A rational analysis of rule-based concept learning

This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well-known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7-feature concepts—a more natural setting in several ways—and again finds that the model explains human performance.     

A model of memory in concept learning

A concept learning model was developed and tested in two conjunctive attribute identification tasks. The model includes assumptions about the focus of attention, decision making, and memory for stimulus information and prior decisions. Predictions are made about how S changes his hypothesis following an error. Procedures in both tasks allowed inference of the subject's current hypothesis. The hypothesis selections and error statistics were in the majority of cases accurately predicted by the model. Deviations from predictions on hypothesis sampling occurred for naive Ss but not for trained Ss who were required to state a hypothesis on each trial.     

Categorization,concept learning,and behavior analysis: an introduction

Categorization and concept learning encompass some of the most important aspects of behavior, but historically they have not been central topics in the experimental analysis of behavior. To introduce this special issue of the Journal of the Experimental Analysis of Behavior (JEAB), we define key terms; distinguish between the study of concepts and the study of concept learning; describe three types of concept learning characterized by the stimulus classes they yield; and briefly identify several other themes (e.g., quantitative modeling and ties to language) that appear in the literature. As the special issue demonstrates, a surprising amount and diversity of work is being conducted that either represents a behavior-analytic perspective or can inform or constructively challenge this perspective.     

Transformation rules in concept learning

Two major classes of models have been proposed to explain concept learning: strength models and distance models (Hayes-Roth & Hayes-Roth, 1977). The present study demonstrates that subjects abstract transformation rules as defined by the Franks and Bransford 11971) distance model. Transformation rules characterize how the patterns of a concept differ from each other. Transformation rules are inconsistent with strength models, which assume that subjects abstract component features and not relational information characterizing the differences among patterns. Whether a strength model or a distance model is more appropriate in other instances of concept learning is probably a function of task demands, stimulus characteristics, and subject characteristics.     

Striatal activity in concept learning

Striatal learning systems have been implicated in learning relationships between visual stimuli and outcomes. In the present study, the activity of the striatum during visual concept learning in humans was examined by using functional magnetic resonance imaging (fMRI). Participants performed three concept-learning tasks and a baseline task. The participants were trained to criterion before fMRI scanning on two tasks, verbal and implicit. In the verbal task, classification could be performed on the basis of a simple verbal rule, but in the implicit task, there was no simple verbal rule. The novel-implicit learning task, in which an implicit structure was used, was not encountered by the participants before scanning. Across all three concept-learning tasks, there was significant activation in the striatum, in comparison with the baseline task. The striatum was recruited similarly in classification when the participants had different levels of expertise (novel-implicit vs. verbal and implicit) and were able to verbalize their learning to different degrees (verbal vs. implicit and novel-implicit). There was left lateral occipital activation when learning was implicit (implicit and novel-implicit), but not when learning was easily verbalized (verbal).     

Abstraction of contingency in concept learning

It is generally agreed that concept learning involves the abstraction of some general representation or schema. Just what is abstracted, however, and how it is used in the classification of sets of stimuli in the natural world or in the laboratory, remain outstanding questions. In this paper a hypothesis involving contingency abstraction is described as a possible solution to these questions. An experiment which manipulated measured contingency in a concept-learning task, and which offered empirical support for the hypothesis, is reported. The advantages of a contingency-abstraction theory of concept learning are briefly discussed.     

An algebra of human concept learning

An important element of learning from examples is the extraction of patterns and regularities from data. This paper investigates the structure of patterns in data defined over discrete features, i.e. features with two or more qualitatively distinct values. Any such pattern can be algebraically decomposed into a spectrum of component patterns, each of which is a simpler or more atomic “regularity.” Each component regularity involves a certain number of features, referred to as its degree. Regularities of lower degree represent simpler or more coarse patterns in the original pattern, while regularities of higher degree represent finer or more idiosyncratic patterns. The full spectral breakdown of a pattern into component regularities of minimal degree, referred to as its power series, expresses the original pattern in terms of the regular rules or patterns it obeys, amounting to a kind of “theory” of the pattern. The number of regularities at various degrees necessary to represent the pattern is tabulated in its power spectrum, which expresses how much of a pattern's structure can be explained by regularities of various levels of complexity. A weighted mean of the pattern's spectral power gives a useful numeric summary of its overall complexity, called its algebraic complexity. The basic theory of algebraic decomposition is extended in several ways, including algebraic accounts of the typicality of individual objects within concepts, and estimation of the power series from noisy data. Finally some relations between these algebraic quantities and empirical data are discussed.     

Stages in concept learning. III

Abstract.— In a task-oriented concept learning situation with 30 children, 12–13 years of age, as subjects, the concept to be learned was made unfamiliar by using labels which had no bearing on the defining attributes. Under these conditions it was found that the categorization of the stimulus figures became more difficult and that identification of the defining attributes preceded an adequate categorization of the stimulus figures. But forcing the child into identification of the attributes before he started to categorize the stimulus figures did not result in better concept learning scores.     

Strengthening concept learning by repeated testing

The aim of this study was to examine whether repeated testing with feedback benefits learning compared to rereading of introductory psychology key‐concepts in an educational context. The testing effect was examined immediately after practice, after 18 days, and at a five‐week delay in a sample of undergraduate students (n = 83). The results revealed that repeated testing with feedback significantly enhanced learning compared to rereading at all delays, demonstrating that repeated retrieval enhances retention compared to repeated encoding in the short‐ and the long‐term. In addition, the effect of repeated testing was beneficial for students irrespectively of working memory capacity. It is argued that teaching methods involving repeated retrieval are important to consider by the educational system.     

The simplicity principle in human concept learning

How do we learn concepts and categories from examples? Part of the answer might be that we induce the simplest category consistent with a given set of example objects. This seemingly obvious idea, akin to simplicity principles in many fields, plays surprisingly little role in contemporary theories of concept learning, which are mostly based on the storage of exemplars, and avoid summarization or overt abstraction of any kind. This article reviews some evidence that complexity minimization does indeed play a central role in human concept learning. The chief finding is that subjects' ability to learn concepts depends heavily on the concepts' intrinsic complexity; more complex concepts are more difficult to learn. This pervasive effect suggests, contrary to exemplar theories, that concept learning critically involves the extraction of a simplified or abstracted generalization from examples.