title: The divergent autoencoder (DIVA) model of category learning.
abstract: A novel theoretical approach to human category learning is proposed in which categories are represented as coordinated statistical models of the properties of the members. Key elements of the account are learning to recode inputs as task-constrained principle components and evaluating category membership in terms of model fit-that is, the fidelity of the reconstruction after recoding and decoding the stimulus. The approach is implemented as a computational model called DIVA (for DIVergent Autoencoder), an artificial neural network that uses reconstructive learning to solve N-way classification tasks. DIVA shows good qualitative fits to benchmark human learning data and provides a compelling theoretical alternative to established models.
authors.affiliation: Department of Psychology, Binghamton University, Binghamton, New York 13902, USA. kkurtz@binghamton.edu
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Kutrz
mesh_terms: D003071:Cognition; D003210:Concept Formation; D004867:Equipment Design; D006801:Humans; D007858:Learning; D011584:Psychology / Q000295:instrumentation*; D012660:Semantics