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README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+license: cc-by-nc-sa-4.0
+language:
+- en
+tags:
+- neuroscience
+- brain-encoding
+- neuromarketing
+- tribe-v2
+- fmri
+- neural-engagement
+- text-scoring
+- cognitive-science
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-classification
+- feature-extraction
+pretty_name: "NeuroScore: Text Scored by Predicted Neural Engagement"
+---
+
+# NeuroScore: 17,175 Texts Scored by Predicted Neural Engagement
+
+The first public dataset of text content scored by predicted human brain activation using Meta's [TRIBE v2](https://github.com/facebookresearch/tribev2) brain encoding model.
+
+Each text has been processed through TRIBE v2, which predicts fMRI brain responses based on training data from 700+ human subjects. The output is predicted activation across 6 cortical regions of interest (ROIs), a weighted composite engagement score, and an emotion profile classification.
+
+## What's in the dataset
+
+| Field | Description |
+|-------|-------------|
+| `text` | The raw text content |
+| `subject_line` | Email subject line (for cold_email type, null otherwise) |
+| `content_type` | One of: ad_copy, cold_email, sales_pitch, tagline, push_notification, linkedin_message |
+| `industry` | Target industry (for generated texts) or "unknown" (for scraped) |
+| `emotional_strategy` | Intended emotional framing (for generated texts) or "unknown" |
+| `source` | "scraped" (from public datasets) or "generated" (via Anthropic API) |
+| `source_dataset` | Original HuggingFace dataset name (for scraped texts) |
+| `reward_proxy` | Predicted activation: ventromedial PFC / orbitofrontal (desire, value, "I want this") |
+| `attention_conflict` | Predicted activation: anterior cingulate cortex (attention capture, surprise) |
+| `value_judgment` | Predicted activation: frontopolar / medial PFC (trust, credibility, decisions) |
+| `social_semantic` | Predicted activation: temporal pole / STS (social meaning, narrative) |
+| `language_processing` | Predicted activation: Broca's + Wernicke's areas (linguistic engagement) |
+| `insula_activation` | Predicted activation: anterior insula (gut feeling, empathy, visceral response) |
+| `composite_score` | Weighted combination of ROI scores, 0 to 100 scale |
+| `emotion_profile` | Classification based on dominant ROIs (see below) |
+| `brain_activation_mean` | Mean activation across all cortical vertices |
+| `brain_activation_std` | Standard deviation of activation |
+| `brain_activation_max` | Peak activation value |
+| `temperature` | Generation temperature (for AI-generated texts only) |
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("tusharGa/neuroscore")
+df = ds["train"].to_pandas()
+
+# Highest scoring texts
+print(df.nlargest(10, "composite_score")[["text", "composite_score", "emotion_profile"]])
+
+# Average score by content type
+print(df.groupby("content_type")["composite_score"].mean().sort_values(ascending=False))
+
+# Average score by emotional strategy (generated texts only)
+gen = df[df["emotional_strategy"] != "unknown"]
+print(gen.groupby("emotional_strategy")["composite_score"].mean().sort_values(ascending=False))
+```
+
+## How it was built
+
+### Data collection
+
+17,175 texts from two sources:
+
+**Scraped (16,263 texts):** Pulled from public HuggingFace datasets including PeterBrendan/Ads_Creative_Text_Programmatic, marketeam/Marketing-Emails, Yale-LILY/aeslc (Enron email corpus), PeterBrendan/AdImageNet, goendalf666/sales-conversations, RafaM97/marketing_social_media, google-research-datasets/go_emotions, dair-ai/emotion, christinacdl/clickbait_notclickbait_dataset, and others.
+
+**Generated (912 texts):** Produced via the Anthropic API (Claude Sonnet) across a controlled matrix of 6 content types × 10 industries × 8 emotional strategies (fear_urgency, aspiration_reward, social_proof, empathy_pain, curiosity_intrigue, authority_trust, scarcity_exclusivity, humor_relatability).
+
+### Brain scoring
+
+Each text was processed through [TRIBE v2](https://github.com/facebookresearch/tribev2) (Meta FAIR, March 2026), a tri-modal foundation model trained on 700+ subjects' fMRI data.
