---
title: "Discovering and Causally Validating Emotion-Sensitive Neurons"
description: "The authors present this as the first causal, neuron-level account of emotion in LALMs. 53 references, cited by 1 paper here."
authors: ["Xiutian Zhao", "Björn Schuller", "Berrak Sisman"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.687/"
doi: "10.18653/v1/2026.acl-long.687"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2026-discovering-causally-validating-emotion-sensitive/"
---
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# Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models

**Authors:** [Xiutian Zhao](https://md.research.elamur.ai/free/en/authors/xiutian-zhao/), [Björn Schuller](https://md.research.elamur.ai/free/en/authors/bjorn-wolfgang-schuller/) and Berrak Sisman · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.687/) · [PDF](https://aclanthology.org/2026.acl-long.687.pdf) · [DOI 10.18653/v1/2026.acl-long.687](https://doi.org/10.18653/v1/2026.acl-long.687) · [OpenAlex](https://openalex.org/W7119234325)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/zhao-2026-discovering-causally-validating-emotion-sensitive/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks whether large audio-language models (LALMs) store emotion information in small, identifiable groups of neurons, or whether it is spread diffusely across the network. The authors study three open-source models: Qwen2.5-Omni-7B, Kimi-Audio and Audio Flamingo 3. They use speech emotion recognition (SER) on IEMOCAP, MELD and MSP-PODCAST as a diagnostic task.

The method has three steps. First, they record SwiGLU gate activations in the decoder MLPs, using only items the model answers correctly. Second, they score neurons with several selectors: frequency (LAP), entropy (LAPE), mean deviation (MAD) and contrast (ConAct), compared against a random baseline. Third, they intervene at inference time, either by switching the selected neurons off or by amplifying them.

Switching off the neurons tied to one emotion mainly hurts recognition of that emotion and leaves other classes largely intact. Amplifying the same neurons pushes predictions toward that emotion. MAD and ConAct isolate these units far better than LAP or LAPE. The authors present this as the first causal, neuron-level account of emotion in LALMs. It suggests a practical way to control affective behaviour in speech assistants.

## Key points
- **Self–cross effect:** For emotion-sensitive neurons (ESNs) chosen by MAD or ConAct, deactivation causes a strong drop for the target emotion ("self") and much smaller drops for other emotions ("cross"). The effects grow with intervention strength and need only modest identification data.
- **Weaker selectors and baseline:** LAP and LAPE often fail to isolate causally specific units. The random baseline (RND) samples the same neuron budget and is averaged over 5 seeds.
- **Steering:** Targeted amplification reliably shifts predictions toward the chosen emotion. Label-free injection was also tested in three variants (2-Pass, Mix, Union) and gave mixed results, which hints that ESNs compete with each other when boosted jointly.
- **Layer locality:** ESNs cluster in decoder layer 0, layers 6–8 and layers 19–22. They are sparse in the central layers 15–18.
- **Cross-dataset transfer:** ESNs identified on one corpus partly transfer to another, but the effect is uneven and sometimes asymmetric. Neutral transfers least stably, so the authors recommend identifying ESNs on multiple datasets.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Imperial College London](https://md.research.elamur.ai/free/en/universities/imperial-college-london/) — University, London, United Kingdom · 48 works · 12,181 citations

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## Cite

Cite as: Xiutian Zhao, Björn Schuller and Berrak Sisman. 2026. Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2026discovering,
  title = {{Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models}},
  author = {Xiutian Zhao, Björn Schuller and Berrak Sisman},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.687},
  url = {https://doi.org/10.18653/v1/2026.acl-long.687}
}
```

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*Updated 31 October 2026*