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A hierarchical bayesian model of human decision-making on an optimal stopping problem
1Department of Cognitive Sciences, University of California, Irvine.
Cognitive Science
|June 28, 2011
Summary
People presented with numbers often use threshold strategies to select the maximum, choosing the first number exceeding a position-specific threshold. This study models these decision-making processes using hierarchical Bayesian methods.
Area of Science:
- Cognitive Psychology
- Decision Science
- Computational Neuroscience
Background:
- Optimal stopping problems are fundamental in decision-making research.
- Human performance in these tasks often deviates from normative models.
- Understanding human heuristics is crucial for cognitive modeling.
Purpose of the Study:
- To investigate human decision-making strategies in an optimal stopping task.
- To empirically test threshold-based models of human performance.
- To develop and apply a hierarchical Bayesian framework for cognitive modeling.
Main Methods:
- Participants engaged in an optimal stopping task with sequentially presented numbers.
- Empirical data were analyzed using a hierarchical generative model.
- Bayesian inference was employed to estimate model parameters.
Main Results:
- Evidence suggests participants utilize threshold-based decision strategies.
- A lack of learning and significant individual differences in performance were observed.
- The hierarchical Bayesian model successfully inferred parameters of these threshold strategies.
Conclusions:
- Threshold-based models provide a viable account of human behavior in optimal stopping problems.
- Hierarchical Bayesian modeling offers a powerful framework for understanding cognitive processes.
- Further research can extend this framework to explain individual differences in decision-making.
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