Generative Inferences Based on Learned Relations

Autor/inn/en	Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.
Titel	Generative Inferences Based on Learned Relations
Quelle	In: Cognitive Science, 41 (2017), S.1062-1092 (31 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12455
Schlagwörter	Inferences; Abstract Reasoning; Learning Processes; Models; Decision Making; Bayesian Statistics; Logical Thinking + Suchen Sie Ihr Suchwort? Inference; Inferenz; Abstraktes Denken; Denken; Learning process; Lernprozess; Analogiemodell; Decision-making; Entscheidungsfindung
Abstract	A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from non-relational inputs. In the present paper, we show that a bottom-up model of relation learning, initially developed to discriminate between positive and negative examples of comparative relations (e.g., deciding whether a sheep is larger than a rabbit), can be extended to make generative inferences. The model is able to make quasi-deductive transitive inferences (e.g., "If 'A' is larger than 'B' and 'B' is larger than 'C', then 'A' is larger than 'C'") and to qualitatively account for human responses to generative questions such as "What is an animal that is smaller than a dog?" These results provide evidence that relational models based on bottom-up learning mechanisms are capable of supporting generative inferences. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01