Galit Yovel

The paper uses deep learning models to show that the superior temporal sulcus represents naturalistic social interactions through multimodal integration of visual and auditory information.

This study investigates whether facial preferences can be influenced by context, finding that adding a decoy face increases selection of a target face by up to 7%.

This paper introduces a developmentally inspired model for face recognition that learns continuously through generative memory replay, achieving performance comparable to batch-trained DCNNs.

The paper investigates the functional role of visual prefrontal cortex activations by comparing their relational structure to text and visual DNNs, finding text DNNs better predict these activations.

This study shows that increasing face image diversity during training improves deep networks' view invariance, sensitivity to critical facial features, and classification performance.

This paper compares self-supervised and supervised models in predicting neural responses to images, finding self-supervision better for fMRI and MEG, with language supervision explaining later stages and image-only supervision capturing early and long latencies.

The paper tests whether high-level visual cortex aligns with visual or abstract text by comparing neural responses to images and their textual descriptions.

This study uses deep learning models to show that domain-specific computations are better for fine-grained classification than domain-general ones.

This study shows that combining visual and semantic information improves predictions of human face recognition in memory, suggesting a new visual-semantic representation in human perception.

This study shows that category-selectivity in visual cortex can estimate normalization pool heterogeneity, influencing responses to complex visual stimuli.

This study investigates face processing in the human brain using fMRI, finding that the pSTS-FA is more responsive to dynamic faces and changeable aspects, while FFA and OFA show similar responses across conditions.