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Generative Inferences Based on Learned Relations
Author(s) -
Chen Dawn,
Lu Hongjing,
Holyoak Keith J.
Publication year - 2017
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12455
Subject(s) - generative grammar , transitive relation , generativity , generative model , relation (database) , computer science , property (philosophy) , artificial intelligence , cognitive psychology , cognitive science , psychology , mathematics , epistemology , social psychology , philosophy , combinatorics , database
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.