The Interactions of Category Structure and Supervision in Category Learning: a comparative approach

The Interactions of Category Structure and Supervision in Category Learning : a comparative approach Hyungwook Yim (yim.31@osu.edu) Department of Psychology, The Ohio State University 1835 Neil Avenue, Columbus, OH 43210, USA Leyre Castro (leyre-castroruiz@uiowa.edu) Edward A. Wasserman (ed-wasserman@uiowa.edu) Department of Psychology, The University of Iowa E11 Seashore Hall, Iowa City, IA 52242, USA Vladimir M. Sloutsky (sloutsky.1@osu.edu) Department of Psychology, The Ohio State University 1835 Neil Avenue, Columbus, OH 43210, USA Abstract The current study investigated the interactions among cate- gory structure, supervision, and the ability to selectively attend during category learning. Specifically, we compared pigeons’ with human adults’ category learning using artificial categories to examine the role of selective attention in category learning. Results showed that pigeons benefit more from supervision, and unlike human adults, the benefit is stronger for sparse cat- egories. Moreover, whereas supervision did not affect human adults’ generalization performance, low-supervision resulted in lower generalization for pigeons. The results were discussed in terms of the difference in utilizing the supervisory signal, and the ability to selectively attend to category relevant infor- mation. Keywords: category learning; selective attention; supervision; category structure; comparative study. Introduction Selective attention is one of the crucial components in cate- gory learning (Shepard, Hovland, & Jenkins, 1961). Impor- tantly, selective attention supports efficient category learning and generalization since it enables one to focus on category- relevant information while ignoring category-irrelevant infor- mation (Mackintosh, 1965). For example, when learning how to distinguish halibuts from flounders, the shape of the tail is a relevant dimension. Once one learns to focus on the shape of the tail and ignore other parts of the fish, distin- guishing the two fishes becomes efficient when encountering new instances. Moreover, since selective attention undergoes marked development due to the maturation of the prefrontal cortex (PFC) (Diamond, 2002; Hanania & Smith, 2010), se- lective attention has been considered to be one of the main factors in explaining the development of category learning (Sloutsky, 2010). Along with selective attention, the structure of the cate- gory is another important factor that shapes the process of category learning. Among many, one way to view category structure is by its density (Gentner, 1981; Kloos & Sloutsky, 2008). Specifically, Kloos & Sloutsky (2008) proposed a for- mal measurement for category density using entropy values. Category density was defined by ”the ratio of variance rel- evant for category membership to the total variance across members and nonmembers of the category”. Therefore, dense categories have low within variability and high between vari- ability, whereas sparse categories have low between variabil- ity and high within variability. For example, dogs and cats are dense categories since they have many overlapping fea- tures within their category, but are also distinguishable from other categories. Due to the redundancy of the category rele- vant information it is known that even infants are able to learn dense categories (e.g., Quinn, Eimas, & Rosenkrantz, 1993). On the other hand, chest x-rays of different kinds of diseases could be considered as sparse categories. For example, it is hard to distinguish patient who have pneumonia from those who do not or even from patients who have other diseases. This is due to the fact that while many x-rays look alike, only a few features are relevant in diagnosing a certain disease. Also, having only little category relevant information makes category learning hard, and requires extensive training (e.g., Palmeri, Wong, & Gauthier, 2004). Naturally, learning sparse categories requires selective at- tention, whereas dense categories could be learnable with- out selective attention. From this point of view, evidence has shown that 3- to 4-month-olds do not selectively attend to category relevant dimensions when learning dense cate- gories (Best, Yim, & Sloutsky, 2013), and adults could learn dense categories using distributed attention (Best, Robinson, & Sloutsky, 2014). Finally, supervision also interacts with the above two com- ponents during category learning, where supervision aids se- lective attention to find the category-relevant information. The distinction between unsupervised and supervised learn- ing in category learning is well recognized (Love, 2002), and different underlying mechanisms have been also proposed (Love, Medin, & Gureckis, 2004). On the other hand, the interaction among the amount of supervision, category struc- ture, and the ability to selectively attend is not extensively studied. Some evidence has shown that sparse categories ben- efit from supervision more than dense categories (Kloos & Sloutsky, 2008). It was argued that the use of supervisory sig-