Simple, individually unique, and context-dependent learning methods for models of human category learning.

Abstract

The gradient descent optimization method has been a de facto standard learning algorithm in computational models of category learning. However, it can be considered as a normative (vs. descriptive) model of human learning processes. In particular, there are three concerns associated with the learning algorithm-namely, complexity, regularity, and context independency. In response to these limitations, the present study introduces an alternative, hypothesis-testing-like learning algorithm on the basis of a stochastic optimization method. The new learning model, termed SCODEL, provides qualitatively simple interpretations for its implied category-learning processes. Moreover, SCODEL is the first modeling attempt to depict individually unique and context-dependent learning processes. Four simulation studies were conducted and showed that the present model has the competence to operate as several different types of learners in various plausibly real-life situations.