title: 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.
authors.affiliation: Department of Psychology, University of California, Berkeley.
authors.forename: Dawn
authors.initials: D
authors.lastname: Chen
authors.affiliation: Department of Psychology, University of California, Berkeley.|Department of Statistics, University of California, Los Angeles.
authors.forename: Hongjing
authors.initials: H
authors.lastname: Lu
authors.affiliation: Department of Psychology, University of California, Berkeley.
authors.forename: Keith J
authors.initials: KJ
authors.lastname: Holyoak
mesh_terms: D003071:Cognition / Q000502:physiology*; D005793:Generalization, Psychological / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008960:Models, Psychological; D014163:Transfer, Psychology / Q000502:physiology*
keywords: Bayesian models; Deduction; Hypothetical reasoning; Induction; Relation learning; Transitive inference