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Reason and Intuition01:37

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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

Updated: Nov 30, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning.

Zachary Wojtowicz1, Simon DeDeo2

  • 1Department of Social and Decision Sciences, Carnegie Mellon University, Pittsburgh, PA, USA.

Trends in Cognitive Sciences
|November 17, 2020
PubMed
Summary
This summary is machine-generated.

Cognitive science reveals diverse explanatory values. A new Bayesian framework unifies these values, explaining why people prefer certain explanations and offering insights into phenomena like conspiracy theories.

Keywords:
Bayesian cognitionexplanationexplanatory valuesrational analysissimplicityvice epistemology

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Area of Science:

  • Cognitive Science
  • Philosophy of Science
  • Psychology
  • Statistics

Background:

  • Explanations are judged on multiple dimensions, or explanatory values.
  • Understanding these values is key to cognitive science and scientific reasoning.

Purpose of the Study:

  • To propose a unified Bayesian account of explanatory values.
  • To clarify the function and integration of these values in explanation-making.
  • To provide a mathematical framework for understanding explanatory virtues and vices.

Main Methods:

  • Developed a Bayesian framework to model explanatory values.
  • Integrated core values from psychology, statistics, and philosophy of science.
  • Operationalized explanatory virtues within the mathematical model.

Main Results:

  • The Bayesian account clarifies the function and integration of explanatory values.
  • A taxonomy of explanatory values emerged from a common mathematical framework.
  • The framework explains individual explanation adoption and phenomena like conspiracy theories.

Conclusions:

  • The proposed Bayesian framework offers a unified understanding of explanatory values.
  • This model provides insight into both scientific reasoning and cognitive biases.
  • It reinterprets explanatory vices driving phenomena like delusions and extremist ideologies.