Comparison of Machine Learning Techniques on Trust Detection Using EEG

Abstract

Trust is a pillar of society and is a fundamental aspect in every relationship. With the use of automated agents in todays workforce exponentially growing, being able to actively monitor an individuals trust level that is working with the automation is becoming increasingly more important. Humans often have miscalibrated trust in automation and therefore are prone to making costly mistakes. Since deciding to trust or distrust has been shown to correlate with specific brain activity, it is thought that there are EEG signals which are associated with this decision. Using both a human-human trust and a human-machine trust EEG dataset from past research, within-participant, cross-participant, and cross-task cross-participant trust detection was attempted. Six machine learning models, logistic regression, LDA, QDA, SVM, RFC, and an ANN, were used for each experiment. Multiple within-participant models had balanced accuracies greater than 70.00 , but no cross-participant or cross-participant cross task models achieved this.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1132388

Entities

People

  • James R Elkins

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Cognitive Science
  • Computational Science
  • Computer Languages
  • Computer Science
  • Databases
  • Dimensionality Reduction
  • Discriminant Analysis
  • Electroencephalography
  • Engineering
  • Health Services
  • Human-Robot Interaction
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Psychology
  • Supervised Machine Learning

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  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Organizational Psychology.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks