scholarly journals Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance

2016 ◽  
Vol 28 (2) ◽  
pp. 282-294 ◽  
Author(s):  
Rankin W. McGugin ◽  
Ana E. Van Gulick ◽  
Isabel Gauthier
Keyword(s):  
Object Recognition ◽  
Cortical Thickness ◽  
Recognition Performance ◽  
Object Perception ◽  
Fusiform Face Area ◽  
General Role ◽  
Face Area ◽  
Differential Attention ◽  
Living Objects

The fusiform face area (FFA) is defined by its selectivity for faces. Several studies have shown that the response of FFA to nonface objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. Here, we show an effect of expertise with nonface objects in FFA that cannot be explained by differential attention to objects of expertise. We explore the relationship between cortical thickness of FFA and face and object recognition using the Cambridge Face Memory Test and Vanderbilt Expertise Test, respectively. We measured cortical thickness in functionally defined regions in a group of men who evidenced functional expertise effects for cars in FFA. Performance with faces and objects together accounted for approximately 40% of the variance in cortical thickness of several FFA patches. Whereas participants with a thicker FFA cortex performed better with vehicles, those with a thinner FFA cortex performed better with faces and living objects. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects but rather living and nonliving objects.

scholarly journals Cortical Thickness in Fusiform Face Area Predicts Face and Object Recognition Performance

2015 ◽  
Vol 15 (12) ◽  
pp. 428
Author(s):  
Rankin McGugin ◽  
Ana Van Gulick ◽  
Isabel Gauthier
Keyword(s):  
Object Recognition ◽  
Cortical Thickness ◽  
Recognition Performance ◽  
Fusiform Face Area ◽  
Face Area

Revisiting the Role of the Fusiform Face Area in Expertise

2016 ◽  
Vol 28 (9) ◽  
pp. 1345-1357 ◽  
Author(s):  
Merim Bilalić
Keyword(s):  
Pattern Analysis ◽  
Spatial Relations ◽  
Fusiform Face Area ◽  
Multivariate Pattern Analysis ◽  
Multiple Objects ◽  
Activation Patterns ◽  
Face Area ◽  
Single Category ◽  
Multivariate Pattern

The fusiform face area (FFA) is considered to be a highly specialized brain module because of its central importance for face perception. However, many researchers claim that the FFA is a general visual expertise module that distinguishes between individual examples within a single category. Here, I circumvent the shortcomings of some previous studies on the FFA controversy by using chess stimuli, which do not visually resemble faces, together with more sensitive methods of analysis such as multivariate pattern analysis. I also extend the previous research by presenting chess positions, complex scenes with multiple objects, and their interrelations to chess experts and novices as well as isolated chess objects. The first experiment demonstrates that chess expertise modulated the FFA activation when chess positions were presented. In contrast, single chess objects did not produce different activation patterns among experts and novices even when the multivariate pattern analysis was used. The second experiment focused on the single chess objects and featured an explicit task of identifying the chess objects but failed to demonstrate expertise effects in the FFA. The experiments provide support for the general expertise view of the FFA function but also extend the scope of our understanding about the function of the FFA. The FFA does not merely distinguish between different exemplars within the same category of stimuli. More likely, it parses complex multiobject stimuli that contain numerous functional and spatial relations.

scholarly journals Thickness of deep layers in the fusiform face area predicts face recognition

2019 ◽  
Author(s):  
Rankin W. McGugin ◽  
Allen T. Newton ◽  
Benjamin Tamber-Rosenau ◽  
Andrew Tomarken ◽  
Isabel Gauthier
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Cortical Thickness ◽  
Neural Plasticity ◽  
Normal Population ◽  
Structural Basis ◽  
Fusiform Face Area ◽  
Vehicle Recognition ◽  
Face Area ◽  
High Resolution Mri

AbstractPeople with superior face recognition have relatively thin cortex in face-selective brain areas, while those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) for abilities acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultra-high resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term MR layers, in the right fusiform face area (rFFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, while vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) while the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of rFFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development.Significance StatementFace and object recognition vary in the normal population and are only modestly related to each other. The recognition of faces and vehicles are both positively related to neural responses in the fusiform face area (FFA), but show different relations to the cortical thickness of FFA. Here, we use very high-resolution MRI, and find that face recognition ability (a skill acquired early in life) is negatively correlated with thickness of FFA’s deepest MR-defined layers, whereas recognition of vehicles (a skill acquired later in life) is positively related to thickness at of all cortical layers. Our methods can be used in the future to characterize sources of variability in human abilities and relate them to distinct mechanisms of neural plasticity.

