Face identity coding in the deep neural network and primate brain

Wang, Jinge; Cao, Runnan; Brandmeir, Nicholas; Li, Xin; Wang, Shuo

doi:10.1038/s42003-022-03557-9

Cited by 12 publications

(22 citation statements)

References 61 publications

(77 reference statements)

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“…As the use of ANNs in social neuroscience matures, we expect to see an even broader range of phenomena explored using this method ( Figure 2C ). To the extent that ANNs faithfully represent the functional principles of brain activity, they may serve as test beds for in silico experiments that could not be carried out on biological brains ( Wang et al ., 2022 ; Jain et al ., 2023 ).…”

Section: Applications Of Anns In Social Neurosciencementioning

confidence: 99%

Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

Sievers,

Thornton

2024

Social Cognitive and Affective Neuroscience

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This review offers an accessible primer to social neuroscientists interested in neural networks. It begins by providing an overview of key concepts in deep learning. It then discusses three ways neural networks can be useful to social neuroscientists: i) building statistical models to predict behavior from brain activity; ii) quantifying naturalistic stimuli and social interactions; and iii) generating cognitive models of social brain function. These applications have the potential to enhance the clinical value of neuroimaging and improve the generalizability of social neuroscience research. We also discuss the significant practical challenges, theoretical limitations, and ethical issues faced by deep learning. If the field can successfully navigate these hazards, we believe that artificial neural networks may prove indispensable for the next stage of the field’s development: deep social neuroscience.

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Section: Applications Of Anns In Social Neurosciencementioning

confidence: 99%

Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

Sievers,

Thornton

2024

Social Cognitive and Affective Neuroscience

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“…Recent studies in humans have also shown hippocampal neurons that encode faces based on visual similarity but not on concept associations, showing region‐based feature coding (Cao et al, 2022; Wang et al, 2022). These studies may help to reconcile the discrepancy in pattern separation reported by previous studies (Leal & Yassa, 2018; Quian Quiroga, 2020, 2023).…”

Section: Examples Of Multi‐task Research Designsmentioning

confidence: 99%

Using multi‐task experiments to test principles of hippocampal function

et al. 2023

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Investigations of hippocampal functions have revealed a dizzying array of findings, from lesion‐based behavioral deficits, to a diverse range of characterized neural activations, to computational models of putative functionality. Across these findings, there remains an ongoing debate about the core function of the hippocampus and the generality of its representation. Researchers have debated whether the hippocampus's primary role relates to the representation of space, the neural basis of (episodic) memory, or some more general computation that generalizes across various cognitive domains. Within these different perspectives, there is much debate about the nature of feature encodings. Here, we suggest that in order to evaluate hippocampal responses—investigating, for example, whether neuronal representations are narrowly targeted to particular tasks or if they subserve domain‐general purposes—a promising research strategy may be the use of multi‐task experiments, or more generally switching between multiple task contexts while recording from the same neurons in a given session. We argue that this strategy—when combined with explicitly defined theoretical motivations that guide experiment design—could be a fruitful approach to better understand how hippocampal representations support different behaviors. In doing so, we briefly review key open questions in the field, as exemplified by articles in this special issue, as well as previous work using multi‐task experiments, and extrapolate to consider how this strategy could be further applied to probe fundamental questions about hippocampal function.

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“…Moreover, the feature space in the deep layer of DCNNs serves as a useful model for simulating neural processes in the medial temporal lobe of the human brain. For example, researchers have demonstrated that features extracted from natural face images using DCNNs can be used to observe the features encoded by neuronal activity in the medial temporal lobe utilized in face identity recognition ( 20 ). On the other hand, generative computational models can be utilized to manipulate the process of face personality trait judgment in humans ( 21, 22 ).…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network with face identity recognition experience exhibits brain-like neural representations of personality traits

Li,

Jiang

2024

Preprint

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Faces contain both identity and personality trait information. Previous studies have found that convolutional neural networks trained for face identity recognition spontaneously generate personality trait information. However, the successful classification of different personality traits does not necessarily mean that convolutional neural networks adopt brain-like representation mechanisms to achieve the same computational goals. Our study found that convolutional neural network with visual experience in face identity recognition (VGG-face) exhibited brain-like neural representations of personality traits, including coupling effects and confusion effects, while convolutional neural networks with the same network architecture but lacked visual experience for face identity recognition (VGG-16 and VGG-untrained) did not exhibit brain-like effects. In addition, compared to the VGG-16 and the VGG-untrained, the VGG-face exhibited higher similarity in neural representations with the human brain across all individual personality traits. In summary, these findings revealed the necessity of visual experience in face identity recognition for developing face personality traits judgment.

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Face identity coding in the deep neural network and primate brain

Cited by 12 publications

References 61 publications

Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain

Using multi‐task experiments to test principles of hippocampal function

Deep convolutional neural network with face identity recognition experience exhibits brain-like neural representations of personality traits

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