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Natural and Artificial Concepts

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...

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Related Experiment Video

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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

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Published on: February 8, 2019

Structure learning enhances concept formation in synthetic Active Inference agents.

Victorita Neacsu¹, M Berk Mirza^2,3, Rick A Adams^4,5

¹Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.

|November 14, 2022

Summary

This study introduces a deep hierarchical Active Inference model to explain how humans learn concepts from their environment. Simulations show this model captures how agents form flexible, generalizable concepts through spatial foraging and Bayesian model reduction.

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Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Humans excel at environmental learning, forming flexible concepts from context-specific affordances and relations.
Understanding concept formation is key to artificial intelligence and cognitive modeling.

Purpose of the Study:

To present a deep hierarchical Active Inference model for goal-directed behavior and concept learning.
To elucidate mechanisms underlying concept learning in spatial foraging tasks using simulations.

Main Methods:

Developed a deep hierarchical Active Inference model.
Employed simulations of a spatial foraging task.
Investigated belief update schemes via maximizing model evidence.

Main Results:

Model representations reflect environmental structure, enhanced by Bayesian model reduction.
Synthetic agents learned associations and formed concepts via inferential, parametric, and structure learning.
Learned representations captured environmental symmetries.

Conclusions:

Active Inference provides a framework for understanding concept formation and environmental abstraction.
Bayesian model reduction plays a crucial role in refining learned representations.
The model demonstrates how diverse beliefs and structures emerge from learning processes.