Kamila Maria Jozwik

The paper shows that visuo-semantic features explain more variance in higher-level ventral stream neural dynamics than deep neural networks, suggesting complementary roles in object recognition.

The paper investigates five animacy-related object dimensions in brain and behavior using fMRI, EEG, and similarity judgments, finding they explain variance in both brain representations and behavior.

The paper shows that Euclidean distance in a 3D morphable face model predicts human face dissimilarity and identity judgements as well as deep neural networks.

Adding a wiring-length penalty to object-recognition training makes model inferior-temporal layers grow face-selective units, face patches, inter-patch connectivity and viewpoint invariance, but only under naturalistic training input.

This study uses iPSCs from autistic individuals to identify early neurodevelopmental differences during cortical and midbrain differentiation.

The paper evaluates hyperparameter configurations for predicting human brain responses using convolutional neural networks and identifies optimal settings for fMRI and MEG data.

The paper finds that model performance on monkey IT data predicts performance on human IT data, outperforming ImageNet-based predictors.

The paper investigates how brain activity links visual stimuli to behaviour, finding that behaviour-related activity emerges rapidly in the ventral visual pathway and cannot be explained by visual features or semantic categories alone.