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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Human-machine collaboration can lead to errors, potentially perceived differently by humans.
  • Error-related potentials (ErrPs) are brain signals indicating error perception.
  • Understanding error attribution is crucial for adaptive human-agent systems.

Purpose of the Study:

  • To investigate if self-related errors and agent-related errors elicit distinct ErrPs.
  • To determine if brain activity can differentiate human attributions of errors in collaborative tasks.

Main Methods:

  • Electroencephalography (EEG) was used to record brain activity during a collaborative trajectory-following task.
  • Eleven participants engaged in tasks with varying collaboration levels and movement errors.
  • Support Vector Machines (SVM) were employed for classification of error types.

Main Results:

  • A higher amplitude of ErrPs was observed at the Cz electrode for self-related errors compared to agent-related errors.
  • SVM models achieved 72.64% accuracy in classifying self- vs. agent-related errors using subject-specific features.
  • The findings were consistent across different collaboration levels.

Conclusions:

  • ErrPs can distinguish whether a human attributes an error to themselves or to an autonomous agent.
  • This differentiation provides valuable feedback for collaborative machines to improve error identification and future actions.
  • Informed error attribution enhances adaptive co-adaptation and shared control in human-agent systems.