ArticleHuman factors2024
Meaningful Communication but not Superficial Anthropomorphism Facilitates Human-Automation Trust Calibration: The Human-Automation Trust Expectation Model (HATEM).
Article in Human factors, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.Biomimetics (Basel, Switzerland) · 2026Article
- Development of the Windmill Model for Mapping Older Adults' Intrinsic Capacity Using Digital Twin Technology: Descriptive Qualitative Study.JMIR aging · 2026Article
- The Influence of Explainable Artificial Intelligence and Mental Health Literacy on Professional Psychological Help-Seeking Behavior: The Mediating Role of Psychological Problem Cognition.Behavioral sciences (Basel, Switzerland) · 2025Article
- An innovative approach to welding robot operator reliability analysis using extended TESEO and HEART methods.Scientific reports · 2025Article
- Transparency improves the accuracy of automation use, but automation confidence information does not.Cognitive research: principles and implications · 2024Article
- An Exploratory Study of the Potential of Online Counseling for University Students by a Human-Operated Avatar Counselor.Healthcare (Basel, Switzerland) · 2024Article
- How do humans learn about the reliability of automation?Cognitive research: principles and implications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objective was to demonstrate anthropomorphism needs to communicate contextually useful information to increase user confidence and accurately calibrate human trust in automation.
backgroundAnthropomorphism 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.
methodNinety-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
results
conclusionAnthropomorphism 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.