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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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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

Updated: May 3, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Towards Data-And Knowledge-Driven AI: A Survey on Neuro-Symbolic Computing.

Wenguan Wang, Yi Yang, Fei Wu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 17, 2024
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    Summary

    Neural-symbolic computing (NeSy) integrates symbolic reasoning with neural network learning for advanced AI. This survey overviews NeSy

    Area of Science:

    • Artificial Intelligence
    • Cognitive Science

    Background:

    • Neural-symbolic computing (NeSy) aims to merge symbolic AI's interpretability with neural networks' learning capabilities.
    • This integration is crucial for developing the next generation of artificial intelligence.
    • NeSy research addresses the need for AI systems that can both reason and learn effectively.

    Purpose of the Study:

    • To provide a comprehensive overview of recent advancements in neural-symbolic computing.
    • To systematically categorize and analyze key NeSy approaches and their characteristics.
    • To identify future research directions and open problems in the field.

    Main Methods:

    • Historical review of NeSy research, including foundational work.
    • Categorization of recent NeSy approaches based on integration, knowledge representation, embedding, and functionality.

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  • Benchmarking of selected NeSy methods on representative application tasks.

    • Main Results:

    • A structured overview of NeSy's evolution, key concepts, and driving factors.
    • Classification of modern NeSy techniques and their successful applications.
    • Empirical evaluation of several NeSy methods across different tasks.

    Conclusions:

    • NeSy holds significant potential for advancing AI by combining reasoning and learning.
    • The survey highlights successful applications and provides a benchmark for NeSy methods.
    • Identifying open problems and future directions aims to accelerate progress in data- and knowledge-driven AI.