Re-learning labeled categories reveals structured representations

Re-learning labeled categories reveals structured representations Andrew T. Hendrickson; George Kachergis; Caitlin M. Fausey; Robert L. Goldstone ({athendri, gkacherg, cfausey, rgoldsto} @indiana.edu) Department of Psychological and Brain Sciences, Indiana University 1101 East Tenth Street, Bloomington, IN 47405 USA Abstract How do people learn to group and re-group objects into labeled categories? In this paper, we examine mechanisms that guide how people re-represent categories. In two experiments, we examine what is easy and what is hard to relearn as people update their knowledge about labeled groups of objects. In Study 1, we test how people learn and re-learn to group objects that share no perceptual features. Data suggest that people easily learn to re-label objects when the category structure remains the same. In Study 2, we test whether more general types of labeling conventions -- words that do or do not correspond with object similarities -- influence learning and re-learning. Data suggest that people are able to learn either kind of convention and may have trouble switching between them when re-structuring their knowledge. Implications for category learning, second language acquisition and updating representations are discussed. Keywords: categories, knowledge change labels, learning and transfer, Introduction An eighth-grade science student will happily tell you that she has just learned the electrons and nucleus of an atom are very similar to the planets and sun in our solar system. She now has a “multi-body orbiting systems” category with both of these systems as members. This approach is a classic example of transferring knowledge: information she previously learned about the solar system can be applied to the atom. But the story doesn’t end there: a physicist will tell you the truth is that an atom doesn’t really work that way, electrons are quantum wave functions that do not orbit a central point. New information causes the analogy to break down and if the eighth-grader continues to study physics she will learn new categorizations because the solar system belongs with systems explained by Newtonian mechanics while the atom belongs with quantum systems. The process of relearning categories and reshaping knowledge is important not only for shifting from novice to expert but also for learning new ways to use words. Many languages group objects, relations and events in different ways (e.g., Majid, Boster, & Bowerman, 2008; Malt, Sloman, & Gennari, 2003; Wolff, Jeon, & Yu, 2009) and second language learners must categorize in new ways in order to speak their second language conventionally. In laboratory category learning tasks, relearning has been studied using highly structured, binary dimensions. Using 3- dimensional binary stimuli, Kruschke (1996) found a clear hierarchy in the speed of relearning. Preserving the same classification rule but reversing the response options is relearned the fastest, and switching to a new classification rule that involves a previously relevant dimension is more quickly learned than a rule using a previously irrelevant dimension. This pattern of results is consistent with the reversal learning literature (e.g., Kendler & D’Amato, 1954) and successfully modeled with straightforward extensions of many attention-shifting exemplar-based categorization models (e.g., Kruschke, 1996). The main goal of this work is to examine category re- learning for categories that are not clearly defined by rules. Many categories in the world and in language do not obey a simple rule-based classification scheme, and it is interesting to consider the challenges that learners may face as they structure and re-structure their knowledge about these categories. In this work, we focus on how people learn to group and re-group objects into labeled categories. Examining how people re-learn to group objects into categories has the potential to reveal insights into three important issues. First, does existing evidence about category learning and re-learning (e.g., with rule-based categories) extend to other kinds of categories? This is important to understand for more general theories of knowledge development. Second, how do people represent labeled groups of objects? What role does a label play in structuring knowledge (e.g., label-as-feature vs. label-as- category-marker, Deng & Sloutsky, 2012; Gelman & Markman, 1986)? This hotly-debated issue may benefit from data about patterns of re-learning because transfer paradigms are a useful way to assess what has been represented, on the logic that people are better able to re- learn structures that closely match what they had originally represented (e.g., Kruschke, 1996). Finally, second language learners are a large part of the world’s population and when people learn a new language they often must learn to categorize objects in different ways. What are the mechanisms that guide this re-representation? As a first step toward these aims, here we consider a variety of potential changes between initial learning and relearning. In the experiments described below, people first learn a category structure and then are asked to relearn across a variety of potential changes. These changes reflect potential real-world relearning situations and the question becomes: what is easy and what is hard to re-learn as people re-structure their knowledge about labeled groups of objects?