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Trust in Shared-Space Collaborative Robots: Shedding Light on the Human Brain.

Sarah K Hopko1, Ranjana K Mehta1

  • 1Texas A&M University, College Station, USA.

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Summary

Neuroergonomic metrics reveal neural indicators of distrust in human-robot collaboration. Brain activation in the prefrontal cortex and altered connectivity signal distrust, offering insights beyond subjective trust measures.

Keywords:
Trustautomationbrainfunctional Near-Infrared Spectroscopyhuman-robot collaboration

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Area of Science:

  • Neuroscience
  • Human-Robot Interaction
  • Cognitive Science

Background:

  • Industry 4.0 enhances manufacturing through human-robot collaboration.
  • Trust is crucial for safety and quality in shared human-robot workspaces.
  • Current understanding of trust indicators in human-robot collaboration is limited.

Purpose of the Study:

  • To investigate neural responses associated with trust in a human-robot collaboration task.
  • To explore the utility of granular and multimodal perspectives for studying trust.
  • To identify physiological indicators of trust and distrust.

Main Methods:

  • Sixteen participants performed a surface finishing task with a UR10 collaborative robot.
  • Experimental conditions included varying robot reliability and assistance levels.
  • Brain activation and connectivity were measured using functional near-infrared spectroscopy (fNIRS), alongside subjective and performance data.

Main Results:

  • Increased neural activation in medial and right dorsolateral prefrontal cortex (PFC) in response to faulty robot behavior.
  • Similar activation trends observed in anterior PFC, primary motor cortex, and primary visual cortex.
  • Faulty robot behavior led to decreased functional brain connectivity.

Conclusions:

  • Prefrontal cortex regions and specific connectivity patterns are key indicators of distrust in human-robot collaboration.
  • Neural responses provide insights into how trust is influenced, measured, and manifested during active teaming.
  • Neuroergonomic metrics offer novel perspectives on automation trust, complementing subjective assessments.