Biology subjects

Grosbard, I.

Publications and source records attributed to Grosbard, I..

3 recordsLinked to original sources

bioRxiv2024

Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms

Visual experience is known to play a critical role in face recognition. This experience is believed to enable the formation of a view-invariant representation, by learning which features are critical for face identification across views. Discovering these critical features and the type of experience that is needed to uncover them is challenging. We have recently revealed a subset of facial features that are critical for human face recognition. We further revealed that deep convolutional neural networks (DCNNs) that are trained on face classification, but not on object categorization, are sensitive to these facial features, highlighting the importance of experience with faces for the system to reveal these critical features. These findings enable us now to ask what type of experience with faces is required for the network to become sensitive to these human-like critical features and whether it is associated with the formation of a view-invariant representation and face classification performance. To that end, we systematically manipulated the number of within-identity and between-identity face images and examined its effect on the network performance on face classification, view-invariant representation, and sensitivity to human-like critical facial features. Results show that increasing the number of images per identity as well as the number of identities were both required for the simultaneous development of a view-invariant representation, sensitivity to human-like critical features, and successful identity classification. The concurrent emergence of sensitivity to critical features, view invariance and classification performance through experience implies that they depend on similar features. Overall, we show how systematic manipulation of the training diet of DCNNs can shed light on the role of experience on the generation of human-like representations.

neuroscience↗

bioRxiv2022

Deep learning models of perceptual expertise support a domain-specific account

Perceptual expertise is an acquired skill that enables fine discrimination of members of a homogenous category. The question of whether perceptual expertise is mediated by general-expert or domain-specific processing mechanisms has been hotly debated for decades in human behavioral and neuroimaging studies. To decide between these two hypotheses, most studies examined whether expertise for different domains is mediated by the same mechanisms used for faces, for which most humans are expert. Here we used deep convolutional neural networks (DCNNs) to test whether perceptual expertise is best achieved by computations that are optimized for face or object classification. We re-trained a face-trained and an object-trained DCNNs to classify birds at the sub-ordinate or individual-level of categorization. The face-trained DCNN required deeper retraining to achieve the same level of performance for bird classification as an object-trained DCNN. These findings indicate that classification at the subordinate- or individual-level of categorization does not transfer well between domains. Thus, fine-grained classification is best achieved by using domain-specific rather than domain-general computations.

neuroscience↗

bioRxiv2022

Deep learning algorithms reveal a new visual-semantic representation of familiar faces in human perception and memory

Recent studies show significant similarities between the representations humans and deep neural networks (DNNs) generate for faces. However, two critical aspects of human face recognition are overlooked by these networks. First, human face recognition is mostly concerned with familiar faces, which are encoded by visual and semantic information, while current DNNs solely rely on visual information. Second, humans represent familiar faces in memory, but representational similarities with DNNs were only investigated for human perception. To address this gap, we combined visual (VGG-16), visual-semantic (CLIP), and natural language processing (NLP) DNNs to predict human representations of familiar faces in perception and memory. The visual-semantic network substantially improved predictions beyond the visual network, revealing a new visual-semantic representation in human perception and memory. The NLP network further improved predictions of human representations in memory. Thus, a complete account of human face recognition should go beyond vision and incorporate visual-semantic, and semantic representations.

neuroscience↗