This article presents a process account of some typicality effects and related similarity-dependent accuracy and response time phenomena that arise in the context of supervised concept acquisition. We describe Symbolic Concept Acquisition (SCA), a computational system that acquires and activates category prediction rules. In contrast to gradient representations, SCA performs by probing for prediction rules in a series of discrete steps. For learning new rules, it acquires general rules but then incrementally learns more specific ones. In describing SCA, we emphasize (...) its functionality in terms of accuracy and efficiency and motivate its design within the set of symbolic mechanisms and memory structures defined by the Soar architecture (Laird, Newell & Rosenbloom, 1987). For replicating human behavior, we first show how SCA exhibits some typicality effects in the course of learning responding faster and more accurately to more typical test examples. Then, using data from human experiments, we evaluate SCA's qualitative predictions on accuracy and response time on individual dataset instances. We show how SCA's predictions correlate with human data across three experimental conditions concerning the effect of instruction on learning strategy. (shrink)

Abstract The notion of necessary new terms (predicates) is proposed. It is shown that necessary new predicates in first?order logic must be directly, recursively defined. I present a first?order inductive learning algorithm that introduces new necessary predicates to model scientific revolution in which a new language is adopted. I demonstrate that my learning system can learn a genetic theory with theoretical terms which, after being induced by my system, can be interpreted as either types of genetic properties (dominant or recessive) (...) or genes, depending on the representation of the hypotheses of the same theoretical terms. (shrink)

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