# Exploratory Embedding Analysis

This is a compact exploratory analysis of serialized AX-CPT representations. It should not be treated as evidence about latent model states or mechanistic representations.

## Inputs

- `outputs/condition_level_representations.jsonl`: 8 rows.
- `outputs/sliding_window_representations.jsonl`: 4800 rows.

Trial-level representations are not embedded in this first pass.

## Embedding Model

- Model/library: `local_hashing_token_ngram_v1` implemented locally in `scripts/run_embedding_analysis.py`.
- Dependency: `numpy` for vector math, cosine similarity, and PCA.
- Text processing: lowercase alphanumeric tokenization with regex `[a-z0-9_]+`.
- Features: token unigrams and bigrams.
- Vectorization: deterministic signed feature hashing with CRC32 into 256 dimensions.
- Normalization: L2 normalization per row.

These are actual text-derived embedding vectors for the serialized representations. They are not neural embeddings, latent model embeddings, hidden states, logits, probabilities, reaction times, costs, or latency measurements.

## Similarity

Cosine similarity and cosine distance are computed on L2-normalized hashed text embeddings. Summary files report within-condition and between-condition comparisons. Similarities reflect overlap in the serialized representation text and should be interpreted cautiously.

## Projection

2D projections use deterministic PCA via `numpy.linalg.svd` on centered embedding matrices. Component signs are fixed by forcing the largest absolute component loading to be positive.

- Condition-level PCA explained variance ratio: PC1=0.607521, PC2=0.177192
- Sliding-window PCA explained variance ratio: PC1=0.495949, PC2=0.147475

## Outputs

- `condition_embeddings.npz`
- `condition_embedding_vectors.csv`
- `condition_embedding_metadata.csv`
- `condition_embedding_similarity_pairs.csv`
- `condition_embedding_projection_2d.csv`
- `sliding_window_embeddings.npz`
- `sliding_window_embedding_metadata.csv`
- `sliding_window_embedding_similarity_summary.csv`
- `sliding_window_embedding_projection_2d.csv`
- `embedding_model_config.json`