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Related Experiment Video

Updated: Apr 18, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Decision from Models: Generalizing Probability Information to Novel Tasks.

Hang Zhang1, Jacienta T Paily2, Laurence T Maloney1

  • 1Department of Psychology and Center for Neural Science, New York University.

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|January 27, 2015
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Summary

Participants learned to generalize decisions under risk in a virtual minefield task. Most participants accurately modeled exponential risk but overestimated the hazard rate.

Keywords:
constant hazard ratedecision from experiencedecision under riskexponential survival functiongeneralization

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Area of Science:

  • Cognitive psychology
  • Decision science
  • Risk assessment

Background:

  • Decisions under risk often involve generalizing past experiences to new situations.
  • Understanding how individuals learn and apply risk models is crucial for predicting behavior.

Purpose of the Study:

  • To investigate a novel decision-making paradigm requiring generalization of experience to new tasks.
  • To model how participants discount reward based on path length and risk (hazard rate).

Main Methods:

  • Participants navigated a virtual minefield, trading path distance for reward under a constant probability of failure (hazard).
  • Training involved feedback on 160 trials; testing used 600 no-feedback trials with novel task parameters.
  • Choices were compared against nine models, including the correct exponential model.

Main Results:

  • A majority of participants' choices aligned with the correct exponential model of decision-making.
  • Participants consistently overestimated the hazard rate compared to the actual trained rate.
  • The study highlights the importance of generalizing experience in decision-from-models paradigms.

Conclusions:

  • Individuals can learn and generalize exponential risk models, a key aspect of decision-making.
  • Overestimation of hazard rates is a common bias in such tasks.
  • This paradigm offers insights into real-world decisions in environments with consistent stochastic properties.