scholarly journals What can bees really tell us about the face processing system in humans?

2006 ◽  
Vol 209 (16) ◽  
pp. 3266-3266 ◽  
Author(s):  
O. Pascalis
Keyword(s):  
Face Processing ◽  
Processing System ◽  
The Face

scholarly journals Does Prosopagnosia Take the Eyes Out of Face Representations? Evidence for a Defect in Representing Diagnostic Facial Information following Brain Damage

2005 ◽  
Vol 17 (10) ◽  
pp. 1652-1666 ◽  
Author(s):  
Roberto Caldara ◽  
Philippe Schyns ◽  
Eugéne Mayer ◽  
Marie L. Smith ◽  
Frédéric Gosselin ◽  
...  
Keyword(s):  
Brain Damage ◽  
Face Processing ◽  
Processing System ◽  
Facial Information ◽  
Spatial Frequencies ◽  
The Face ◽  
Occipitotemporal Cortex ◽  
Response Classification ◽  
Unfamiliar Faces ◽  
Normal Controls

One of the most impressive disorders following brain damage to the ventral occipitotemporal cortex is prosopagnosia, or the inability to recognize faces. Although acquired prosopagnosia with preserved general visual and memory functions is rare, several cases have been described in the neuropsychological literature and studied at the functional and neural level over the last decades. Here we tested a brain-damaged patient (PS) presenting a deficit restricted to the category of faces to clarify the nature of the missing and preserved components of the face processing system when it is selectively damaged. Following learning to identify 10 neutral and happy faces through extensive training, we investigated patient PS's recognition of faces using Bubbles, a response classification technique that sampled facial information across the faces in different bandwidths of spatial frequencies [Gosselin, F., & Schyns, P. E., Bubbles: A technique to reveal the use of information in recognition tasks. Vision Research, 41, 2261-2271, 2001]. Although PS gradually used less information (i.e., the number of bubbles) to identify faces over testing, the total information required was much larger than for normal controls and decreased less steeply with practice. Most importantly, the facial information used to identify individual faces differed between PS and controls. Specifically, in marked contrast to controls, PS did not use the optimal eye information to identify familiar faces, but instead the lower part of the face, including the mouth and the external contours, as normal observers typically do when processing unfamiliar faces. Together, the findings reported here suggest that damage to the face processing system is characterized by an inability to use the information that is optimal to judge identity, focusing instead on suboptimal information.

Face Detection in Natural Scenes

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 67-67 ◽  
Author(s):  
H Hill ◽  
R Watt
Keyword(s):  
Face Detection ◽  
Face Processing ◽  
Feature Detection ◽  
Processing System ◽  
Decision Task ◽  
Natural Scenes ◽  
Natural Scene ◽  
Energy Models ◽  
The Face ◽  
And Performance

The first task of any face processing system is detection of the face. We studied how the human visual system achieves face detection using a 2AFC task in which subjects were required to detect a face in the image of a natural scene. Luminance noise was added to the stimuli and performance was measured as a function of orientation and orientation bandwidth of the noise. Sensitivity levels and the effects of orientation bandwidth were similar for horizontally and vertically oriented noise. Performance was reduced for the smallest orientation bandwidth (5.6°) noise but sensitivity did not decrease further with increasing bandwidth until a point between 45° and 90°. The results suggest that important information may be oriented close to the vertical and horizontal. To test whether the results were specific to the task of face detection the same noise was added to the images in a man-made natural decision task. Equivalent levels of noise were found to be more disruptive and the effect of orientation bandwidth was different. The results are discussed in terms of models of face processing making use of oriented filters (eg Watt and Dakin, 1993 Perception22 Supplement, 12) and local energy models of feature detection (Morrone and Burr, 1988 Proceedings of the Royal Society of London B235 221 – 245).

Representations in learning new faces: Evidence from prosopagnosia

1996 ◽  
Vol 2 (3) ◽  
pp. 240-248 ◽  
Author(s):  
Michael R. Polster ◽  
Steven Z. Rapcsak
Keyword(s):  
Left Hemisphere ◽  
Face Processing ◽  
Processing System ◽  
Poor Performance ◽  
Facial Features ◽  
Study Phase ◽  
Learning Tasks ◽  
The Face ◽  
Feature Based ◽  
Pictorial Representations

AbstractWe report the performance of a prosopagnosic patient on face learning tasks under different encoding instructions (i.e., levels of processing manipulations). R.J. performs at chance when given no encoding instructions or when given “shallow” encoding instructions to focus on facial features. By contrast, he performs relatively well with “deep” encoding instructions to rate faces in terms of personality traits or when provided with semantic and name information during the study phase. We propose that the improvement associated with deep encoding instructions may be related to the establishment of distinct visually derived and identity-specific semantic codes. The benefit associated with deep encoding in R.J., however, was found to be restricted to the specific view of the face presented at study and did not generalize to other views of the same face. These observations suggest that deep encoding instructions may enhance memory for concrete or pictorial representations of faces in patients with prosopagnosia, but that these patients cannot compensate for the inability to construct abstract structural codes that normally allow faces to be recognized from different orientations. We postulate further that R.J.'s poor performance on face learning tasks may be attributable to excessive reliance on a feature-based left hemisphere face processing system that operates primarily on view-specific representations. (JINS, 1996, 2, 240–248.)

