He adjusted his glasses, twice. Michael found himself excessively focused on the grain of the wooden table. The room was bland, the walls a depressing shade of beige, just like his chances. Across from him, Sarah was speaking eloquently, her words flowing effortlessly, painting a vivid picture of her experience.

He fiddled with his pen, clicking it repeatedly. Each click felt like a condemnation. The interviewer's eyes darted between him and Sarah. Michael felt a sudden urge to flee, to disappear into the anonymity of the hallway.

“And what about you, Michael?” The question snapped him back. He forced a smile, but it felt brittle, likely unnatural. He cleared his throat and started to ramble about his skills.

Emotion: self-conscious

Cluster: Embarrassment
PC1 (Valence): -1.03 Negative
PC2 (Disposition): 0.52

Role in Research

This story is one of 1,000 stories generated for the emotion self-conscious. During extraction, it was fed through Gemma4-31B and its hidden state activations were captured at 11 layers.

The mean activation across all 1,000 self-conscious stories, after denoising with neutral dialogue baselines, produces the self-conscious emotion vector -- a direction in the model's 5,376-dimensional representation space.

Logit Lens (Layer 40)

Tokens promoted/suppressed when the self-conscious vector is projected through the unembedding matrix.

Promoted:
S0.307
C0.298
ness0.273
尴尬0.263
😶0.242
Suppressed:
que-0.399
de-0.266
triumphant-0.266
আনন্দের-0.247
ايا-0.238