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Comparison of basic assumptions embedded in learning models for experience-based decision making.

Eldad Yechiam1, Jerome R Busemeyer

  • 1Indiana University, Bloomington, Indiana, USA. yeldad@tx.technion.ac.il

Psychonomic Bulletin & Review
|October 21, 2005
PubMed
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This study tested learning models for predicting behavior in decisions from experience. Models incorporating experience decay best predicted choices, though rule ranking varied by evaluation method.

Area of Science:

  • Cognitive Psychology
  • Behavioral Economics
  • Computational Neuroscience

Background:

  • Decisions from experience involve learning unknown probabilities and payoffs through repeated choices and feedback.
  • Existing learning models make assumptions about how experience is updated and how it influences choices.

Purpose of the Study:

  • To examine basic assumptions in learning models for predicting behavior in decisions from experience.
  • To compare four classes of models resulting from combinations of experience updating and choice rules.
  • To evaluate model performance using two distinct prediction methods.

Main Methods:

  • Systematic comparison of four classes of learning models.
  • Two evaluation methods: one-step-ahead prediction and a priori prediction.

Related Experiment Videos

  • Parameter estimation from individual participant data and separate experiments.

    • Main Results:

    • Models incorporating the decay of previous experience demonstrated an advantage in predicting behavior.
    • The relative performance of different choice rules was dependent on the evaluation method employed.
    • Model comparison revealed distinct performance characteristics based on the prediction strategy.

    Conclusions:

    • Learning models that account for the decay of past experience are crucial for accurately predicting decisions from experience.
    • The choice of evaluation method significantly influences the assessment of different learning and choice rules.
    • Future research should consider the interplay between experience updating mechanisms and choice strategies in decision-making models.