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Related Experiment Video

Updated: May 1, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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Benchmarking automation-aided performance in a forensic face matching task.

Megan L Bartlett¹, Daniel J Carragher¹, Peter J B Hancock²

¹School of Psychology, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, 5005, Australia.

Applied Ergonomics

|August 9, 2024

Summary

This summary is machine-generated.

Human performance using Automated Facial Recognition Systems (AFRS) in face matching tasks is suboptimal. Reanalysis shows collaboration with AFRS is inefficient, failing to match system accuracy alone.

Keywords:

Face recognition Human-automation interaction Signal detection theory

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Area of Science:

Cognitive Psychology
Human-Computer Interaction
Forensic Science

Background:

Previous research by Carragher and Hancock (2023) indicated suboptimal performance in one-to-one face matching tasks when aided by an Automated Facial Recognition System (AFRS).
AFRS-assisted individuals did not achieve the performance levels of the AFRS operating independently.

Purpose of the Study:

To reanalyze existing data on AFRS-assisted face matching.
To benchmark automation-aided performance against statistical models of collaborative decision-making.
To assess the efficiency of human-automation collaboration in face identification.

Main Methods:

Reanalysis of data from Carragher and Hancock (2023).
Application of a Bayesian hierarchical signal detection model.
Comparison of collaborative performance against various statistical models of automation dependence.

Main Results:

Collaborative performance in face matching was found to be highly inefficient.
The observed inefficiency closely aligned with the most suboptimal models of automation dependence.
This pattern extends previous findings of suboptimal human-automation interaction in diverse decision-making tasks.

Conclusions:

Human-automation collaboration in one-to-one face matching tasks is significantly inefficient.
This study provides the first benchmarks for automation-aided performance in face matching.
Findings highlight the need for improved models of human-automation interaction.