Minimal Recognizable Configurations Elicit Category-selective Responses in Higher Order Visual Cortex

2019 ◽  
Vol 31 (9) ◽  
pp. 1354-1367
Author(s):  
Yael Holzinger ◽  
Shimon Ullman ◽  
Daniel Harari ◽  
Marlene Behrmann ◽  
Galia Avidan
Keyword(s):  
Visual Cortex ◽  
Object Recognition ◽  
Visual Recognition ◽  
Recognition Performance ◽  
Occipital Cortex ◽  
Building Blocks ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Face Area ◽  
Early Visual Cortex

Visual object recognition is performed effortlessly by humans notwithstanding the fact that it requires a series of complex computations, which are, as yet, not well understood. Here, we tested a novel account of the representations used for visual recognition and their neural correlates using fMRI. The rationale is based on previous research showing that a set of representations, termed “minimal recognizable configurations” (MIRCs), which are computationally derived and have unique psychophysical characteristics, serve as the building blocks of object recognition. We contrasted the BOLD responses elicited by MIRC images, derived from different categories (faces, objects, and places), sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and scrambled, unrecognizable images. Stimuli were presented in blocks, and participants indicated yes/no recognition for each image. We confirmed that MIRCs elicited higher recognition performance compared to sub-MIRCs for all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs of each category did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs or scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category-selective regions including fusiform face area and occipital face area (faces), lateral occipital cortex (objects), and parahippocampal place area and transverse occipital sulcus (places). These findings reveal the psychological and neural relevance of MIRCs and enable us to make progress in developing a more complete account of object recognition.

scholarly journals Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration

2016 ◽  
Vol 28 (4) ◽  
pp. 558-574 ◽  
Author(s):  
Panqu Wang ◽  
Isabel Gauthier ◽  
Garrison Cottrell
Keyword(s):  
Object Recognition ◽  
Test Performance ◽  
Brain Research ◽  
Inverse Correlation ◽  
Memory Test ◽  
Fusiform Face Area ◽  
Face Memory ◽  
General Ability ◽  
Face Area ◽  
Basic Level

Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing [“The Model”, TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a “spreading transform” for faces (separating them in representational space) that generalizes to objects that must be individuated. Interestingly, when the task of the network is basic level categorization, no increase in the correlation between domains is observed. Hence, our model predicts that it is the type of experience that matters and that the source of the correlation is in the fusiform face area, rather than in cortical areas that subserve basic level categorization. This result is consistent with our previous modeling elucidating why the FFA is recruited for novel domains of expertise [Tong, M. H., Joyce, C. A., & Cottrell, G. W. Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation. Brain Research, 1202, 14–24, 2008].

scholarly journals The role of the fusiform face area in social cognition: implications for the pathobiology of autism

2003 ◽  
Vol 358 (1430) ◽  
pp. 415-427 ◽  
Author(s):  
Robert T. Schultz ◽  
David J. Grelotti ◽  
Ami Klin ◽  
Jamie Kleinman ◽  
Christiaan Van der Gaag ◽  
...  
Keyword(s):  
Social Cognitive ◽  
Semantic Information ◽  
Fusiform Face Area ◽  
Functional Magnetic Resonance ◽  
Lateral Aspect ◽  
Face Area ◽  
Temporal Pole ◽  
Human Faces ◽  
Special Relevance

A region in the lateral aspect of the fusiform gyrus (FG) is more engaged by human faces than any other category of image. It has come to be known as the ‘fusiform face area’ (FFA). The origin and extent of this specialization is currently a topic of great interest and debate. This is of special relevance to autism, because recent studies have shown that the FFA is hypoactive to faces in this disorder. In two linked functional magnetic resonance imaging (fMRI) studies of healthy young adults, we show here that the FFA is engaged by a social attribution task (SAT) involving perception of human–like interactions among three simple geometric shapes. The amygdala, temporal pole, medial prefrontal cortex, inferolateral frontal cortex and superior temporal sulci were also significantly engaged. Activation of the FFA to a task without faces challenges the received view that the FFA is restricted in its activities to the perception of faces. We speculate that abstract semantic information associated with faces is encoded in the FG region and retrieved for social computations. From this perspective, the literature on hypoactivation of the FFA in autism may be interpreted as a reflection of a core social cognitive mechanism underlying the disorder.

Sign in / Sign up

Export Citation Format

Share Document