Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reasoning

Reasoning

Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...

Deductive Reasoning

Deductive Reasoning

Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Reason and Intuition

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...

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...

Metacognition

Metacognition

Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A formal model for assessing the learning outcomes of academic programs.

Evaluation and program planning·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 16, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

A Validated Ontology for Metareasoning in Intelligent Systems.

Manuel F Caro¹, Michael T Cox², Raúl E Toscano-Miranda¹

¹Education, Technology & Language (EduTLan Research Group), Department of Educational Informatics, University of Córdoba, Carrera 6 No. 77-305, Montería 230002, Córdoba, Colombia.

Journal of Intelligence

|December 22, 2022

Summary

This study introduces IM-Onto, an ontology for metareasoning in intelligent systems, to solve the heterogeneity problem. IM-Onto enhances understanding and integration of diverse metareasoning models.

Keywords:

heterogeneity problem intelligent systems metareasoning ontology metareasoning problem ontology validation

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Related Experiment Videos

Last Updated: Aug 16, 2025

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

Area of Science:

Artificial Intelligence
Knowledge Representation
Ontology Engineering

Background:

Metareasoning models in intelligent systems often suffer from heterogeneity due to diverse contexts and terminology.
Lack of a common understanding hinders the integration of different metareasoning approaches.
Existing models present challenges in sharing knowledge and ensuring consistency.

Purpose of the Study:

To propose an ontology-driven knowledge representation for metareasoning in intelligent systems.
To address the heterogeneity problem by establishing a common understanding of metareasoning concepts.
To facilitate the integration of diverse metareasoning models.

Main Methods:

Development of an ontology named IM-Onto for metareasoning.
Application of a rigorous research methodology to ensure ontology integrity and acceptance.
Utilizing visual representations for sharing common understanding of terms and concepts.

Main Results:

The proposed IM-Onto ontology provides a visual means for a shared understanding of metareasoning.
A rigorous method ensured the ontology's integrity and acceptance by researchers and practitioners.
High accuracy rates suggest the ontology's usefulness for integrating various metareasoning problems.

Conclusions:

IM-Onto offers a robust solution to the heterogeneity problem in metareasoning for intelligent systems.
The ontology facilitates a common understanding and integration of diverse metareasoning models.
The developed knowledge representation is valuable for advancing the field of intelligent systems.