Daniel Janini

This study shows that self-supervised learning in neural networks can replicate human category-selective brain regions' representational signatures.

The paper investigates whether letter perception relies on specialized letter features or general object-based features, finding that general object-based features better account for perceptual similarity.

The paper uses EEG to show that mid-level features in images rapidly activate neural responses for animacy and object size, even when images are unrecognizable.

This study uses MEG to decode symbolic and non-symbolic number stimuli, finding format-specific representations rather than a single abstract number representation.