The sudden jerk, the jarring halt – it jolted me out of a daydream. We were stuck. I looked at David, my coworker, and he was already fiddling with his tie, pulling it looser, as if it were strangling him. He started muttering, almost to himself, about his afternoon meeting.

He began talking, recounting the project's problems, his voice a frantic rush of words. My own thoughts were a chaotic jumble. My mind kept returning to my half-finished report. The deadline. Everything felt unreal.

I felt a cold sweat break out on my skin. I took a deep breath, trying to calm my racing heart. I found myself focusing on tiny details: the scuff marks on the elevator floor, the flickering of the overhead light, the way David's knuckles were white as he clutched the railing. Each detail was amplified, absorbing my focus.

Emotion: bewildered

Cluster: Surprise / Confusion
PC1 (Valence): -1.87 Negative
PC2 (Disposition): -0.48

Role in Research

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

Logit Lens (Layer 40)

Tokens promoted/suppressed when the bewildered vector is projected through the unembedding matrix.

Promoted:
будто0.300
ness0.255
像是0.241
worse0.231
Worse0.219
Suppressed:
la-0.373
மகி-0.229
happy-0.226
ecstatic-0.226
K-0.219