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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Predicting binary choices from probability phrase meanings.

Thomas S Wallsten1, Yoonhee Jang

  • 1Department of Psychology, University of Maryland, College Park, Maryland 20742, USA. twallsten@psyc.umd.edu

Psychonomic Bulletin & Review
|September 17, 2008
PubMed
Summary
This summary is machine-generated.

This study shows that scales of probability phrase meanings can predict how people choose between two events. This supports a memory sampling model for understanding relative likelihoods.

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Probability Theory

Background:

  • Understanding how individuals judge relative likelihoods is crucial for decision-making.
  • Probability phrases (e.g., "likely," "unlikely") are commonly used but their subjective meanings can vary.
  • Existing models often struggle to integrate the semantic understanding of probability with choice behavior.

Purpose of the Study:

  • To investigate if derived scales of probability phrase meanings can predict binary choices.
  • To test a memory sampling model for judging relative likelihoods.
  • To determine if probability phrase meanings are measured meaningfully.

Main Methods:

  • Developed scales representing the meanings of probability phrases.
  • Utilized a choice model incorporating these scaled meanings.
  • Assessed the model's ability to predict binary choices based on memory sampling.
  • Applied sequential sampling models to refine predictions.

Main Results:

  • The choice model successfully predicted outcomes for 34 out of 41 participants.
  • Predictions showed a slight, consistent underestimation bias.
  • Sequential sampling models significantly improved the model's predictive fit.
  • Scaled probability phrase meanings served as effective proxies for memory confidence.

Conclusions:

  • Derived scales of probability phrase meanings are valuable for predicting binary choices.
  • The findings support a memory sampling account of relative likelihood judgments.
  • The study has implications for both theoretical models of decision-making and applied contexts involving probabilistic language.