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Related Experiment Video

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A linear threshold model for optimal stopping behavior.

Christiane Baumann1, Henrik Singmann2, Samuel J Gershman3

  • 1Department of Psychology, University of Zürich, 8050 Zurich, Switzerland; c.baumann@psychologie.uzh.ch.

Proceedings of the National Academy of Sciences of the United States of America
|May 29, 2020
PubMed
Summary
This summary is machine-generated.

Humans often deviate from optimal strategies in sequential decision-making tasks. A new linear threshold model better explains human stopping decisions and search behavior in these scenarios.

Keywords:
adaptive behaviorcognitive modelingoptimal stoppingsequential decision making

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Area of Science:

  • Cognitive Psychology
  • Decision Science
  • Behavioral Economics

Background:

  • Real-life decisions often involve sequential search through options distributed in space and time.
  • Optimal strategies require selecting the first option exceeding a position-dependent threshold.
  • Human decision-making strategies frequently diverge from this optimal approach, with reasons unclear.

Purpose of the Study:

  • To present and validate a model of human stopping decisions in sequential tasks.
  • To investigate the underlying mechanisms of human divergence from optimal search strategies.
  • To assess the generalizability of the proposed model to real-world problems.

Main Methods:

  • Development of a linear threshold heuristic model for sequential decision-making.
  • Empirical testing across three studies involving human participants.
  • Comparison of the linear threshold model against existing decision-making models.

Main Results:

  • The linear threshold model provided a superior account of sequential decision-making compared to existing models.
  • The model accurately predicted human search behavior across diverse environmental conditions.
  • Validation of the model's applicability to a real-world decision-making problem.

Conclusions:

  • The linear threshold model offers a robust explanation for human stopping decisions in sequential tasks.
  • This model advances the understanding of human behavior in situations requiring sequential search and choice.
  • The findings represent a significant step toward comprehending complex human sequential decision-making processes.