> ## Documentation Index
> Fetch the complete documentation index at: https://api.thesapientcompany.com/llms.txt
> Use this file to discover all available pages before exploring further.

# From networks to lenses

> How the per-second brain networks translate into the four lenses.

The seven networks are the raw signal. The four lenses are the answers. This page explains the translation: how a per-second pattern of brain activity becomes a plain statement like "this holds attention" or "this drives desire."

## 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

| Lens              | Reads most from                              | The question it answers                |
| ----------------- | -------------------------------------------- | -------------------------------------- |
| **Attention**     | Dorsal Attention, Ventral Attention          | Is the viewer locked in?               |
| **Memory**        | Default Mode                                 | Will this be remembered and absorbed?  |
| **Buy / Not Buy** | Limbic (reward)                              | Will they want it?                     |
| **Manipulation**  | Limbic, Ventral Attention vs. Frontoparietal | Is it pressuring more than persuading? |

## 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.

<Columns cols={2}>
  <Card title="The 7 networks" href="/knowledge-base/the-7-networks">
    The raw signal behind every lens.
  </Card>

  <Card title="The 4 lenses" href="/knowledge-base/lenses">
    What each lens analyzes for, with examples.
  </Card>
</Columns>