Biology subjects

Abudarham, N.

Publications and source records attributed to Abudarham, N..

5 recordsLinked to original sources

bioRxiv2026

Altering facial preferences with context

Facial features are known to play a key role in how we judge a persons traits. However, in many scenarios people do not evaluate a person in isolation but are required to compare and choose between multiple candidates. Yet, the principles that govern these decisions remain unknown. In economic literature, decisions are known to be influenced by the context of available options, but it is unclear to what extent these principles apply to social decisions about naturalistic stimuli such as human faces. Here, we bridged between these two fields of research - face evaluations and economic decisions - to investigate whether facial preferences can be systematically shifted by context, exhibiting a well-known choice context effect, the "decoy effect". We asked participants to choose the person they think is more trustworthy from either a set of two ("target" and "competitor") or three faces (including a "decoy"). Based on economic theory, we carefully selected face sets using their trustworthiness rankings and perceptual similarity, and tested them across several exploratory samples (n=100-400) and one large, preregistered experiment (n=1099). We found that adding a decoy face to the choice set increased participants choices of the target option by up to 7%, while the decoy face was rarely selected, consistent with a typical decoy effect. These results provide direct evidence that social judgements are affected by the same context-dependent principles known from economic choices, and that the decoy effect can be generalized to complex naturalistic stimuli such as human faces, relevant to various real-world applications. Significance statementPeople often rely on facial judgements when choosing between candidates, like politicians or potential partners. These judgments are typically made in comparison to available alternatives rather than in isolation. In economic literature, decisions are known to depend on the context of alternatives, but it remains unclear whether social evaluations follow the same principles. Here, tested whether the "decoy effect", a well-known choice context effect, applies to social judgments. Participants were asked to choose the more trustworthy individual from either two (decoy-absent) or three faces (decoy-present). Adding a decoy face increased selection of the target face by up to 7%, while the decoy itself was rarely chosen, showing that social judgments are governed by the same context-dependent principles as economic choices.

neuroscience↗

bioRxiv2025

A developmentally inspired computational model of face recognition that learns continuously through generative memory replay

Deep convolutional neural networks (DCNNs) have emerged as powerful models for human face recognition, capturing several hallmark behavioral phenomena such as the face inversion effect and the other-race effect. Yet, key differences remain between how DCNNs and humans, particularly infants, learn to recognize faces. In this study, we present a developmentally-inspired model that addresses three critical gaps between humans and standard DCNNs: (1) DCNNs are trained via one-time batch learning, whereas infants encounter faces gradually over time; (2) DCNNs are trained on thousands of face images with high variability of static images for each identity, while infants are initially exposed to a small number of identities, often limited to close caregivers, through a continuous, dynamic stream of visual input with lower within-identity variability; (3) The goal of DCNNs is to recognize untrained (unfamiliar) faces, while the goal of human face system is to recognize familiar faces. To better approximate developmental face learning, we introduce a model comprising three interacting modules: an Embedder - a DCNN trained to encode face identity continually on up to 10 identities, based on images taken from videos of a TV series; an Autoencoder used for generative memory-replay, to construct images of missing identities in each training phase; and a Memory module - stores identity-specific latent codes. Results show that this model achieves recognition performance comparable to batch-trained DCNNs. We propose this new model as a framework for studying the developmental and mature mechanisms of human face recognition.

neuroscience↗

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↗

bioRxiv2020

Face recognition depends on specialized mechanisms tuned to view-invariant facial features: Insights from deep neural networks optimized for face or object recognition

Faces are processed by specialized neural mechanisms in high-level visual cortex. How does this divergence to a face-specific and an object-general system contribute to face recognition? Recent advances in machine face recognition together with our understanding of how humans recognize faces enable us to address this fundamental question. We hypothesized that face recognition depends on a face-selective system that is tuned to view-invariant facial features, which cannot be accomplished by an object-general system. To test this hypothesis, we used deep convolutional neural networks (DCNNs) that were optimized for face or object recognition. Consistent with the hierarchical architecture of the visual system, results show that a human-like, view-invariant face representation emerges only at higher layers of a face-trained but not the object-trained neural network. Thus, by combining human psychophysics and computational modelling of the visual system, we revealed how the functional organization of the visual cortex may contribute to recognition.

neuroscience↗