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Found 5 papers, 1 papers with code
Decoding Part-of-Speech from Human EEG Signals
This work explores techniques to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution with electroencephalography (EEG) during text reading.
Its Not a Modality Gap: Characterizing and Addressing the Contrastive Gap
We present evidence that attributes this contrastive gap to low uniformity in CLIP space, resulting in embeddings that occupy only a small portion of the latent space.
What's the Opposite of a Face? Finding Shared Decodable Concepts and their Negations in the Brain
Thus, our contrastive-learning methodology better characterizes new and existing visuo-semantic representations in the brain by leveraging multimodal neural network representations and a novel adaptation of clustering algorithms.
Correcting Biased Centered Kernel Alignment Measures in Biological and Artificial Neural Networks
Using fMRI and MEG data from the THINGS project, we show that if biased CKA is applied to representations of different sizes in the low-data high-dimensionality domain, they are not directly comparable due to biased CKA's sensitivity to differing feature-sample ratios and not stimuli-driven responses.
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models
In the final section of our analysis, we localize shared decodable concepts in the brain using a voxel-masking optimization method to produce a shared decodable concept (SDC) space.
