Functional Neuroanatomy of Face and Object Processing

Sergent, Justine; Ohta, Shinsuke; MacDonald, B.A.

doi:10.1093/brain/115.1.15

Cited by 1,423 publications

(826 citation statements)

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“…It appears that the acquisition of expertise in subordinate classification of a novel synthetic object class, "Greebles", leads to a similar sensitivity to configuration information . Gauthier, Tarr, Anderson, Skuklarski and Gore (1998) have also observed in fMRI studies that expert-level Greeble classification activates an area in fusiform gyrus thought by some to be specialized for faces (McCarthy, Puce, Gore & Allison, 1997;Sergent, Ohta & MacDonald, 1992).…”

Section: How Might Face Recognition Differ From General Object Recognmentioning

confidence: 90%

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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Section: How Might Face Recognition Differ From General Object Recognmentioning

confidence: 90%

Organization of face and object recognition in modular neural network models

1999

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“…We speculate that the neuroanatomical locus of the neural processes indexed by the N350 identification effect is in occipitotemporal and mid-fusiform regions [13] implicated in shape representation and identification functions that may be consciously assessable [12,[16][17][18]22,24,25,28,32,38,44,49,57], as the related Ncl has been localized to these areas. Many brain regions seem to contribute to LPC effects [19], such as temporal regions linked to object knowledge and naming processes [12].…”

Section: Neuroanatomical Considerationsmentioning

confidence: 93%

Neurophysiological evidence for two processing times for visual object identification

Schendan

2002

Neuropsychologia

120

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Event-related brain potentials (ERPs) were recorded to fragmented pictures of objects that were named correctly or were not to investigate the time course of visual object identification. The first ERP difference distinguishing identified from unidentified pictures estimates the upper limit of the time by which human brain regions have begun to activate long-term memory (LTM) representations specifying the identity of a visual object. Data from 15 young adults indicate that this time varies with the extent to which object parts are recoverable from the visual input, being ∼200 ms earlier with recoverable than unrecoverable parts. Successful identification is evident by ∼300 ms when object parts and overall structural configuration are readily recoverable but not until ∼550 ms when object parts are difficult or impossible to recover (i.e. too poorly specified by the available contours to be recovered). In both cases, successful identification is associated with greater relative positivity. However, unidentified recoverable pictures are associated with an enhanced frontal negativity (N350), linked to object matching operations, not seen for non-recoverable pictures. Taken together, these results implicate two distinct processing sequences in the successful identification of visual objects.

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“…is difficult when they are part of two otherwise different faces [see Hancock et al, 2000]. Furthermore, also in facerecognition, specifically face-sensitive, areas have been localized, namely in the lateral fusiform gyrus, inferior occipital gyri, and the superior temporal sulcus, and also for the processing of face information, a ventral processing stream has been suggested in temporal lobe areas and the ventral limbic system [Haxby et al, 2000;Kanwisher et al, 1997;Perrett et al, 1984;Sergent et al, 1992]. These observations and the number of further parallels in voice and face processing [Belin et al, 2002;Mann et al, 1979] make it likely that both domains follow common principles.…”

Section: Mismatch Response As An Indicator Of Gestalt-like Processingmentioning

confidence: 99%

Dissociation of human and computer voices in the brain: Evidence for a preattentive gestalt‐like perception

Lattner

Maess

Wang

et al. 2003

Human Brain Mapping

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Abstract:We investigated the early ("preattentive") cortical processing of voice information, using the so-called "mismatch response." This brain potential allows inferences to be made about the sensory short-term store. Most importantly, the mismatch potential also provides information about the organization of long-term memory traces in the auditory system. Such traces have reliably been reported for phonemes. However, it is unclear whether they also exist for human voice information. To explore this issue, 10 healthy subjects were presented with a single word stimulus uttered by voices of different prototypicality (natural, manipulated, synthetic) in a mismatch experiment (stimulus duration 380 msec, onset-to-onset interval 900 msec). The event-related magnetic fields were recorded by a 148-channel whole-head magnetometer and a source current density modeling of the magnetic field data was performed using a minimum-norm estimate. Each deviating voice signal in a series of standard-voice stimuli evoked a mismatch response that was localized in temporal brain regions bilaterally. Increased mismatch related magnetic flux was observed in response to decreased prototypicality of a presented voice signal, but did not correspond to the acoustic similarity of standard voice and deviant voices. We, therefore, conclude that the mismatch activation predominantly reflects the ecological validity of the voice signals. We further demonstrate that the findings cannot be explained by mere acoustic feature processing, but rather point towards a holistic mapping of the incoming voice signal onto long-term representations in the auditory memory. Hum. Brain Mapping 20:13-21, 2003.

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Functional Neuroanatomy of Face and Object Processing

Cited by 1,423 publications

References 0 publications

Organization of face and object recognition in modular neural network models

Organization of face and object recognition in modular neural network models

Neurophysiological evidence for two processing times for visual object identification

Dissociation of human and computer voices in the brain: Evidence for a preattentive gestalt‐like perception

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