The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection
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Abstract (arXiv)
Current language model memory systems store what happened but not how it felt. This distinction -- between semantic memory (knowing about a past event) and episodic memory (re-experiencing it) -- was identified by Tulving as the difference between noetic and autonoetic consciousness. Damasio demonstrated that humans with intact knowledge but absent emotional markers exhibit impaired decision-making. We bridge this gap for language models. Using Gemma 3 1B-IT with pretrained Gemma Scope 2 sparse autoencoders, we identify 310 emotion-exclusive features at layer 22 with psychologically valid geometry. We construct distinctive-feature emotion vectors during experience and partially re-inject them during recall, triggered by context similarity at layer 7. We test four conditions paralleling Damasio's framework: A (no memory), B (semantic labels), C (emotion echo), and BC (semantic + echo). For emotional orientation, the echo alone steepens the threat-safety gradient: the regression slope of threat rating on contextual similarity is 0.80 for C vs 0.56 for A (p=0.011, permutation test). For decisions, the echo amplifies knowledge into action: BC=80% good choices vs B=52% (z=+2.60, p<0.01), while the echo alone has no effect (C=22%, n.s.). The echo changes how the model feels independently, but changes what it does only when combined with knowledge -- replicating Damasio's core finding. The echo amplifies knowledge. It does not replace it.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 9 May 2026.
Builds on
8 of 29 references.
References · 8
- 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
- 2024Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2cited by 394
- 2024Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoderscited by 300
- 2023Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Controlcited by 187
- 2024AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agentscited by 102
- 2024Larimar: Large Language Models with Episodic Memory Controlcited by 53
- 2026Control Reinforcement Learning: Interpretable Token-Level Steering of LLMs via Sparse Autoencoder Featurescited by 2
- 2026A-MBER: Affective Memory Benchmark for Emotion Recognitioncited by 2
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Citing papers per year: 2026: 1.
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- 2025Emotions Where Art Thou: Understanding and Characterizing the Emotional Latent Space of Large Language Models
- 2025Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models
- 2025Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports
- 2026Do Emotion Concepts Generalize Across Sources, Modalities, and Architectures in Vision-Language Models?
- 2026Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds
- 2025Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models