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---
title: Cognitive Proxy
emoji: 🧠
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: 4.44.1
app_file: app.py
pinned: false
license: cc-by-4.0
model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---

# Brain Coordinates for Language Models

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Demo](https://img.shields.io/badge/Demo-Hugging%20Face-yellow)](https://huggingface.co/spaces/ai-nthusiast/cognitive-proxy)
[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org)

**MEG Phase-Locking as a Steering Geometry for LLMs**

**Author**: Sandro Andric

## Overview

We propose using human brain activity not as a score to optimize, but as a **coordinate system** for reading and steering model states. From MEG recordings of 21 subjects listening to naturalistic speech, we construct a brain atlas of Phase-Locking Value (PLV) patterns for 2,113 words and train lightweight adapters that project frozen LLM hidden states into this space.

**Key Results**:
- **Function-Content Axis**: Dominant axis (61% variance) separating syntactic binding from semantic access
- **Cross-Architecture Transfer**: GPT-2 (d=1.59) and TinyLlama (d=1.40), both p < 10^-22
- **Bidirectional Steering**: Control generation along brain-derived axes (p < 0.0001)
- **Scale-Dependent Structure**: Agency axis transfers to larger model only (d=-0.82)

---

## 1. Installation

Requires Python 3.9+ and PyTorch.

```bash
# Install dependencies
pip install torch transformers scikit-learn pandas scipy numpy streamlit plotly sentencepiece

# Ensure local modules are Importable
export PYTHONPATH=$PYTHONPATH:$(pwd)/src
```

## 2. Reproduction Pipeline

To reproduce the scientific results from scratch, execute the following steps in order.

### Step 1: Build the Cognitive Atlas
Constructs the "Brain Dictionary" from the MEG-MASC dataset.
*   **Input**: MEG-MASC BIDS data (configured in `DATA_ROOT`).
*   **Output**: `results/final_atlas_256.pkl` (and `_vocab.pkl`).

```bash
python experiments/build_clustered_atlas.py
```

### Step 2: Interpret the Axis (Phase 10.1)
Analyzes the semantics of the discovered brain clusters.
*   **Output**: Correlation stats showing Cluster A = Function, Cluster B = Content.

```bash
python experiments/analyze_axis_correlations.py \
  --pos-cluster Cluster_2 \
  --neg-cluster Cluster_3
```

### Step 3: Train the Adapter
Trains the MLP mapping `GPT-2 Hidden -> Brain PLV`.
*   **Input**: GPT-2 Tokenizer + Atlas.
*   **Output**: `results/gpt2_adapter.pt`.

```bash
python experiments/train_gpt2_adapter.py
```

### Step 4: Validate the Alignment (Phase 10.2)
Performs the rigorous T-Test on held-out words.
*   **Metric**: Cohen's d > 0.9 expected for Function vs Concrete.

```bash
python experiments/validate_adapter_stats.py \
  --pos-cluster Cluster_2 \
  --neg-cluster Cluster_3
```

### Step 5: Systematic Steering (Phase 10.3)
Generates text under "Neuro-Steering" conditions to measure causal effect.

```bash
python experiments/evaluate_steering_batch.py \
  --pos-cluster Cluster_2 \
  --neg-cluster Cluster_3 \
  --alpha 50.0
```

---

## 3. Interactive Demo (Cognitive Proxy)

**Try it online**: [huggingface.co/spaces/ai-nthusiast/cognitive-proxy](https://huggingface.co/spaces/ai-nthusiast/cognitive-proxy)

Or run locally:

```bash
streamlit run src/ui/app_tinyllama_minimal.py
```

Features:
- **Compare**: See three generation variants side-by-side (semantic, baseline, syntactic)
- **Inspect**: Analyze text projection onto brain coordinate space with PLV visualization
- **Steer**: Manually control generation along the Function-Content axis

---

## 4. Directory Structure

*   `src/`: Core libraries (`models`, `data`, `ui`).
*   `experiments/`: Scientific scripts (Training, Validation).
*   `results/`: Trained models (`.pt`) and atlases (`.pkl`).
*   `artifacts/`: Project history, papers, and walkthroughs.