention, whereas categories that have few relevant dimensions (or statistically sparse categories) may require selective attention (Kloos & Sloutsky, 2008; Sloutsky, 2010). For example, when learning the category dog, many dimensions are relevant (e.g. nose, fur, four-legs, etc.) and therefore it is relatively easy to learn. However, when learning abstract concepts such as friction, very few dimensions are relevant among many irrelevant dimensions (e.g. a car trying to stop at the red light and a person trying to open a jar both shows friction). Therefore, to learn a sparse category one has to “selectively attend” to the relevant dimension among many other irrelevant dimensions. Finally, the deployment of selective attention may be also affected by learning regime. Since supervised learning provides information about the relevant dimension, it is more likely to recruit selective attention than unsupervised