Confirmation Bias Estimation from Electroencephalography with Machine Learning

Abstract

Cognitive biases are known to plague human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, behavioral methods are employed to measure the level of bias in a decision. However, these measures can be hindered by a multitude of subjective factors and cannot be collected in real-time. This work investigates enhancing the current measures of estimating confirmation bias with additional behavior patterns and physiological variables to explore the viability of real-time bias detection. Confirmation bias in decisions is estimated by modeling the relationship between Electroencephalography (EEG) signals and behavioral data using machine learning methods.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2019
Accession Number
AD1076610

Entities

People

  • Micah Villarreal

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence Software
  • Bayesian Networks
  • Cognition
  • Cognitive Workload
  • Computational Science
  • Computers
  • Convolutional Neural Networks
  • Databases
  • Detection
  • Dimensionality Reduction
  • Information Science
  • Machine Learning
  • Military Operations
  • Neural Networks
  • Recurrent Neural Networks
  • Supervised Machine Learning

Readers

  • Regression Analysis.
  • Systems Analysis and Design
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks