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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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A model of plausibility.

Louise Connell1, Mark T Keane

  • 1Cognition & Communication Research Centre, Division of Psychology, Northumbria UniversitySchool of Computer Science and Informatics, University College Dublin.

Cognitive Science
|June 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces the Plausibility Analysis Model (PAM), a new cognitive model for human plausibility judgment. PAM effectively simulates how commonsense knowledge influences our assessment of scenario likelihood.

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Area of Science:

  • Cognitive Science
  • Psychology
  • Artificial Intelligence

Background:

  • Plausibility is crucial for cognitive functions like comprehension and problem-solving.
  • Existing research often treats plausibility as a variable, not a phenomenon to be studied intrinsically.
  • A gap exists in understanding the cognitive mechanisms underlying plausibility judgments.

Purpose of the Study:

  • To introduce and describe the Plausibility Analysis Model (PAM).
  • To model human plausibility judgment using commonsense knowledge.
  • To provide a computational framework for understanding plausibility.

Main Methods:

  • Developed the Plausibility Analysis Model (PAM) based on concept-coherence and commonsense knowledge.
  • Simulated empirical findings on plausibility judgments using the PAM.
  • Conducted a sensitivity analysis to assess the model's robustness.

Main Results:

  • The PAM accurately models human plausibility judgments by assessing scenario coherence with prior knowledge.
  • The model demonstrates that high plausibility correlates with strong corroboration, low explanatory complexity, and minimal conjecture.
  • Simulation results showed a close correspondence between PAM's outputs and human judgments.

Conclusions:

  • The Plausibility Analysis Model (PAM) offers a novel computational approach to understanding human plausibility.
  • PAM highlights the role of commonsense knowledge and concept-coherence in judging scenario likelihood.
  • The model's robustness and accuracy suggest its potential for further research in cognitive science and AI.