Do some names just ‘fit’ a face? The face-name matching effect revisited.

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Authors: Yoshi Alaerts, Steven Verheyen
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Abstract

We will conduct a study that builds further upon the study by Zwebner et al. (2017) and by Meers (2024) to investigate the face-name matching effect. This will be done with a name learning paradigm in which participants have to learn 20 face-name pairs. In all the trials, the name to be learned will be the depicted person’s actual name. In half of the trials, the face-name matches will have a high goodness-of-fit, and in the other half of the trials, the face-name matches will have a low goodness-of-fit. The goodness-of-fit was conceptualized as the proportion of participants who chose the name as the true name of the depicted person in a previous study using the same stimuli. The names with a high goodness-of-fit were chosen above chance level, while the names with a low goodness-of-fit were chosen below chance level. The participants will be KU Leuven psychology students who will be compensated with course credit. The name learning experiment commences with an informed consent form and demographic questions (age, nationality, mother tongue & gender). In the first phase, participants are shown all 20 face-name pairs once in a random order. In the second phase, participants are shown just the face and have to type in the correct name for each face. Corrective feedback is provided on every trial. This second phase is conducted repeatedly until the participant has correctly named all 20 faces or until the maximum number of rounds (15) is reached. Finally, participants will be asked whether they recognize any of the faces (familiarity question). The manipulation is conducted within participants. We expect that they will learn the face-name matches with a high goodness-of-fit faster than those with a low goodness-of-fit. The study will be carried out in groups in a university computer classroom using PsychoPy.

Fields of Study & Subjects

Face Recognition and Perception Memory Processes and Influences Face recognition and analysis

Publication details

Publication date
1 January 2026
Language
EN
Document type
Preprint