The Influence of Presentation Order on Category Transfer

Abstract

This study of supervised categorization shows how different kinds of category representations are influenced by the order in which training examples are presented. We used the well-studied 5-4 category structure of Medin and Schaffer (1978) , which allows transfer of category learning to new stimuli to be discriminated as a function of rule-based or similarity-based category knowledge. In the rule-based training condition (thought to facilitate the learning of abstract logical rules and hypothesized to produce rule-based classification), items were grouped by subcategories and randomized within each subcategory. In the similarity-based training condition (thought to facilitate associative learning and hypothesized to produce exemplar classification), transitions between items within the same category were determined by their featural similarity and subcategories were ignored. We found that transfer patterns depended on whether the presentation order was similarity-based, or rule-based, with the participants particularly capitalizing on the rule-based order.

Learning hierarchically organized science categories: simultaneous instruction at the high and subtype levels

“…Much other research has also pursued techniques for effectively teaching categories at a single level of a hierarchy. Such techniques include ones that manipulate the presentation sequences of training instances from contrasting categories (e.g., Carvalho & Goldstone, 2014; Eglington & Kang, 2017; Kornell & Bjork, 2008; Mathy & Feldman, 2016); the order in which hard versus easy instances are presented (e.g., Pashler & Mozer, 2013); the size of the sets of training instances and which specific training instances to use (e.g., Nosofsky et al, 2018, 2019; Wahlheim et al, 2012); and the use of explicit coaching such as visual highlighting of diagnostic features (Miyatsu, Gouravajhala, Nosofsky, & McDaniel, 2019). Combining these techniques with variants of the presently proposed two-stage response procedure might yield even better learning of hierarchically organized science categories.…”

Recommendations From Cognitive Psychology for Enhancing the Teaching of Natural-Science Categories

“…Thus, the formal models can help guide the search for effective procedures for teaching the categories. The idea is to use the successful models of human category learning to simulate the outcome of different teaching techniques, including a focus on the fundamental instructional questions described earlier (e.g., Khajah, Lindsey, & Mozer, 2014; Mathy & Feldman, 2016; Nosofsky, Sanders, Zhu, & McDaniel, 2018; Patil, Zhu, Kopec, & Love, 2014). The significant advantage is that cognitive psychologists can then focus empirical studies on those techniques that the models predict would be most successful.…”

An order-dependent transfer model in categorization

“…The scale "do re mi fa sol la si" is typically classified as a joyful melody, while the scale "si la sol fa mi re do" is typically classified as a somber one. A large number of studies have confirmed the intuition that the order in which information is presented can alter perception (Birnbaum et al, 2012;Carvalho & Goldstone, 2014a;Clapper, 2014;Rohrer, 2009Rohrer, , 2012, representation (Corcoran et al, 2011;Elio & Anderson, 1981, 1984Mathy & Feldman, 2016;Sana et al, 2016;Zeithamova & Maddox, 2009), and learning (Bloom & Shuell, 1981;Carpenter & Mueller, 2013;Carvalho & Goldstone, 2015a;Helsdingen et al, 2011;Kornell & Bjork, 2008;Kornell et al, 2010;Lipsitt, 1961;Mathy & Feldman, 2009;Samuels, 1969;Sandhofer & Doumas, 2008;. More specific experimental studies have investigated how the temporal context influences the way categories are learned and encoded (Brady, 2008;Carvalho & Goldstone, 2020, 2014bJones & Sieck, 2003;Kang & Pashler, 2012;Li et al, 2012;Mack & Palmeri, 2015;Mcdaniel et al, 2013;Qian & Aslin, 2014;Yan et al, 2017;Zotov et al, 2011;; however, only few of them have attempted to model and account for order effects through the conception and use of computational models.…”