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Related Concept Videos

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.
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Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Related Experiment Video

Updated: Apr 14, 2026

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

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Human and machine learning in non-Markovian decision making.

Aaron Michael Clarke1, Johannes Friedrich2, Elisa M Tartaglia3

  • 1Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Plos One
|April 22, 2015
PubMed
Summary

This study introduces a spiking neural network model capable of handling non-Markovian reinforcement learning (RL) conditions, outperforming previous models. The model accurately describes human learning in complex feedback environments.

Related Experiment Videos

Last Updated: Apr 14, 2026

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Published on: August 2, 2018

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Cognitive science

Background:

  • Reinforcement learning (RL) is crucial for decision-making.
  • Existing RL models primarily focus on Markovian processes.
  • Non-Markovian decision-making, where outcomes depend on more than the current state, is less understood.

Purpose of the Study:

  • To develop and evaluate a spiking neural network model for non-Markovian reinforcement learning.
  • To compare the model's performance against human learning and a Bayes optimal reference.

Main Methods:

  • Developed a novel model based on spiking neurons.
  • Implemented policy gradient descent for learning.
  • Compared model performance with human behavioral data and theoretical upper bounds.

Main Results:

  • The spiking neuron model effectively handles non-Markovian feedback conditions.
  • Model performance closely matches human learning patterns across tested scenarios.
  • The model provides a robust description of human decision-making under uncertainty.

Conclusions:

  • Spiking neural networks offer a viable framework for modeling complex, non-Markovian learning.
  • This model advances our understanding of biological and artificial learning systems.
  • The findings suggest new directions for artificial intelligence and cognitive modeling.