Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.08611 [cs.AI] |
| (or arXiv:2605.08611v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08611 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jared Glover [view email]
[v1]
Sat, 9 May 2026 02:12:39 UTC (596 KB)