4.7 Article

Recognizing an individual face: 3D shape contributes earlier than 2D surface reflectance information

Journal

NEUROIMAGE
Volume 47, Issue 4, Pages 1809-1818

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.05.065

Keywords

Face recognition; N170; ERP Adaptation; 2D surface reflectance; 3D shape

Funding

  1. Communaute Francaise de Belgique - Actions de Recherche Concertees [07/12-007]
  2. Human Frontier Science Program (HFSP)
  3. Belgian National Fund for Scientific Research (Fonds de la Recherche Scientifique - FNRS)

Ask authors/readers for more resources

The human brain recognizes faces by means of two main diagnostic sources of information: three-dimensional (3D) shape and two-dimensional (2D) Surface reflectance. Here we used event-related potentials (ERPs) ill a face adaptation paradigm to examine the time-course of processing for these two types of information. With a 3D morphable model, we generated pairs of faces that were either identical, varied in 3D shape only, in 2D surface reflectance only, or in both. Sixteen human observers discriminated individual faces in these 4 types of pairs, in which a first (adapting) face was followed shortly by a second (test) face. Behaviorally, observers were as accurate and as fast for discriminating individual faces based oil either 3D shape or 2D surface reflectance alone, but were faster when both sources of information were present. As early as the face-sensitive N170 component (similar to 160 ms following the test face), there was larger amplitude for changes in 3D shape relative to the repetition of the same face, especially over the right occipito-temporal electrodes. However, changes in 2D reflectance between the adapter and target face did not increase the N170 amplitude. At about 250 ms, both 3D shape and 2D reflectance contributed equally, and the largest difference in amplitude compared to the repetition of the same face was found when both 3D shape and 2D reflectance were combined, in line with observers' behavior. These observations indicate that evidence to recognize individual faces accumulate faster in the right hemisphere human Visual cortex from diagnostic 3D shape information than from 2D surface reflectance information. (C) 2009 Elsevier Inc. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Neurosciences

The inferior occipital gyrus is a major cortical source of the face-evoked N170: Evidence from simultaneous scalp and intracerebral human recordings

Corentin Jacques, Jacques Jonas, Louis Maillard, Sophie Colnat-Coulbois, Laurent Koessler, Bruno Rossion

HUMAN BRAIN MAPPING (2019)

Article Neuroimaging

Reduced neural sensitivity to rapid individual face discrimination in autism spectrum disorder

Sofie Vettori, Milena Dzhelyova, Stephanie Van der Donck, Corentin Jacques, Jean Steyaert, Bruno Rossion, Bart Boets

NEUROIMAGE-CLINICAL (2019)

Article Ophthalmology

The contribution of color information to rapid face categorization in natural scenes

Charles C. -F. Or, Talia L. Retter, Bruno Rossion

JOURNAL OF VISION (2019)

Article Behavioral Sciences

Effect of face-related task on rapid individual face discrimination

Xiaoqian Yan, Joan Liu-Shuang, Bruno Rossion

NEUROPSYCHOLOGIA (2019)

Article Behavioral Sciences

Rapid and automatic discrimination between facial expressions in the human brain

Fanny Poncet, Jean-Yves Baudouin, Milena P. Dzhelyova, Bruno Rossion, Arnaud Leleu

NEUROPSYCHOLOGIA (2019)

Article Neurosciences

A robust implicit measure of facial attractiveness discrimination

Qiuling Luo, Bruno Rossion, Milena Dzhelyova

SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE (2019)

Article Multidisciplinary Sciences

An objective, sensitive and ecologically valid neural measure of rapid human individual face recognition

Friederike G. S. Zimmermann, Xiaoqian Yan, Bruno Rossion

ROYAL SOCIETY OPEN SCIENCE (2019)

Article Behavioral Sciences

An ecological measure of rapid and automatic face-sex categorization

Diane Rekow, Jean-Yves Baudouin, Bruno Rossion, Arnaud Leleu

CORTEX (2020)

Article Neurosciences

All-or-none face categorization in the human brain

Talia L. Retter, Fang Jiang, Michael A. Webster, Bruno Rossion

NEUROIMAGE (2020)

Article Neurosciences

An implicit neural familiar face identity recognition response across widely variable natural views in the human brain

Xiaoqian Yan, Friederike G. S. Zimmermann, Bruno Rossion

COGNITIVE NEUROSCIENCE (2020)

Article Behavioral Sciences

Combined frequency-tagging EEG and eye tracking reveal reduced social bias in boys with autism spectrum disorder

Sofie Vettori, Milena Dzhelyova, Stephanie Van der Donck, Corentin Jacques, Tim Van Wesemael, Jean Steyaert, Bruno Rossion, Bart Boets

CORTEX (2020)

Article Psychology, Multidisciplinary

Impact of Learning to Read in a Mixed Approach on Neural Tuning to Words in Beginning Readers

Alice van de Walle de Ghelcke, Bruno Rossion, Christine Schiltz, Aliette Lochy

FRONTIERS IN PSYCHOLOGY (2020)

Article Psychology, Developmental

The right hemispheric dominance for face perception in preschool children depends on the visual discrimination level

Aliette Lochy, Christine Schiltz, Bruno Rossion

DEVELOPMENTAL SCIENCE (2020)

Article Psychology, Developmental

Maternal odor shapes rapid face categorization in the infant brain

Arnaud Leleu, Diane Rekow, Fanny Poncet, Benoist Schaal, Karine Durand, Bruno Rossion, Jean-Yves Baudouin

DEVELOPMENTAL SCIENCE (2020)

Article Neurosciences

Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models

Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle

Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.

NEUROIMAGE (2024)