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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Quantifying uncertainty in drift diffusion models of decision making under temporal dependence and parameter

Gabriel Riegner1, Armin Schwartzman1,2, Pamela Reinagel3

  • 1Halicioğlu Data Science Institute, University of California San Diego.

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Summary

This study introduces a new method for analyzing decision-making, accounting for how behavior changes over time. It provides more accurate uncertainty estimates for drift diffusion model parameters, improving our understanding of dynamic decision processes.

Keywords:
2AFCDDMperceptual decisionssequential decision makingtime-series analysis

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Behavioral Economics

Background:

  • Decision-making behavior is dynamic, showing temporal correlations and nonstationarity.
  • Current drift diffusion model (DDM) fitting methods often lack uncertainty quantification or make restrictive assumptions of independence and parameter constancy.
  • These limitations can lead to underestimation of uncertainty in parameter estimates.

Purpose of the Study:

  • To develop a computationally efficient method for estimating analytic uncertainties in DDM parameters.
  • To create a method robust to temporal dependence and unmodeled parameter variability.
  • To explicitly model nonstationary variability using covariates.

Main Methods:

  • Proposed a novel method for analytic uncertainty estimation in DDM parameters.
  • Ensured robustness against temporal dependencies and unmodeled parameter variability.
  • Incorporated covariates to model nonstationary variability.

Main Results:

  • Successfully applied the method to analyze rat decision-making in a two-alternative forced-choice (2AFC) visual task.
  • Revealed dynamic decision-making states across multiple timescales.
  • Demonstrated the method's ability to provide robust uncertainty quantification.

Conclusions:

  • The developed method offers accurate uncertainty quantification for DDM parameters, even with temporal dependencies.
  • It provides a more comprehensive understanding of dynamic decision-making processes.
  • A Python implementation is available for broader application.