The effects of dual verbal and visual tasks on featural vs. relational category learning

The effects of dual verbal and visual tasks on featural vs. relational category learning Wookyoung Jung (jung43@illinois.edu) Department of Psychology, 603 E. Daniel Street Champaign, IL 61820 USA John E. Hummel (jehummel@illinois.edu) Department of Psychology, 603 E. Daniel Street Champaign, IL 61820 USA Abstract Many studies have examined the distinction between feature- and relation-based categories (Gentner, 2005; Genter & Kurtz, 2005; Jung & Hummel, 2009; Tomlinson & Love, 2011). Those findings suggest that featural and relationl categories have fundamentally different learning algorithms, where relational categories rely on explicit representations and thus require working memory and attention, as opposed to featural categories which may be learned more implicitly. In this study, we investigated further the distinction between feature-and relation-based category learning using a dual task methodology. Our results revealed an interaction: featural category learning was more impaired by a visuospatial dual task than by a verbal dual task, whereas relational category learning was more impaired by the verbal dual task. Our results suggest that in contrast to featural category learning, which may involve mainly non-verbal mechanisms, relational category learning appears to place greater demands on more explicit and attention-demanding verbal or verbally-related learning mechanisms. confidence that anything learned about category learning using feature-based categories will generalize at all to the case of relational categories. For example, the kinds of learning algorithms that work well with feature-based categories (i.e., various kinds of statistical learning) are completely incapable of learning relational categories (Doumas, Hummel & Sandhofer, 2008; Hummel & Holyoak, 2003; Jung & Hummel, 2009; Kittur et al., 2004, One of the clearest examples of this difference comes in the form of peoples’ ability to learn probabilistic (aka family resemblance) category structures. It has been known since the 1970s that people have no difficulty learning categories with probabilistic structures, in which any given feature is likely to belong to a given category (e.g., “bugs” in category A are likely to have one kind of head whereas “bugs” in category B are likely to have another), but no feature is deterministically associated with any given category (e.g., sometimes, bugs from category B will have heads typical of bugs from category A and vice-versa; see Murphy, 2002, for a review). However, as noted by Kittur et al. (2004), such prototype effects have always been observed with feature-based categories. With categories defined by the relations between their exemplars’ features, such prototype effects have proven difficult or impossible to observe (Jung & Hummel, 2009, 2011; Kittur et al., 2004, These differences between peoples’ ability to learn featural and relational categories are consistent with the claim that fundamentally different learning algorithms may be at work in the two cases. For example, whereas associative learning may work in the case of featural categories, relational category learning may require a more sophisticated algorithm based, for example, on structured intersection discovery, in which learners compare examples to one another, retaining what the examples have in common and discarding or discounting the details on which they differ (Gick & Holyoak, 1983; Hummel & Holyoak, 2003; Jung & Hummel, 2009, 2011; Kittur et al, 2004, Key words: featural category learning; relational category learning; dual task; verbal dual task; visuospatial dual task; category learning algorithms The ability to categorize plays a central role in human mental life. We use categories to makes sense of the world. They allow us to generalize knowledge form one situation to another, to decide which objects in the world are fundamentally the same, and to infer the unseen properties of novel category members. Research on categorization has mainly focused on feature-based categories—that is, categories defined by their exemplars’ features, as when “bugs” in one category tend to have a particular kind of head, body and tail and “bugs” in the opposite category tend to have a different kind of head, body and tail (e.g., Taylor and Ross, 2009)—and comparatively little on relation- based categories—i.e., categories by the relations between exemplars’ parts, or by relations between category exemplars and other objects in the world (for reviews, see Gentner, 2005; Goldwater, Markman, & Stilwell, 2011; Jung & Hummel, 2009; Kittur, Hummel & Holyoak, 2004). The distinction between featural and relational categories matters because features and relations are very different things—so different that we can have little or no