Distinguishing between Category-based and Similarity-based Induction

Distinguishing between Category-based and Similarity-based Induction Tracey Miser (miser.4@osu.edu) Department of Psychology, 1835 Neil Avenue Columbus, OH43210 USA Vladimir Sloutsky (sloutsky.1@osu.edu) Department of Psychology, 1835 Neil Avenue Columbus, OH43210 USA Abstract Performing inductive generalizations is critical for learning, yet there is much debate regarding the mechanisms underlying this ability. One view posits that similarity-based induction, utilizing perceptual features, may allow for increased encoding and higher memory accuracy on recognition tests. While category-based induction, utilizing semantic information, may result in limiting encoding of perceptual detail, thus resulting in decreased memory accuracy. In Experiment 1, we attempted to impair spontaneous categorization by presenting a second Working Memory load task. In Experiment 2, we attempted to impair perceptual processing by introducing a second Visual Search task. Results indicate that adult participants can rely on either mechanism when performing induction. Keywords: Induction; Learning; Memory. Introduction The ability to generalize from the known to novel is a critical aspect of cognition – this ability allows expanding knowledge to new situations. At the same time, the learner may not know how far new knowledge can be expanded outside of the learning situation. Suppose that one learned that adenosine promotes myelination in the brain of the Capuchin monkey. Should this knowledge be generalized to New World monkeys, all monkeys, all primates, or all mammals? One way of generalizing knowledge is by identifying a common category that licenses such generalization. For example, one may decide that MONKEY is such a category and generalize knowledge to all monkeys. However, while knowledge of categories is useful, it is not necessary for inductive generalization. For example, one may decide that animals share a property to the extent their similarity exceeds some criterial value. The latter mechanism seems to be a good candidate for generalization early in development, whereas the former could be a product of development. However, despite the fact that inductive generalization exhibits early onset (Baldwin, Markman, & Melartin, 1993; Gelman & Markman, 1986; Sloutsky, Lo, & Fisher, 2001; Welder & Graham, 2001), the mechanisms underlying early induction are hotly debated. According to the naive theory approach (see Murphy, 2002, for a review) induction is a two-step process: children first identify encountered entities as members of categories, and, if entities belong to the same category (say, the same natural kind), then infer that these entities share many properties. The inference is licensed by children’s assumptions that members of some categories (such as, for example, natural kinds) share many properties. Given that children are more likely to know basic-level categories (e.g., MONKEY) than superordinate categories (e.g., MAMMAL), they are more likely to generalize properties within basic-level categories. According to another position (i.e., the similarity view), induction is a generalization process, and young children generalize on the basis of multiple commonalities, or similarities, among presented entities (e.g., Jones & Smith, 2002; McClelland & Rogers, 2003; Sloutsky, Fisher, & Lo, 2001; Sloutsky, 2003, Sloutsky & Fisher, 2004a, 2004b). This view does not attribute conceptual assumptions to young children. In an attempt to address these issues, Sloutsky and Fisher (2004b; Fisher & Sloutsky, 2005) introduced Induction-then-Recognition (ITR) paradigm. The idea is based on the following reasoning. There is a well-known “level-of-processing effect” – deeper semantic processing facilitates correct recognition of presented items, increasing the proportion of “hits” (Craik & Lockhart, 1972; Craik & Tulving, 1975). At the same time, deeper processing also results in higher levels of memory intrusions – false recognition of non-presented “critical lures” of semantically associated or categorically related items (e.g., Koutstaal & Schacter, 1997; Rhodes & Anastasi, 2000; Thapar & McDermott, 2001). Due to elevated levels of false alarms, the net result of deep semantic processing on recognition accuracy (i.e., Hits – False Alarms) is negative. At the same time, it is known that focusing on perceptual details of pictorially presented information leads to more accurate recognition (Marks, 1991) – although hits might be slightly lower, false alarms are significantly lower than under deep semantic processing. Therefore, these memory findings suggest that categorization (which is a variant of deeper semantic processing) would result in a higher level of memory intrusions and thus in lower recognition accuracy