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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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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.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Cognitivism01:17

Cognitivism

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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
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Reasoning01:30

Reasoning

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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,...
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Inductive Reasoning00:59

Inductive Reasoning

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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...
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Deductive Reasoning01:16

Deductive Reasoning

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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...
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Reason and Intuition01:37

Reason and Intuition

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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...
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Related Experiment Video

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High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
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Cognitive AI beyond prediction: toward reasoning and discovery.

Jianliang Gong1, Han Zhou2, Shicheng Yu2

  • 1Key Lab of Fluorine and Silicon for Energy Materials and Chemistry for the Ministry of Education, Jiangxi Normal University, Nanchang, 330022, China. ywchen@ncu.edu.cn.

Materials Horizons
|February 18, 2026
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Summary

Artificial intelligence (AI) is advancing in materials discovery beyond property prediction to scientific reasoning. Cognitively enabled AI can act as research colleagues, enhancing battery science discoveries.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Battery Technology

Background:

  • Modern artificial intelligence (AI) is crucial for materials investigation, particularly in battery research, accelerating the analysis of electrolytes, interfaces, and structural frameworks.
  • Current AI excels at property prediction but struggles with fundamental scientific objectives like understanding, explanation, and adaptive reasoning.

Purpose of the Study:

  • To propose that AI in materials discovery is evolving towards scientific reasoning capabilities.
  • To outline a modular cognitive architecture for AI systems to address complex battery research challenges.
  • To highlight the potential of AI as a collaborative tool for scientific discovery.

Main Methods:

  • Leveraging recent advancements in neuro-symbolic reasoning, hypothesis generation, and autonomous systems.
  • Developing a modular cognitive architecture integrating representation construction, mechanism inference, hypothesis formulation, experimentation, and belief revision.
  • Applying these AI capabilities to address specific battery research issues like interfacial instability and electrolyte design under uncertainty.

Main Results:

  • AI is progressing from property prediction to encompass scientific reasoning, including hypothesis generation and experimental design.
  • A cognitive architecture can integrate diverse AI capabilities to tackle complex, uncertain problems in battery research.
  • Cognitively enabled AI systems show promise as reasoning partners for scientists.

Conclusions:

  • AI's evolution towards scientific reasoning is essential for deeper understanding and innovation in materials science.
  • Integrating cognitive architectures into AI can unlock new approaches to challenging problems in battery development.
  • Future AI systems can serve as invaluable collaborators, enhancing scientific discovery while preserving research integrity.