Meaningful Communication but Not Superficial Anthropomorphism Facilitates Human-Automation Trust Calibration: The Human-Automation Trust Expectation Model (HATEM)

Objective: The objective was to demonstrate anthropomorphism needs to communicate contextually useful information to increase user confidence and accurately calibrate human trust in automation.

Background: Anthropomorphism is believed to improve human-automation trust but supporting evidence remains equivocal. We test the Human-Automation Trust Expectation Model (HATEM) that predicts improvements to trust calibration and confidence in accepted advice arising from anthropomorphism will be weak unless it aids naturalistic communication of contextually useful information to facilitate prediction of automation failures.

Method: Ninety-eight undergraduates used a submarine periscope simulator to classify ships, aided by the Ship Automated Modelling (SAM) system that was 50% reliable. A between-subjects 2 × 3 design compared SAM (anthropomorphic avatar vs. camera eye) and voice (monotone vs. meaningless vs. meaningful), with the inflections communicating contextually useful information about automated advice regarding certainty and uncertainty.

Results: SAM appearance was rated as more anthropomorphic than camera , and and inflections were both rated more anthropomorphic than . However, for subjective trust, trust calibration, and confidence in accepting SAM advice, there was no evidence of anthropomorphic appearance having any impact, while there was decisive evidence that inflections yielded better outcomes on these trust measures than and inflections.

Conclusion: Anthropomorphism had negligible impact on human-automation trust unless its execution enhanced communication of relevant information that allowed participants to better calibrate expectations of automation performance.

Application: Designers using anthropomorphism to calibrate trust need to consider what contextually useful information will be communicated via anthropomorphic features.