Fuzzy-Face

1998 ◽  
Vol 06 (03) ◽  
pp. 281-298 ◽  
Author(s):  
Terry Huntsberger ◽  
John Rose ◽  
Shashidhar Ramaka
Keyword(s):  
Face Processing ◽  
Gender Discrimination ◽  
Computational Models ◽  
Experimental Studies ◽  
Processing System ◽  
Facial Hair ◽  
Lighting Conditions ◽  
Face Images ◽  
The Face ◽  
Strong Motivation

The human face is one of the most important patterns our visual system receives. It establishes a person's identity and also plays a significant role in everyday communication. Humans can recognize familiar faces under varying lighting conditions, different scales, and even after the face has changed due to aging, hair style, glasses, or facial hair. Our ease at recognizing faces is a strong motivation for the investigation of computational models of face processing. This paper presents a newly developed face processing system called Fuzzy-Face that combines wavelet pre-processing of input with a fuzzy self-organizing feature map algorithm. The wavelet-derived face space is partitioned into fuzzy sets which are characterized by face exemplars and membership values to those exemplars. This system learns faces using relatively few training epochs, has total recall for faces it has been shown, generalizes to face images that are acquired under different lighting conditions, and has rudimentary gender discrimination capabilities. We also include the results of some experimental studies.

scholarly journals Evaluating the organizational structure and specificity of network topology within the face processing system

2019 ◽  
Vol 40 (9) ◽  
pp. 2581-2595 ◽  
Author(s):  
Daniel B. Elbich ◽  
Peter C.M. Molenaar ◽  
K. Suzanne Scherf
Keyword(s):  
Organizational Structure ◽  
Network Topology ◽  
Face Processing ◽  
Processing System ◽  
The Face

scholarly journals Spatiotemporal Dynamics of Covert Versus Overt Processing of Happy, Fearful and Sad Facial Expressions

2021 ◽  
Vol 11 (7) ◽  
pp. 942
Author(s):  
Antonio Maffei ◽  
Jennifer Goertzen ◽  
Fern Jaspers-Fayer ◽  
Killian Kleffner ◽  
Paola Sessa ◽  
...  
Keyword(s):  
Face Processing ◽  
Univariate Analysis ◽  
Processing System ◽  
Intermediate Stage ◽  
Task Demands ◽  
Event Related Potential ◽  
Voluntary Attention ◽  
Affective Content ◽  
Fearful Faces ◽  
The Face

Behavioral and electrophysiological correlates of the influence of task demands on the processing of happy, sad, and fearful expressions were investigated in a within-subjects study that compared a perceptual distraction condition with task-irrelevant faces (e.g., covert emotion task) to an emotion task-relevant categorization condition (e.g., overt emotion task). A state-of-the-art non-parametric mass univariate analysis method was used to address the limitations of previous studies. Behaviorally, participants responded faster to overtly categorized happy faces and were slower and less accurate to categorize sad and fearful faces; there were no behavioral differences in the covert task. Event-related potential (ERP) responses to the emotional expressions included the N170 (140–180 ms), which was enhanced by emotion irrespective of task, with happy and sad expressions eliciting greater amplitudes than neutral expressions. EPN (200–400 ms) amplitude was modulated by task, with greater voltages in the overt condition, and by emotion, however, there was no interaction of emotion and task. ERP activity was modulated by emotion as a function of task only at a late processing stage, which included the LPP (500–800 ms), with fearful and sad faces showing greater amplitude enhancements than happy faces. This study reveals that affective content does not necessarily require attention in the early stages of face processing, supporting recent evidence that the core and extended parts of the face processing system act in parallel, rather than serially. The role of voluntary attention starts at an intermediate stage, and fully modulates the response to emotional content in the final stage of processing.

scholarly journals CNN explains tuning properties of anterior, but not middle, face-processing areas in macaque IT

2019 ◽  
Author(s):  
Rajani Raman ◽  
Haruo Hosoya
Keyword(s):  
Neural Networks ◽  
Face Processing ◽  
Processing System ◽  
Computational Studies ◽  
Comprehensive Model ◽  
Entire System ◽  
Face Area ◽  
The Face ◽  
Goal Representation ◽  
Model Layer

AbstractRecent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate whether CNNs exhibit tuning properties as previously observed in different macaque face areas. While simulating four past experiments on a variety of CNN models, we sought for the model layer that quantitatively matches the multiple tuning properties of each face area. Our results show that higher model layers explain reasonably well the properties of anterior areas, while no layer simultaneously explains the properties of middle areas, consistently across the model variation. Thus, some similarity may exist between CNNs and the primate face-processing system in the near-goal representation, but much less clearly in the intermediate stages, thus giving motivation for a more comprehensive model for understanding the entire system.

scholarly journals Information-Theoretic Evidence for Predictive Coding in the Face-Processing System

2017 ◽  
Vol 37 (34) ◽  
pp. 8273-8283 ◽  
Author(s):  
Alla Brodski-Guerniero ◽  
Georg-Friedrich Paasch ◽  
Patricia Wollstadt ◽  
Ipek Özdemir ◽  
Joseph T. Lizier ◽  
...  
Keyword(s):  
Face Processing ◽  
Predictive Coding ◽  
Processing System ◽  
Information Theoretic ◽  
The Face

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Underlying Mechanism ◽  
Inversion Effect ◽  
Configural Processing ◽  
Major Contributor ◽  
Face Inversion Effect ◽  
Configural Information ◽  
Face Inversion ◽  
The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

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