Generative Inferences Based on Learned Relations
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 17, 2016
- Source ID
- 10.1111/cogs.12455
Entities
People
- Dawn Chen
- Hongjing Lu
- Keith Holyoak
Organizations
- National Science Foundation
- Office of Naval Research
- University of California, Berkeley
- University of California, Los Angeles