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)

This article addresses issues in developing cognitive architectures--generic computational models of cognition. Cognitive architectures are believed to be essential in advancing understanding of the mind, and therefore, developing cognitive architectures is an extremely important enterprise in cognitive science. The article proposes a set of essential desiderata for developing cognitive architectures. It then moves on to discuss in detail some of these desiderata and their associated concepts and ideas relevant to developing better cognitive architectures. It argues for the importance of taking (...) into full consideration these desiderata in developing future architectures that are more cognitively and ecologically realistic. A brief and preliminary evaluation of existing cognitive architectures is attempted on the basis of these ideas. (shrink)

Many theories of explanation evaluation are based on context‐independent criteria. Such theories either restrict their consideration to explanation towards a fixed goal, or assume that all valid explanations are equivalent, so that evaluation criteria can be neutral to the goals underlying the attempt to explain. However, explanation can serve a range of purposes that place widely divergent requirements on the information an explanation must provide. It is argued that understanding what determines explanations' goodness requires a dynamic theory of evaluation, based (...) on analysis of the information needed to satisfy the many goals that can prompt explanation; this view conforms to the commonsense idea that people accept and apply explanations precisely if those explanations give the information they need. A range of goals that can underly explanation is examined, and a theory is presented for evaluating whether an explanation provides the information an explainer needs for these goals. The theory is illustrated by sketching its implementation in the computer program, ACCEPTER, which does goal‐based evaluation of the goodness of explanations for surprising events in news stories. (shrink)