Varying variation: the effects of within- versus across-feature differences on relational category learning

Katherine A. Livins, Michael J. Spivey, Leonidas A. A. Doumas
2015 Frontiers in Psychology  
Learning of feature-based categories is known to interact with feature-variation in a variety of ways, depending on the type of variation (e.g., Markman and Maddox, 2003) . However, relational categories are distinct from feature-based categories in that they determine membership based on structural similarities. As a result, the way that they interact with feature variation is unclear. This paper explores both experimental and computational data and argues that, despite its reliance on
more » ... al factors, relational category-learning should still be affected by the type of feature variation present during the learning process. It specifically suggests that within-feature and across-feature variation should produce different learning trajectories due to a difference in representational cost. The paper then uses the DORA model (Doumas et al., 2008) to discuss how this account might function in a cognitive system before presenting an experiment aimed at testing this account. The experiment was a relational category-learning task and was run on human participants and then simulated in DORA. Both sets of results indicated that learning a relational category from a training set with a lower amount of variation is easier, but that learning from a training set with increased within-feature variation is significantly less challenging than learning from a set with increased across-feature variation. These results support the claim that, like feature-based category-learning, relational category-learning is sensitive to the type of feature variation in the training set. Citation: Livins KA, Spivey MJ and Doumas LAA (2015) Varying variation: the effects of within-versus across-feature differences on relational category learning. Front. Psychol. 6:129.
doi:10.3389/fpsyg.2015.00129 pmid:25709595 pmcid:PMC4321646 fatcat:hillcmnwgfgo3gb2b7eiitfdwm