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Related Experiment Video

Updated: Jan 17, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Misspecified models create the appearance of adaptive control during value-based choice.

Harrison Ritz1,2,3, Romy Frömer4,5,6,7, Amitai Shenhav8,9

  • 1Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA. hr0283@princeton.edu.

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Summary

This study challenges a new theory of motivated control in decision-making. Alternative explanations, including task confounds, better account for observed adaptive control in evidence accumulation.

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Area of Science:

  • Cognitive Science
  • Decision Science
  • Computational Neuroscience

Background:

  • Metacognitive factors influence decision-making by affecting evidence accumulation and response commitment.
  • A recent theory proposed that option values modulate decision formation and response vigor, operationalized as lowered decision thresholds in drift-diffusion models.

Purpose of the Study:

  • To reanalyze data supporting the novel theory of motivated control.
  • To provide alternative explanations for observed adaptive control phenomena in decision-making.
  • To critically evaluate the computational operationalization of controlled threshold adjustments.

Main Methods:

  • Reanalysis of existing experimental data.
  • Application of drift-diffusion modeling.
  • Investigation of alternative evidence accumulation models.
  • Examination of task confounds and time-dependent effects.

Main Results:

  • Evidence for controlled threshold adjustments was explained by alternative factors.
  • Task confounds, time-dependent threshold collapses, and stimulus-driven dynamics were identified as alternative explanations.
  • The specific evidence for the novel theory of motivated control was challenged.

Conclusions:

  • The findings question the necessity of invoking controlled threshold adjustments to explain adaptive decision control.
  • Alternative computational models and careful consideration of task confounds are crucial for understanding decision-making control.
  • This work highlights potential pitfalls in applying computational approaches to study control in decision processes.