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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...
Counterfactual Thinking01:19

Counterfactual Thinking

Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in human cognition.Types of...
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...
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...
The Influence of Cognition on Affect01:29

The Influence of Cognition on Affect

Cognition plays a pivotal role in shaping emotional experiences, as demonstrated by Schachter and Singer’s two-factor theory of emotion. According to this model, emotion arises from a combination of physiological arousal and cognitive interpretation. The body’s physiological response to stimuli is ambiguous and only gains emotional significance through cognitive labeling. For instance, an increased heart rate and adrenaline surge while standing near an attractive person may be interpreted as...

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Related Experiment Video

Updated: Jun 27, 2026

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

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Counterfactual Reasoning as the Missing Link Between Statistical AI and Human Cognition: A Cognitive Science

Piercesare Grimaldi1

  • 1Department of Life Sciences, Health and Health Professions, Link Campus University, 00165 Rome, Italy.

Behavioral Sciences (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

Large language models (LLMs) struggle with counterfactual reasoning due to limitations in causal understanding. Studying the brain

Keywords:
Default Mode NetworkNeuroAIPearl’s Ladder of Causationcausal inferencecausal world modelscognitive neurosciencecounterfactual reasoninglarge language models

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Artificial Intelligence

Background:

  • Text-only large language models (LLMs) excel at linguistic tasks but falter in counterfactual reasoning.
  • Formal causal competence involves distinct, irreducible levels: association, intervention, and counterfactual reasoning.

Purpose of the Study:

  • To synthesize formal causal inference, cognitive science, and neuroscience for AI.
  • To explain LLM limitations in counterfactual reasoning through the lens of causal models.
  • To propose brain-inspired AI architectures for enhanced counterfactual capabilities.

Main Methods:

  • Cross-level synthesis of formal causal inference, developmental cognitive science, and cognitive neuroscience.
  • Analysis of LLM failures in intervention-sensitive and individual-level counterfactual reasoning.
  • Review of neuroscientific evidence for counterfactual cognition processes.

Main Results:

  • LLMs lack structured causal models necessary for counterfactual invariance.
  • Neuroscientific evidence highlights episodic construction, fictive evaluation, and scenario simulation in counterfactual cognition.
  • The Default Mode Network and hippocampal systems are implicated in these processes.

Conclusions:

  • The brain's approach to counterfactual reasoning offers a more promising AI development path than explicit rule implementation.
  • Neuroscience provides valuable constraints and evaluation criteria for AI research.
  • Future AI should emulate the brain's approximate counterfactual reasoning mechanisms.