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Related Experiment Video

Updated: Jun 26, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Towards a 'manifesto' for super-recognizer research.

Meike Ramon¹, Anna K Bobak², David White³

¹Applied Face Cognition Lab, University of Fribourg, Switzerland.

British Journal of Psychology (London, England : 1953)

|June 25, 2019

Summary

Super-recognizers possess exceptional facial recognition skills. This response proposes a flexible framework and guiding principles for assessing superior face recognition abilities in future research.

Keywords:

Super-recognizers face identification face matching face processing face recognition open science replicability

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Area of Science:

Cognitive Psychology
Forensic Science

Background:

The study of super-recognizers, individuals with extraordinary facial recognition abilities, has gained traction.
Understanding the assessment and application of these skills is crucial for both scientific advancement and real-world scenarios.

Purpose of the Study:

To synthesize common themes from commentaries on 'Super-recognizers: From the lab to the world and back again'.
To propose a flexible framework for assessing superior facial recognition capabilities.
To establish guiding principles for future research in the field of super-recognition.

Main Methods:

Analysis of five expert commentaries on the original article.
Identification of consensus points and areas for development.
Framework development based on synthesized feedback.

Main Results:

Commonalities across commentaries highlight the need for standardized assessment methods.
A flexible framework for evaluating superior face recognition has been proposed.
Key principles for advancing future research have been outlined.

Conclusions:

The proposed framework offers a structured approach to studying super-recognizers.
Guiding principles will facilitate more robust and comparable research in facial recognition.
This work aims to bridge the gap between laboratory findings and real-world applications of super-recognition.