The idea
Each lens is a question, and each question is best answered by a particular blend of networks. The model already knows which networks matter for which question, so it reads the second-by-second networks and resolves them into a single 0–100 read per lens, then tracks how that read rises and falls across the content. No language model is guessing here. The lens is a reduction of the predicted brain response, not an opinion about your script.Which networks drive which lens
Why this is the “why”
Because every lens traces back to specific networks, you can always ask why a number is what it is. A high Attention read with a low Memory read means the focus networks are firing but the reflection network isn’t, so the content grabs but doesn’t stick. A high Manipulation read means emotional and salience networks are doing the heavy lifting while the reasoning network stays quiet. The lenses are deliberately overlapping cuts of one underlying response. That’s a feature: it lets you see the same moment from four angles at once.The 7 networks
The raw signal behind every lens.
The 4 lenses
What each lens analyzes for, with examples.