You Only Had to Ask Me Once: Long-term Retention Requires Direct Queries During Learning

You Only Had to Ask Me Once: Long-term Retention Requires Direct Queries During Learning Yasuaki Sakamoto (ysakamot@stevens.edu) School of Technology Management, Stevens Institute of Technology Hoboken, NJ 07030 USA Bradley C. Love (brad_love@mail.utexas.edu) Department of Psychology, The University of Texas at Austin Austin, TX 78712 USA Abstract instead a missing feature value is inferred, followed by corrective feedback. What is the key to retaining learned study materials? We examine the role that direct queries play in the retention of category information acquired through inference learning. In inference learning, learners predict an unknown property of an item given its other properties and category membership. We manipulated query frequency across properties during inference learning and found that properties that were queried less often were remembered as well as properties that were queried more frequently. These effects extended from minutes to multiple day delays. Thus, asking about properties during inference training only a few times can immediately promote learning and greatly enhance the long-term retention of category knowledge. We situate our results in the broader memory and education literatures and consider how these findings constrain the development of category learning models. Keywords: Retention; category learning; inference; direct query; explicit evaluation. Introduction Figure 1: An inference learning trial in the current experiment is shown, in which the learner predicts an unknown property. The stimulus on the left differs from the stimulus on the right only on the values of the queried dimension (in this case, smaller vs. larger body size). What is the key to long-term retention of studied materials? In the present work, we investigate the role that direct queries play in category learning and recognition memory. The present work is unique in measuring retention over multiple day delays in category learning tasks. Retention has obvious importance in education and other everyday activities. However, the importance of consolidation to memory performance has been underappreciated by cognitive psychologists in general (Wixted, 2005) and grossly neglected by category learning researchers in particular. For instance, category learning studies that examine retention often impose delays of only a few minutes (e.g., Palmeri & Nosofsky, 1995; Sakamoto & Love, 2004). In addition to examining the role that direct query plays in consolidation, the current work advances our understanding of how category learning works using inference as the induction method. Typically, the classification induction method is used in category learning tasks. In classification learning, the learner is shown a stimulus and classifies it as belonging to one of a number of categories and then is supplied with corrective feedback. Like classification learning, inference learning is also a supervised learning method, but differs in that the category label is known and In category learning experiments using the inference method, the learner’s goal is to correctly predict an unknown property of an item given the item’s remaining properties and its category membership. Figure 1 displays an inference learning trial from the present experiment, in which the learner predicts whether the Tiger shark is small or large by guessing whether the left or right side describes the shark correctly. The left side of Figure 1 conveys the same information as the right side except for the queried dimension, size – the learner predicts the unknown property. The property that is queried in inference learning varies across trials. On the next inference learning trial, whether the Tiger shark has darker or lighter shade may be queried given the other properties and the category membership. Inference learning can lead to more efficient learning of the categories and greater knowledge about the categories than classification learning (Sakamoto & Love, 2006; Yamauchi & Markman, 1998). In classification learning, the learner’s goal is to successfully predict the category