Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects

Gauthier, Isabel; Tarr, Michael J.; Anderson, Adam W.; Skudlarski, Pawel; Gore, John C.

doi:10.1038/9224

Cited by 1,084 publications

(926 citation statements)

References 33 publications

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“…Although the abstract paintings are as complex and interesting as the scenes and cover the same section of the visual field, they do not depict realistic three-dimensional environments and thus would be predicted by the spatial layout hypothesis to engage the PPA less strongly than the scenes, as we observed. The FFA, in line with previous findings [Gauthier et al, 1999;Tarr and Gauthier, 2000;Tong et al, 2000;Xu, 2005] while responding most strongly to faces, also responded more to other nonface stimuli than to the blank-screen baseline.…”

Section: Ffa and Ppa Activitysupporting

confidence: 90%

The engagement of mid‐ventrolateral prefrontal cortex and posterior brain regions in intentional cognitive activity

Dove¹,

Manly²,

Epstein³

et al. 2007

Human Brain Mapping

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It is now widely recognized that cognitive processes are carried out by a distributed network of brain areas, some of which are involved in perceptual processing of a stimulus, whilst others are involved in cognitive control processes required to carry out certain tasks. In this study, differential contributions of higher visual areas and of an area involved in cognitive control processes were investigated in a task requiring participants to simply look at a stimulus or to look with the intention of remembering. Varying the extent to which intentional cognitive processes were required and the stimulus material in this task allowed the analysis of ''top-down'' and ''bottom-up'' influences on these areas, respectively. Significant increases in the mid-ventrolateral prefrontal cortex (mid-VLPFC) were only observed when the stimuli were viewed with an intention in mind, irrespective of the stimulus type. In contrast, activity in the parahippocampal place area and the fusiform face area, was only modulated in conditions requiring intentional control when stimuli were presented that also elicited activity in these regions during passive viewing. These findings help to clarify the complimentary role that the mid-VLPFC and posterior higher visual areas play in controlled and relatively automatic memory process-

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Section: Ffa and Ppa Activitysupporting

confidence: 90%

The engagement of mid‐ventrolateral prefrontal cortex and posterior brain regions in intentional cognitive activity

Dove¹,

Manly²,

Epstein³

et al. 2007

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…3 Gauthier and colleagues have amassed a great deal of evidence for this hypothesis Gauthier, Anderson, Tarr, Skudlarski & Gore, 1997;Gauthier, Tarr, Moylan, Anderson & Gore, 1998;Gauthier, Tarr, Anderson, Skudlarski & Gore, 1999). Our experiments do not address the issue directly, but the nearest neighbor subordinate classification results for cups and cans (and, in fact, cup identification and can identification experiments we have not reported in detail) suggest that in many cases, subordinate classification may be biased toward low spatial frequency information.…”

Section: Subordinate Classification Accuracymentioning

confidence: 68%

Organization of face and object recognition in modular neural network models

1999

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There is strong evidence that face processing in the brain is localized. The double dissociation between prosopagnosia, a face recognition deficit occurring after brain damage, and visual object agnosia, difficulty recognizing other kinds of complex objects, indicates that face and non-face object recognition may be served by partially independent neural mechanisms. In this paper, we use computational models to show how the face processing specialization apparently underlying prosopagnosia and visual object agnosia could be attributed to (1) a relatively simple competitive selection mechanism that, during development, devotes neural resources to the tasks they are best at performing, (2) the developing infant's need to perform subordinate classification (identification) of faces early on, and (3) the infant's low visual acuity at birth. [361][362][363][364][365][366][367][368] in the mixtures of experts (ME) modeling paradigm, we provide a preliminary computational demonstration of how this theory accounts for the double dissociation between face and object processing. We present two feed-forward computational models of visual processing. In both models, the selection mechanism is a gating network that mediates a competition between modules attempting to classify input stimuli. In Model I, when the modules are simple unbiased classifiers, the competition is sufficient to achieve enough of a specialization that damaging one module impairs the model's face recognition more than its object recognition, and damaging the other module impairs the model's object recognition more than its face recognition. However, the model is not completely satisfactory because it requires a search of parameter space. With Model II, we explore biases that lead to more consistent specialization. We bias the modules by providing one with low spatial frequency information and the other with high spatial frequency information. In this case, when the model's task is subordinate classification of faces and superordinate classification of objects, the low spatial frequency network shows an even stronger specialization for faces. No other combination of tasks and inputs shows this strong specialization. We take these results as support for the idea that something resembling a face processing "module" could arise as a natural consequence of the infant's developmental environment without being innately specified. ᭧

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“…This idea has been mostly put forward for face-selective regions, based on evidence that faces are processed more holistically than other types of object [105][106][107][108] . The hypothesis that the face-selective cortex is not actually selective for faces per se, but rather for the neural processing that is triggered by our extensive expertise with faces, has been tested with other object categories for which some individuals have expertise [109][110][111] -for example, cars and birds in car experts and ornithologists, respectively. However, recent evidence casts doubt on the idea that holistic processing occurs for any expert object category other than faces 112 , and all fMRI studies of expertise that investigated both the FFA and the LOC found that any increased responses to expert categories were larger in the LOC than in the FFA 112,113 .…”

Section: Basic Properties In the Ventral Visual Cortexmentioning

confidence: 99%

Interpreting fMRI data: maps, modules and dimensions

2008

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Neuroimaging research over the past decade has revealed a detailed picture of the functional organization of the human brain. Here we focus on two fundamental questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli.Advances in brain imaging technology (especially functional MRI (fMRI)) have radically improved our understanding of the functional organization of the human brain (BOX 1). In this Review we describe the organization of the ventral visual pathway, which is characterized by strong selectivity for particular object categories (for example, faces and bodies) at the level of both individual neurons and larger cortical regions. We then consider two central questions: whether this organization reflects maps or modules, and what properties are mapped. In each case we derive clues from the literature on the primary sensory cortex, in which cortical maps have been studied extensively using electrophysiology in animals. We find that apparently modular cortical regions, such as orientation columns and face-selective regions, might be parts of larger maps, and show that it is a substantial challenge to determine the basic properties and dimensions that describe functional organization most parsimoniously. We then propose a new framework that reconciles the existence of graded cortical maps and distinct functional modules. In this framework, the strong category selectivity that exists for faces and other objects might arise from the nonlinear combination of multiple correlated maps for simpler stimulus properties. The ventral visual pathwayThe ventral visual pathway comprises a large cortical region that occupies the ventral and lateral surfaces of the occipital and temporal lobes (FIG. 1). A substantial proportion of fMRI voxels in this pathway are 'object-selective' -that is, they respond more strongly when people view images of objects than when people view scrambled versions of these objects or texture patterns. This object-selective region is often referred to as the lateral occipital complex (LOC) 1 . The LOC has little selectivity for particular stimulus categories 2-4 , but several regions of NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript cortex near the LOC are selective for particular object categories: they respond at least twice as strongly to their 'preferred' stimuli than to other stimuli. For example, in essentially all humans cortical regions can be found that respond selectively to faces (the fusiform face area (FFA) 5,6 and, in many individuals, the occipital face area (OFA)) 7,8 , to pl...

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Activation of the middle fusiform 'face area' increases with expertise in recognizing novel objects

Cited by 1,084 publications

References 33 publications

The engagement of mid‐ventrolateral prefrontal cortex and posterior brain regions in intentional cognitive activity

The engagement of mid‐ventrolateral prefrontal cortex and posterior brain regions in intentional cognitive activity

Organization of face and object recognition in modular neural network models

Interpreting fMRI data: maps, modules and dimensions

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