Two areas for familiar face recognition in the primate brain

Landi, S. M.; Freiwald, Winrich A.

doi:10.1126/science.aan1139

Cited by 117 publications

(86 citation statements)

References 66 publications

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“…Consistent with human MEG data 21 , facial representations in macaques were also found to gradually build up and become more invariant to viewpoint at successive processing stages, measured both spatially and temporally 39,43 , again showing the usefulness of the macaque's face perception system as a model to study human face perception. With regard to familiarity, our findings are in line with a recent study in macaques reporting early quantitative differences and late qualitative differences in processing of familiar versus unfamiliar faces 44 . However, the paradigms and stimuli that have been used so far in humans and macaque studies are too different to provide a precise correspondence between species.…”

Section: Discussionsupporting

confidence: 92%

How face perception unfolds over time

Dobs

Işık

Pantazis

et al. 2018

Preprint

114

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Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. Here we used magnetencephalography to ask which properties of a face are extracted when, and how early in processing these computations are affected by face familiarity. Subjects viewed images of familiar and unfamiliar faces varying orthogonally in gender and age. Using representational similarity analysis, we found that gender and age information emerged significantly earlier than identity information, followed by a late signature of familiarity. Importantly, gender and identity representations were enhanced for familiar faces early during processing. These findings start to reveal the sequence of processing steps entailed in face perception in humans, and suggest that early stages of face processing are tuned to familiar face features.

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Section: Discussionsupporting

confidence: 92%

How face perception unfolds over time

Dobs

Işık

Pantazis

et al. 2018

Preprint

114

View full text Add to dashboard Cite

show abstract

“…Our results are consistent with strong evidence that neurons in areas ML/MF and AM code faces in terms of a continuous "shape-appearance" space 42 55 and macaques 56 .…”

Section: Discussionsupporting

confidence: 90%

Efficient inverse graphics in biological face processing

Yildirim

Freiwald

Tenenbaum

2018

Preprint

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Vision must not only recognize and localize objects, but perform richer inferences about the underlying causes in the world that give rise to sensory data. How the brain performs these inferences remains unknown: Theoretical proposals based on inverting generative models (or "analysis-by-synthesis") have a long history but their mechanistic implementations have typically been too slow to support online perception, and their mapping to neural circuits is unclear. Here we present a neurally plausible model for e ciently inverting generative models of images and test it as an account of one high-level visual capacity, the perception of faces. The model is based on a deep neural network that learns to invert a threedimensional (3D) face graphics program in a single fast feedforward pass. It explains both human behavioral data and multiple levels of neural processing in non-human primates, as well as a classic illusion, the "hollow face" e↵ect. The model fits qualitatively better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how images are transformed into percepts in the ventral stream.

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“…Our model suggests one such hypothesis, in the form of 2.5D or intrinsic image components, which capture facial appearance and shape in a view-based, image-centric frame, and correspond well to middle face patch representations in macaques. Our model also suggests how those pictorial 2.5D representations can lead downstream to a full 3D description of face shape and appearance, which would correspond to more anterior face regions that [as noted by Grossman et al (44)] have yet to be studied intracranially in humans and then further downstream to representations of familiar individuals' identities [e.g., medial temporal lobe (MTL) and perirhinal cortex], which have been characterized in both humans and macaques (45).…”

Section: Discussionmentioning

confidence: 63%

Efficient inverse graphics in biological face processing

et al. 2020

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Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.

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Two areas for familiar face recognition in the primate brain

Cited by 117 publications

References 66 publications

How face perception unfolds over time

How face perception unfolds over time

Efficient inverse graphics in biological face processing

Efficient inverse graphics in biological face processing

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