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Related Experiment Video

Updated: May 20, 2026

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Bayesian analogy with relational transformations.

Hongjing Lu¹, Dawn Chen, Keith J Holyoak

¹Department of Psychology, University of California, 405 Hilgard Avenue, Los Angeles, CA 90095-1563, USA. hongjing@ucla.edu

Psychological Review

|July 11, 2012

Summary

This summary is machine-generated.

This study introduces Bayesian Analogy with Relational Transformations (BART), a model that learns relational representations from feature vectors. BART successfully solves complex analogies, suggesting a bottom-up approach to high-level reasoning.

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

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RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

Cognitive Science
Artificial Intelligence
Computational Neuroscience

Background:

Understanding how humans acquire relational representations for high-level reasoning is a key challenge.
Existing models often struggle to bridge the gap between feature-level data and abstract relational understanding.

Purpose of the Study:

To explore bottom-up learning mechanisms for inducing relational representations from feature vectors.
To develop and test a computational model capable of solving analogy problems using learned relational representations.

Main Methods:

Introduced Bayesian Analogy with Relational Transformations (BART), a novel computational model.
Coded animal pair features using continuous-valued vectors (human ratings, normed ratings, or topic model outputs).
Applied BART to learn first-order comparative relations and solve 4-term analogies.

Main Results:

BART successfully induced first-order relations from positive examples only, represented as probabilistic weight distributions.
Learned representations enabled classification of novel relations and demonstrated a symbolic distance effect.
BART reliably solved 4-term analogies by transforming learned representations through importance-guided mapping.

Conclusions:

Structured analogies can be solved using representations induced from unstructured feature vectors via bottom-up mechanisms.
Findings support the potential of algorithmic and neural models for relational thinking and abstract thought evolution.
Provides a proof-of-concept for learning abstract relations from raw data.