发表机构
Stanford University; University of Alabama; University of Southampton（斯坦福大学; 阿拉巴马大学; 南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将CEBRA应用于对话双人组EEG，发现嵌入流形主要编码个体身份而非自闭症特质或说话/听角色，并建议采用重训练式置换检验作为分组对比嵌入的默认验证方法。
AI 中文摘要
对比表示学习越来越多地用于从神经记录中恢复低维结构，但其输出通常通过解码准确率而非其产生的流形几何来验证。我们将CEBRA应用于对话双人组中记录的脑电图，并分析所得嵌入，训练将其约束在二维球面上。描述双人组的标签，包括伙伴间自闭症商数得分的绝对差异，解码效果显著高于偶然水平（对于二元AQ幅度，0.77对比0.55的多数类基线；对于六类$|\Delta$AQ$|$划分，0.44对比0.25）。然而，两种置换控制在结果上存在显著差异：在冻结嵌入上置换标签得到p=0.001，而在每次置换下重新训练编码器得到p=0.50。只有后者测试的是标签而非几何。与此一致，球面混合结构和每类离散度追踪的是身份而非双人组中的自闭症特质差异；频带和非振荡活动消融控制不改变结果。然而，参与者级模型确实与其身份感知的零模型分离（p=0.0099），而说话者与听者角色分析在同一嵌入中表现处于偶然水平，表明流形由个体组织——并且与当前神经语言学模型相反，几乎对说话与听不敏感。基于这些结果，我们建议对于分组数据对比嵌入，基于重新训练的零模型应作为默认方法。
英文摘要
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 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.
