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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision Making01:20

Decision Making

212
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Related Experiment Video

Updated: Sep 3, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

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Flexible and efficient simulation-based inference for models of decision-making.

Jan Boelts1,2, Jan-Matthis Lueckmann1, Richard Gao1

  • 1Machine Learning in Science, Excellence Cluster Machine Learning, University of Tübingen, Tübingen, Germany.

Elife
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

We introduce mixed neural likelihood estimation (MNLE), a more efficient simulation-based inference method for cognitive neuroscience models. MNLE significantly reduces the number of simulations needed for parameter inference in decision-making models compared to previous approaches.

Keywords:
Bayesian inferencecomputational modelingdecision-makingmachine learningneurosciencenonesimulation-based inference

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Area of Science:

  • Cognitive Neuroscience
  • Computational Modeling
  • Bayesian Inference

Background:

  • Parameter inference for computational models is crucial in cognitive neuroscience.
  • Traditional Bayesian methods struggle with models lacking efficient likelihood computation.
  • Simulation-based inference (SBI) bypasses likelihood evaluation but can be simulation-intensive.

Purpose of the Study:

  • To develop a more simulation-efficient method for applying SBI to cognitive models.
  • To enable accurate Bayesian parameter inference for complex decision-making models.
  • To facilitate faster model design and scientific discovery in cognitive neuroscience.

Main Methods:

  • Introduced Mixed Neural Likelihood Estimation (MNLE), a novel SBI approach.
  • MNLE trains neural density estimators on model simulations to emulate the simulator.
  • The method captures both continuous (e.g., reaction times) and discrete (choices) data.

Main Results:

  • MNLE achieves comparable likelihood accuracy to Likelihood Approximation Networks (LANs) using six orders of magnitude fewer simulations.
  • MNLE demonstrates significantly higher accuracy than LANs when trained with equivalent simulation budgets.
  • Validated MNLE on two variants of the drift-diffusion model.

Conclusions:

  • MNLE offers a substantially more simulation-efficient alternative for SBI in cognitive neuroscience.
  • This method allows for Bayesian parameter inference using standard approximate methods like Markov Chain Monte Carlo sampling.
  • MNLE empowers researchers to efficiently analyze custom decision-making models, accelerating scientific exploration.