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Related Experiment Video

Updated: May 7, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

A psychological approach to learning causal networks.

Manaf Zargoush¹, Farrokh Alemi, Vinzenzo Esposito Vinzi

¹Operations Management and Decision Sciences, ESSEC Business School, Paris, France, zargoush@gmail.com.

Health Care Management Science

|September 20, 2013

Summary

A cognitive heuristic, prioritizing causal over diagnostic relationships, improved Bayesian network learning. This enhanced algorithm agreement by 25% without sacrificing predictive accuracy.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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Published on: August 7, 2017

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Cognitive Science
Machine Learning
Artificial Intelligence

Background:

Human cognition utilizes heuristics for simplifying complex information processing.
Bayesian networks are graphical models representing probabilistic relationships between variables.
Unsupervised learning algorithms construct Bayesian networks from data without prior labels.

Purpose of the Study:

To investigate the utility of a common human cognitive heuristic in unsupervised Bayesian network learning.
To determine if incorporating human-like causal inference improves network structure and accuracy.
To assess the impact of this heuristic on the agreement between different network learning algorithms.

Main Methods:

Applied a cognitive heuristic that favors causal over diagnostic relationships to orient arcs in Bayesian networks.
Evaluated the heuristic's effect on predictive accuracy using standard metrics.
Quantified the agreement between multiple network learning algorithms (Max Spanning Tree, Taboo, EQ, SopLeq, Taboo Order) before and after heuristic application using Kappa statistic.

Main Results:

The heuristic led to a statistically significant increase in agreement between different network learning algorithms, raising the multiple raters Kappa from 0.60 to 0.85.
This improvement in algorithm consensus was achieved without a significant decrease in predictive accuracy (average increase of 0.51%).
The heuristic enhanced the total agreement among the five tested algorithms by 25%.

Conclusions:

A common human cognitive heuristic can enhance the construct validity and agreement of unsupervised Bayesian network learning algorithms.
The heuristic offers a method to improve the reliability and consistency of network structures derived from data.
Further research is warranted to validate these findings across diverse datasets and to elucidate the underlying mechanisms of heuristic-driven algorithmic improvement.