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arXiv Computation and Language By Xiutian Zhao, Luqi Sun, Bj\"orn Schuller, Berrak Sisman

Concentrated Causal Influence, Distributed Diagnostic Information: Cognitive Impairment Decisions in Large Audio-Language Models

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Large audio‑language models can detect cognitive impairment from speech, but it was unclear which internal components drive this decision or how much impairment information is stored in their representations. The study identifies class‑contrastive activation neurons (CICNs) in decoder MLPs and shows that ablating small CICN sets causes significant, class‑directed prediction shifts in several models, while CICN activations can be linearly read out to achieve high AUC scores. These findings demonstrate that a concentrated set of neurons exerts causal control over CI decisions, whereas diagnostic information is broadly distributed across the model’s representations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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