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Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

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Related Experiment Video

Updated: Aug 2, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Learning to Learn Functions.

Michael Y Li¹, Fred Callaway², William D Thompson³

¹Department of Computer Science, Stanford University.

Cognitive Science

|April 13, 2023

Summary

This summary is machine-generated.

Humans learn complex functions by adjusting expectations based on experience. This study models learning-to-learn functions using hierarchical Bayesian inference on Gaussian process hyperparameters.

Keywords:

Bayesian nonparametrics Function learning Gaussian process Hierarchical Bayesian models Learning-to-learn

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Related Experiment Videos

Last Updated: Aug 2, 2025

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

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Published on: June 20, 2020

A Computerized Functional Skills Assessment and Training Program Targeting Technology Based Everyday Functional Skills

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Published on: February 13, 2020

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Cognitive Science
Machine Learning
Statistics

Background:

Humans excel at learning complex functional relationships from limited data.
Prior expectations about function forms guide human learning.
Gaussian processes are a statistical framework for modeling human function learning.

Purpose of the Study:

To investigate how humans adjust their expectations about function forms through experience.
To model the process of learning to learn functions.
To apply hierarchical Bayesian inference to Gaussian process hyperparameters.

Main Methods:

Three experiments were conducted to observe human function learning.
Human function learning was modeled using Gaussian processes.
Hierarchical Bayesian inference was used to model learning-to-learn functions.

Main Results:

People adjust their expectations about function forms based on encountered data.
Experience leads to learning about the likely forms of functions.
The study provides a computational model for learning-to-learn functions.

Conclusions:

Human function learning involves adapting prior expectations through experience.
Hierarchical Bayesian inference offers a framework for understanding learning-to-learn processes.
This research bridges cognitive science and machine learning through advanced statistical modeling.