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 $|\Delta\mathrm{AQ}|$ partition). However, the two permutation controls have notable differences in results: permuting labels over a frozen embedding yields $p=0.001$, whereas retraining the encoder under each permutation yields $p=0.50$. Only the latter tests the label rather than the geometry. Consistent with this, spherical mixture structure and per-class dispersion track identity rather than autism trait differences in dyads; frequency-band and non-oscillatory activity ablation controls do not change the results. However, participant-level model does separate from its identity-aware null ($p=0.0099$) while speaker-versus-listener role analysis performs at chance in the same embedding, indicating a manifold organized by individual - and, in contrast with current neurolinguistics models, almost invariant to speaking vs. listening. Based on these results, we suggest that retraining-based nulls should be the default for grouped-data contrastive embeddings.