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Leaping to Conclusions: Why Premise Relevance Affects Argument Strength.

Keith J Ransom1, Amy Perfors2, Daniel J Navarro2

  • 1School of Psychology, University of Adelaide. keith.ransom@adelaide.edu.au.

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|October 17, 2015
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People can reason beyond raw data by considering how it was sampled. This study shows that understanding sampling assumptions influences category-based reasoning and argument strength, impacting conclusions drawn.

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Area of Science:

  • Cognitive Science
  • Psychology
  • Artificial Intelligence

Background:

  • Everyday reasoning often requires more evidence than raw data provides.
  • People can infer additional information by reasoning about data sampling processes.
  • Premise non-monotonicity, where adding premises weakens an argument, is a key phenomenon in category-based induction.

Purpose of the Study:

  • To investigate how reasoning about data sampling influences everyday reasoning.
  • To explore the role of premise sampling assumptions in category-based induction.
  • To complement existing relevance theories with a Bayesian model incorporating sampling.

Main Methods:

  • Developed a Bayesian model of category-based induction.
  • Incorporated premise sampling assumptions and category similarity into the model.
  • Conducted an experiment manipulating participants' sampling assumptions.

Main Results:

  • The Bayesian model successfully predicted how sampling assumptions affect reasoning.
  • Sensitivity to premise relationships can be violated by weak sampling assumptions.
  • Premise monotonicity is restored when weak sampling assumptions are induced.

Conclusions:

  • Reasoning about data sampling is a crucial component of everyday induction.
  • A Bayesian approach integrating sampling assumptions offers a powerful framework for understanding category-based reasoning.
  • Experimental results support the model's predictions regarding sampling effects on argument strength.