Face neurons encode nonsemantic features

Bardon, Alexandra; Xiao, Will; Ponce, Carlos R.; Livingstone, Margaret S.; Kreiman, Gabriel

doi:10.1073/pnas.2118705119

Cited by 11 publications

(5 citation statements)

References 37 publications

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“…At a larger scale of IT cortex, our results are consistent with evidence converging on a unified understanding of organization in terms of particular texture, shape, and curvature-based visual feature tuning that underlie and support categorical distinctions (Op De Beeck et al 2008; Bao et al 2020; Yue, Robert, and Ungerleider 2020; Long, Yu, and Konkle 2018; A. V. Jagadeesh and Gardner 2022; Wang, Janini, and Konkle 2022).…”

Section: Discussionsupporting

confidence: 86%

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The neural code for ‘face cells’ is not face specific

Vinken

Konkle

Livingstone

2022

Preprint

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'Face cells' are visual neurons that selectively respond more to faces than other objects. Clustered together in inferotemporal cortex, they are thought to form a network of modules specialized in face processing by encoding face-specific features. Here we reveal that their category selectivity is instead captured by domain-general attributes. Analyzing neural responses in and around macaque face patches to hundreds of objects, we discovered graded tuning for non-face objects that was more predictive of face preference than was tuning for faces themselves. The relationship between category-level face selectivity and image-level non-face tuning was not predicted by color and simple shape properties, but by domain-general information encoded in deep neural networks trained on object classification. These findings contradict the long-standing assumption that face cells owe their category selectivity to face-specific features, challenging the prevailing idea that visual processing is carried out by discrete modules, each specialized in a semantically distinct domain.

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Section: Discussionsupporting

confidence: 86%

“…These results suggest that even the most face selective sites from a confirmed face region are not entirely categorical, and that their tuning curves do not simply encode how “face-like” any particular image is [consistent with previous evidence (Bardon et al 2022)].…”

Section: Supplemental Informationsupporting

confidence: 85%

The neural code for ‘face cells’ is not face specific

Vinken

Konkle

Livingstone

2022

Preprint

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“…There is a high discrepancy between studies in what they call a “scrambled” stimulus: from face-like patterns with altered vertical symmetry ( Buiatti et al, 2019 ) to face images modified based on the parametric texture model ( Livingstone et al, 2017 ) or diffeomorphic transformation ( Stojanoski and Cusack, 2014 ). In all cases it would be important to estimate the perceived “facedness” of such stimuli by animals (but see Bardon et al, 2022 ), because even an automatic scrambling approach might accidentally produce an illusory face-like pattern. Conversely, since behavioural tests with newborn animals are often challenging, one might consider to estimate the “facedness” of stimuli by a neural network that has been trained on face detection.…”

Section: Discussionmentioning

confidence: 99%

Face detection mechanisms: Nature vs. nurture

Kobylkov,

Vallortigara

2024

Front. Neurosci.

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For many animals, faces are a vitally important visual stimulus. Hence, it is not surprising that face perception has become a very popular research topic in neuroscience, with ca. 2000 papers published every year. As a result, significant progress has been made in understanding the intricate mechanisms underlying this phenomenon. However, the ontogeny of face perception, in particular the role of innate predispositions, remains largely unexplored at the neural level. Several influential studies in monkeys have suggested that seeing faces is necessary for the development of the face-selective brain domains. At the same time, behavioural experiments with newborn human babies and newly-hatched domestic chicks demonstrate that a spontaneous preference towards faces emerges early in life without pre-existing experience. Moreover, we were recently able to record face-selective neural responses in the brain of young, face-naïve chicks, thus demonstrating the existence of an innate face detection mechanism. In this review, we discuss these seemingly contradictory results and propose potential experimental approaches to resolve some of the open questions.

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“…Nevertheless, it is important to highlight that the phenomenon of face pareidolia occurs specifically for visual patterns with a spurious face-like arrangement, which can be separated from the more general faceness of an object. Any object, such as an apple or a house, could be evaluated on how face-like it is (Bardon et al, 2022), without necessarily creating the perceptual illusion of a face. Finally, our inclusion of the face-deprived monkeys in the current study might seem counterintuitive to the hypothesis we tested.…”

Section: Discussionmentioning

confidence: 99%

When the whole is only the parts: non-holistic object parts predominate face-cell responses to illusory faces

Sharma,

Vinken,

Livingstone

2023

Preprint

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Humans are naturally inclined to perceive faces in everyday objects with a face-like configuration. This illusion, known as face pareidolia, is often attributed to a specialized network of 'face cells' in primates. We found that face cells in macaque inferotemporal cortex responded selectively to pareidolia images, but this selectivity did not require a holistic, face-like configuration, nor did it encode human faceness ratings. Instead, it was driven mostly by isolated object parts that are defined as eyes only within a face-like context. These object parts lack usual characteristics of primate eyes, pointing to the role of lower-level features. Our results suggest that face-cell responses are dominated by local, generic features, unlike primate visual perception, which requires holistic information. These findings caution against interpreting neural activity through the lens of human perception. Doing so could impose human perceptual biases, like seeing faces where none exist, onto our understanding of neural activity.

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Face neurons encode nonsemantic features

Cited by 11 publications

References 37 publications

The neural code for ‘face cells’ is not face specific

The neural code for ‘face cells’ is not face specific

Face detection mechanisms: Nature vs. nurture

When the whole is only the parts: non-holistic object parts predominate face-cell responses to illusory faces

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