Cognitive Science 41 (S5):1062-1092 (2017)

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, can be extended to make generative inferences. The model is able to make quasi-deductive transitive inferences 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.
Keywords Transitive inference  Bayesian models  Relation learning  Hypothetical reasoning  Induction  Deduction
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Reprint years 2017
DOI 10.1111/cogs.12455
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How to Build a Baby: II. Conceptual Primitives.Jean M. Mandler - 1992 - Psychological Review 99 (4):587-604.

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