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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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...
Cognitivism01:17

Cognitivism

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 information is...
Reason and Intuition01:37

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 brain can only use...
Revisionist Views of Adolescent and Adult Cognition01:24

Revisionist Views of Adolescent and Adult Cognition

A revisionist approach to Jean Piaget's theory of cognitive development has brought new insights that challenge and reinterpret his established ideas. Piaget proposed that the formal operational stage, emerging in adolescence, represents the culmination of cognitive maturity. During this stage, individuals are said to develop abstract thinking, engage in systematic problem-solving, and show a form of egocentrism, believing others are as preoccupied with their behavior as they are themselves.
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

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Related Experiment Video

Updated: Jul 17, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Reclaiming AI as a Theoretical Tool for Cognitive Science.

Iris van Rooij1,2,3, Olivia Guest1,2, Federico Adolfi4,5

  • 1Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

Human cognition as computation is a useful idea, but current Artificial Intelligence (AI) overestimates its practical feasibility. Creating human-level AI is computationally intractable, leading to flawed self-understanding.

Keywords:
Artificial Intelligence (AI)Cognitive scienceComputational complexityEngineeringExplanationTheory

Related Experiment Videos

Last Updated: Jul 17, 2026

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Computational Theory

Background:

  • The concept of human cognition as computation was foundational to cognitive science and Artificial Intelligence (AI).
  • AI historically served as a provider of computational tools for cognitive science theory-building.
  • Contemporary AI shifts focus from theoretical possibility to practical realization of human-level cognition.

Purpose of the Study:

  • To formally prove the computational intractability of creating human-level cognitive systems.
  • To critique the current trajectory of AI and its impact on cognitive science.
  • To propose a return to AI as a theoretical tool for understanding cognition.

Main Methods:

  • Formal proof of computational intractability.
  • Analysis of the theoretical implications of AI's practical pursuits.
  • Conceptual critique of AI's current role in cognitive science.

Main Results:

  • Achieving human-level cognition in AI systems is intrinsically computationally intractable.
  • Current AI systems are decoys that distort our understanding of human cognition.
  • AI practice is currently deteriorating, not advancing, cognitive science.

Conclusions:

  • The pursuit of practical human-level AI is theoretically flawed and detrimental to cognitive science.
  • Reorienting AI as a theoretical tool is crucial for advancing cognitive understanding.
  • Avoiding past conceptual errors is necessary for a healthier AI-cognitive science relationship.