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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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Published on: February 26, 2014

Grounding quantum probability in psychological mechanism.

Bradley C Love1

  • 1University College London, Cognitive, Perceptual and Brain Sciences, London WC1H 0AP, United Kingdom. b.love@ucl.ac.uk

The Behavioral and Brain Sciences
|May 16, 2013
PubMed
Summary

Quantum probability (QP) theory better models human judgment than classical probability (CP) theory. Grounding QP in mechanisms using process-level data can overcome theoretical under-specification for cognitive science research.

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Last Updated: May 11, 2026

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Published on: February 26, 2014

Area of Science:

  • Cognitive Science
  • Quantum Cognition
  • Decision Making

Background:

  • Classical probability (CP) theory has limitations in explaining human judgment.
  • Quantum probability (QP) theory offers a potential alternative framework.
  • Theories solely at the computational level risk being underconstrained.

Purpose of the Study:

  • To evaluate the efficacy of quantum probability (QP) theory in modeling human judgment.
  • To address the under-specification issue in computational-level theories.
  • To propose grounding quantum probability accounts in underlying mechanisms.

Main Methods:

  • Review and analysis of existing literature on quantum probability and human judgment.
  • Examination of Pothos & Busemeyer's arguments for QP theory.
  • Discussion of the necessity for process-level data to support theoretical claims.

Main Results:

  • Quantum probability (QP) theory provides a more compelling account of human judgment compared to classical probability (CP) theory.
  • Theories of cognition, including QP, require mechanistic grounding to avoid under-specification.
  • Leveraging process-level data is crucial for validating and refining cognitive models.

Conclusions:

  • Quantum probability (QP) theory shows promise for explaining human judgment.
  • Mechanistic explanations are essential for robust cognitive theories.
  • Further research should focus on integrating process-level data with QP models.