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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Decision Making01:20

Decision Making

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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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Related Experiment Video

Updated: Jun 24, 2026

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Jamal Esmaily1,2, Rani Moran3,4,5, Yasser Roudi6,7

  • 1Department of General Psychology and Education and Graduate School of Systemic Neurosciences, Ludwig Maximilians University Munich, 80539, Munich, Germany.

Neural Computation
|June 22, 2026
PubMed
Summary

This study introduces a reinforcement learning algorithm for perceptual decisions, enabling animals to learn and optimize decision boundaries. The model explains how animals balance evidence gathering with the cost of continued information sampling.

Related Experiment Videos

Last Updated: Jun 24, 2026

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

Published on: August 2, 2018

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Biology

Background:

  • The boundary model explains decision-making but not how boundaries are learned.
  • Optimizing decision boundaries is crucial for efficient behavior under uncertainty.

Purpose of the Study:

  • To propose a model-free reinforcement learning algorithm for perceptual decisions under uncertainty.
  • To investigate how animals learn and optimize decision boundaries during sequential information sampling.

Main Methods:

  • Developed a reinforcement learning algorithm integrating sequential sampling with an implicit decision boundary.
  • Simulated the model to reproduce key features of perceptual decision-making.

Main Results:

  • The model successfully reproduced canonical features of perceptual decision-making, including accuracy and reaction time dependencies.
  • Demonstrated the model's ability to modulate the speed-accuracy trade-off based on payoff regimes.
  • Showcased the model's capacity to learn optimal strategies for committing to decisions or continuing information sampling.

Conclusions:

  • The proposed framework unifies learning and decision-making, offering insights into behavioral flexibility.
  • This model provides a novel perspective on the mechanisms underlying context-dependent behavioral adjustments.