Contrastive Neural Embeddings Reveal Individual Traits Beyond Conversational Role
Contrastive representation learning is increasingly used to recover low-dimensional structure from neural recordings, but its output is typically validated by decoding accuracy rather than by the geometry of the manifold it produces. We apply CEBRA to EEG recorded from dyads in conversation, and analyze the resulting embedding, which training constrains to the 2D sphere. Labels describing the dyads, including the absolute difference between partners' autism-quotient scores, decode well above chance (0.77 against a 0.55 majority baseline for binary AQ magnitude; 0.44 against 0.25 for the six-class $|Δ$AQ$|$ partition). However, the two permutation controls have notable differences in resul...
| Authors | Hubert Huang, Michelle McCleod, Brendan Ames et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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