Face processing in humans is compatible with a simple shape–based model of vision

Riesenhuber, Maximilian; Jarudi, Izzat; Gilad, Sharon; Sinha, Pawan

doi:10.1098/rsbl.2004.0216

Cited by 127 publications

(119 citation statements)

References 20 publications

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“…Several studies suggested that face inversion disturbs the processing of configural/holistic information in a face more than that of the facial features either overall across the entire face (Rhodes et al, 1993;Cabeza and Kato, 2000;Freire et al, 2000;Leder and Bruce, 2000;Barton et al, 2001;Leder et al, 2001;Rossion and Gauthier, 2002;Leder and Carbon, 2006) or at least in the lower, mouth region (Xu and Tanaka, 2013;Tanaka et al, 2009). Other studies, however, emphasize that the processing of featural and configural information are equally disrupted by face inversion (Riesenhuber et al, 2004;Yovel and Kanwisher, 2004; for a review see Tanaka and Gordon, 2011). Despite the lack of consensus regarding the details 2 , common to all these studies is the assumption that certain mechanisms, exclusively involved in the processing of upright faces are interrupted by picture-plane inversion.…”

Section: Discussionmentioning

confidence: 99%

Repetition probability effects for inverted faces

et al. 2014

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Section: Discussionmentioning

confidence: 99%

Repetition probability effects for inverted faces

et al. 2014

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“…This has been illustrated by the fresh perspective that computational modelling has afforded of the distinction between part-based and configural processing of objects and faces 117 . Computational evidence has also revealed that image fragments of intermediate complexity are more informative for object categorization than fragments of low or high complexity 118,119 , suggesting that neural coding in object-selective areas might be based on such intermediate-complexity shape features.…”

Section: Object Category As a Basic Propertymentioning

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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“…Most importantly, researchers have found that inversion in the picture plane impairs how observers performed with facial configurations rather than facial features (Collishaw & Hole, 2002;Freire, Lee, & Symons, 2000;Goffaux & Rossion, in press;Leder, Candrian, Huber, & Bruce, 2000;Tanaka & Farah, 1993). Others have found that inversion simply affects how efficiently facial features are processed (Sekuler, Gasper, Gold, & Bennett, 2004;Riesenhuber, Jarudi, Gilad, & Sinha, 2004;Yovel & Kanwisher, 2004). By comparison, nonface objects may be recognized primarily on the basis of features and coarse spatial relations (Biederman, 1987) without a corresponding reliance on metric configurations of features.…”

Section: Introductionmentioning

confidence: 99%

Recognizing rotated faces and Greebles: What properties drive the face inversion effect?

Ashworth¹,

Vuong

Rossion

et al. 2008

Visual Cognition

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The fact that faces are strongly affected by picture-plane inversion has often been cited as evidence for face-specific mechanisms. It is unclear, however, whether this "face inversion effect" is driven by properties shared by faces or whether the effect is specific to faces as a category. To address this issue, we compared the recognition of faces and novel Greebles, which were specifically matched to faces along various stimulus dimensions. In two experiments, participants were required to name individual faces or Greebles following training at either single or multiple orientations. We found that performance systematically decreased with increasing misorientation from either the upright (Experiment 1) or nearest trained orientation (Experiment 2). Importantly, the magnitude of this orientation effect was similar for both faces and Greebles. Taken together, these results suggest that the face inversion effect may be a consequence of the visual homogeneity of the stimulus category, regardless of the category.Face inversion 2

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Face processing in humans is compatible with a simple shape–based model of vision

Cited by 127 publications

References 20 publications

Repetition probability effects for inverted faces

Repetition probability effects for inverted faces

Interpreting fMRI data: maps, modules and dimensions

Recognizing rotated faces and Greebles: What properties drive the face inversion effect?

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