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Related Experiment Video

Updated: Apr 16, 2026

Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

Acquisition of a High-precision Skilled Forelimb Reaching Task in Rats

Published on: June 22, 2015

A rational model of function learning.

Christopher G Lucas¹, Thomas L Griffiths², Joseph J Williams³

¹School of Informatics, University of Edinburgh, 10 Crichton St., Edinburgh, EH8 9AB, UK. c.lucas@ed.ac.uk.

Psychonomic Bulletin & Review

|March 4, 2015

Summary

This summary is machine-generated.

People learn continuous variable relationships by estimating functions or using similarity. This study shows these are two views of one solution, offering a unified model for function learning.

Keywords:

Bayesian modeling Function learning

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Area of Science:

Cognitive Science
Machine Learning
Statistics

Background:

Human learning theories often separate explicit function estimation and similarity-based associative learning.
Existing models struggle to unify these distinct learning mechanisms.

Purpose of the Study:

To provide a rational analysis of human function learning.
To unify explicit rule learning and similarity-based learning into a single framework.

Main Methods:

Rational analysis drawing on machine learning and statistics.
Utilizing the equivalence between Bayesian linear regression and Gaussian processes.
Developing a unified probabilistic model of function learning.

Main Results:

Demonstrated that explicit function learning and similarity-based learning are unified under a single probabilistic framework.
Showcased how Bayesian linear regression and Gaussian processes offer a probabilistic basis for similarity.

Conclusions:

Human function learning can be understood as a unified process, integrating rule-based and similarity-based approaches.
The proposed rational model accounts for diverse experimental findings in function learning.