Explanatory Coherence and Belief Revision in Naive Physics.

Autor/inn/en	Ranney, Michael; Thagard, Paul
Institution	Pittsburgh Univ., PA. Learning Research and Development Center.
Titel	Explanatory Coherence and Belief Revision in Naive Physics.
Quelle	(1988), (15 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Cognitive Processes; Cognitive Restructuring; Cognitive Structures; College Science; Computer Oriented Programs; Computer Uses in Education; Computers; Concept Formation; High Schools; Higher Education; Learning Processes; Learning Theories; Misconceptions; Physics; Secondary School Science + Suchen Sie Ihr Suchwort? Cognitive process; Kognitiver Prozess; Cognitive structure; Kognitive Struktur; Computerprogramm; Computernutzung; Digitalrechner; Concept learning; Begriffsbildung; High school; Oberschule; Hochschulbildung; Hochschulsystem; Hochschulwesen; Learning process; Lernprozess; Learning theory; Lerntheorie; Missverständnis; Physik
Abstract	Students of reasoning have long tried to understand how people revise systems of beliefs. This paper maintains that people often change their beliefs in ways driven by considerations of explanatory coherence. In this report, a computational model is described of how experimental subjects revise their naive beliefs about physical motion. First, instances in which subjects changed their beliefs while learning elementary physics are presented. Each of these cases involved an individual's attempt to explain a surprising observation. Next it is shown how their belief revisions can be modeled using ECHO, a connectionist computer program that uses constraint-satisfaction techniques to implement a theory of explanatory coherence. The resulting simulations even captured temporal characteristics of the observed changes in beliefs. Finally, the model's representational sensitivity and procedural robustness are discussed. The paper concludes by showing how ECHO can be used to generate empirical predictions about subjects' current beliefs. (ML)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC