Prototype-referenced shape encoding revealed by high-level aftereffects

By David A. Leopold, Alice J. O'Toole, Thomas Vetter, Volker Blanz. Nature Neuroscience, 2001

918 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/31187dd3b1a96e53cb43470b639d331390961823

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Citations in the corpus

A 3D Face Model for Pose and Illumination Invariant Face Recognition (2009, background): “They have become a well established technology able to perform various tasks, most important face recognition [8, 18, 11], but also face image analysis [7] (estimating the 3D shape from a single photograph), expression transfer between individuals [6, 17], animation of faces and whole bodies [6, 1],”
The dynamics of visual adaptation to faces (2005, methodology): “A neutral test face with average features, for example, is seen as possessing features that are ‘opposite’ to those of the adapting face, i.e. the adapting face’s ‘anti-face’ is seen (see Leopold et al. 2001).”
Mechanisms of face perception. (2008, background): “Moreover, if a face is transformed further along the morph line representing the deviation of that face from the average face, the transformed face is easily recognized as the same individual (e.g. Figure 5c); this is the basis of caricature (Leopold et al 2001).”
Rapid Expectation Adaptation during Syntactic Comprehension (2013, background): “, depth and blur) according to recent experience [105] (for further work on adaptation in vision, see [106–110]).”
Retinotopic and non-retinotopic stimulus encoding in binocular rivalry and the involvement of feedback. (2008, positive): “…to motion (Mather, Verstraten, & Anstis, 1998), to spatial (Blakemore, Nachmias, & Sutton, 1970) and temporal (Saul & Cynader, 1989) frequency, to depth cues (Knapen & van Ee, 2006), and even to object content such as face identity (Leopold, O’Toole, Vetter, & Blanz, 2001; Webster & MacLin, 1999).”
Adaptation and visual coding (2011, background): “…of face perception, in which individual faces might be represented by how they differ from an average or prototype that is again calibrated by adaptation (Leopold, Bondar, & Giese, 2006; Leopold et al., 2001; Loffler et al., 2005; Rhodes & Jeffery, 2006; Rhodes et al., 2005; Robbins et al., 2007).”
Repetition and the brain: neural models of stimulus-specific effects. (2006, methodology): “using visual aftereffects to infer the nature of orientation tuning [21] or face representation [22, 23 ]), with single-cell recording [24], and more recently has become popular with fMRI, particularly given claims that it enables improved spatial resolution [7] (Box 1). The second reason for height”
Bayesian models of object perception. (2003, background): “Relating image intensity to measured surface-depth statistics has yielded computer vision solutions for face recognition [24], provided objective models [25] for face recognition experiments [26], and given insight into the functional nature of certain kinds of illusions [27 ].”
Race-contingent aftereffects suggest distinct perceptual norms for different race faces (2008, background): “Third, face adaptation indicates that the norm face has a central role in the coding of individual faces (Leopold et al., 2001; Rhodes & Jeffery, 2006).”
Object perception as Bayesian inference. (2004, background): “…has yielded computer vision solutions for face recognition (Atick et al 1996) and has provided objective prior models for face recognition experiments suggesting that human vision may represent facial characteristics along principal component dimensions in an opponent fashion (Leopold et al 2001).”
The fusiform face area: a cortical region specialized for the perception of faces (2006, background): “B (2006) (d) Norm-based coding of faces The power of caricatures to capture the likeness of a face suggests that face identity is coded in terms of deviation from the norm or average face, a hypothesis supported by behavioural studies (Rhodes et al. 1987; Leopold et al. 2001).”