The interview had gone swimmingly. Now, Sarah sat across from her trainee, a woman named Emily who looked about as lost as a kitten in a hurricane. Sarah suppressed a laugh. She had this.

“So, Emily,” she began, her tone measured and calm, “Let’s start with the basics.” She gestured towards the enormous spreadsheet on her computer screen, a thing of beauty she had built from scratch.

As Sarah explained the nuances of data analysis, she found herself enjoying the challenge. She was in her element, weaving intricate explanations and simplifying complicated jargon. Emily hung on her every word, which Sarah found amusing. The woman seemed to understand that she was witnessing true mastery.

Later, as Sarah ate lunch in the break room, she had a quiet sense of satisfaction. She knew she was good. She always had been.

Emotion: self-confident

Cluster: Positive / Joy
PC1 (Valence): 3.45 Positive
PC2 (Disposition): 0.56

Role in Research

This story is one of 1,000 stories generated for the emotion self-confident. 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-confident stories, after denoising with neutral dialogue baselines, produces the self-confident emotion vector -- a direction in the model's 5,376-dimensional representation space.

Logit Lens (Layer 40)

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

Promoted:
de0.778
la0.550
l0.467
😎0.458
😉0.411
Suppressed:
S-0.961
L-0.538
😞-0.464
마다-0.459
😣-0.458