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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.
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Cognitive Learning

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Related Experiment Video

Updated: Dec 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Network architectures supporting learnability.

Perry Zurn¹, Danielle S Bassett^2,3,4,5,6,7

¹Department of Philosophy, American University, Washington, DC 20016, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences

|February 25, 2020

Summary

This summary is machine-generated.

Human learning relies on both knowledge network and brain architecture. Hierarchically modular networks at conceptual and neural levels optimize learning and development.

Keywords:

collective dynamics constraints curiosity knowledge networks network neuroscience

Related Experiment Videos

Last Updated: Dec 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Cognitive Science
Neuroscience
Statistical Physics

Background:

Human knowledge acquisition involves complex relational networks.
Learning capacity depends on both knowledge network structure and brain architecture.
This study integrates epistemic (conceptual) and computational (neural) network levels.

Purpose of the Study:

To explore network constraints on learning relational knowledge.
To model these constraints using statistical physics, thermodynamics, and information theory.
To unify conceptual and neural network architectures in biological systems.

Main Methods:

Reviewing emerging research on network constraints in learning.
Applying principles from statistical physics for explanatory models.
Distinguishing between the modeller, model, and modelled for analysis.
Examining similarities between conceptual and neural network constraints.

Main Results:

Constraints on learning relational knowledge and neural system development converge on hierarchically modular networks.
Identified parallels between conceptual and neural network architectures.
Proposed epistemological norms for analyzing biological networks.

Conclusions:

Hierarchically modular networks are crucial for both knowledge acquisition and neural development.
Further philosophical discussion is needed on explanation and mechanism in knowledge acquisition.
The study contributes to a unified approach to hierarchies in biological networks.