Representing, Running, and Revising Mental Models: A Computational Model

Autor/inn/en	Friedman, Scott; Forbus, Kenneth; Sherin, Bruce
Titel	Representing, Running, and Revising Mental Models: A Computational Model
Quelle	In: Cognitive Science, 42 (2018) 4, S.1110-1145 (36 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12574
Schlagwörter	Models; Cognitive Science; Scientific Concepts; Cognitive Structures; Artificial Intelligence + Suchen Sie Ihr Suchwort? Analogiemodell; Kognitionswissenschaft; Cognitive structure; Kognitive Struktur; Künstliche Intelligenz
Abstract	People use commonsense science knowledge to flexibly explain, predict, and manipulate the world around them, yet we lack computational models of how this commonsense science knowledge is represented, acquired, utilized, and revised. This is an important challenge for cognitive science: Building higher order computational models in this area will help characterize one of the hallmarks of human reasoning, and it will allow us to build more robust reasoning systems. This paper presents a novel "assembled coherence (AC) theory" of human conceptual change, whereby people revise beliefs and mental models by constructing and evaluating explanations using fragmentary, globally inconsistent knowledge. We implement AC theory with "Timber," a computational model of conceptual change that revises its beliefs and generates human-like explanations in commonsense science. "Timber" represents domain knowledge using predicate calculus and qualitative model fragments, and uses an abductive model formulation algorithm to construct competing explanations for phenomena. "Timber" then (a) scores competing explanations with respect to previously accepted beliefs, using a cost function based on simplicity and credibility, (b) identifies a low-cost, preferred explanation and accepts its constituent beliefs, and then (c) greedily alters previous explanation preferences to reduce global cost and thereby revise beliefs. Consistency is a soft constraint in "Timber"; it is biased to select explanations that share consistent beliefs, assumptions, and causal structure with its other, preferred explanations. In this paper, we use "Timber" to simulate the belief changes of students during clinical interviews about how the seasons change. We show that "Timber" produces and revises a sequence of explanations similar to those of the students, which supports the psychological plausibility of AC theory. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01