+
+Texts were scored using the language pathway (LLaMA 3.2 embeddings → transformer integration → cortical surface mapping). Predicted activations were extracted at ~20,484 vertices on the fsaverage5 cortical mesh, then aggregated into ROI scores using the Destrieux atlas parcellation.
+
+Processing was performed on Google Colab (T4 GPU) with batch inference, averaging 3 to 15 seconds per text.
+
+### Composite score
+
+Weighted combination of 6 working ROIs, normalized to 0 to 100:
+
+```
+composite = (0.25 × reward_proxy + 0.25 × attention_conflict + 0.20 × value_judgment 
+           + 0.12 × social_semantic + 0.10 × language_processing + 0.08 × insula_activation) × 100
+```
+
+ROI scores are min-max normalized across the full dataset before weighting.
+
+### Emotion profiles
+
+Classified by the top 2 dominant ROIs per text:
+
+| Profile | Dominant ROIs | Count |
+|---------|--------------|-------|
+| mixed | No clear dominant pair | 8,362 |
+| fear-urgency | attention + insula | 6,022 |
+| high-arousal-desire | attention + reward | 1,521 |
+| curiosity-hook | attention + language | 1,007 |
+| social-aspiration | reward + social | 106 |
+| empathy-based | insula + social | 88 |
+| reward-driven | reward + value | 45 |
+| trust-authority | social + value | 24 |
+
+## Key findings
+
+**Enron emails scored highest.** Internal corporate emails about taxes, accounting, and pay stubs outscored all crafted content. Dense with numbers, carrying real stakes, demanding action. Composite scores up to 92.3.
+
+**Loss framing > gain framing.** "You're losing X" triggered 9.2% more activation than "You could save X." Loss framing activated both attention and reward circuits; gain framing activated reward alone.
+
+**Specificity drives attention.** Texts opening with a number activated 40% more attention-related cortex than texts opening with a question.
+
+**Longer text activates more brain.** Sales pitches (62.0 mean) outscored taglines (43.4) and ad copy (40.4). More context = more simultaneous ROI activation.
+
+**Emotional strategy ranking.** Scarcity (71.7) > curiosity (70.4) > fear (69.7) > humor (68.2) > social proof (67.9) > empathy (66.9) > authority (66.4) > aspiration (65.7).
+
+## Known limitations
+
+**Subcortical regions missing.** TRIBE v2 maps cortical surface only. Amygdala (fear/emotional intensity) and hippocampus (memory encoding) are subcortical and returned all zeros. These were excluded from the dataset. The emotional picture is incomplete.
+
+**Predicted, not measured.** TRIBE v2 predicts group-averaged fMRI responses. Individual brains vary. These are statistical tendencies across a population, not deterministic outcomes.
+
+**Activation ≠ effectiveness.** High neural engagement could mean deep interest or strong aversion. The model does not distinguish valence in all regions.
+
+**Corpus bias.** English-only. Commercially weighted (ads, emails, sales). Do not generalize to other languages, registers, or communication contexts.
+
+**Composite score is opinionated.** The weights reflect a cognitive priority model (attention and reward weighted highest). Other weighting schemes are valid. Raw ROI scores are provided for custom scoring.
+
+## Licensing and attribution
+
+This dataset is released under **CC BY-NC-SA 4.0**.
+
+Brain scores were generated using [TRIBE v2](https://github.com/facebookresearch/tribev2) by Meta FAIR, released under CC BY-NC. Generated texts were produced via the Anthropic API. Scraped texts originate from their respective public datasets on HuggingFace (see source_dataset field for attribution).
+
+If you use this dataset, please cite:
+
+```
+@dataset{neuroscore2026,
+  title={NeuroScore: Text Scored by Predicted Neural Engagement},
+  author={Tushar Gautam},
+  year={2026},
+  url={https://huggingface.co/datasets/tusharGa/neuroscore},
+  note={Brain scores generated using Meta FAIR's TRIBE v2 model}
+}
+```
+
+## Contact
+
+Built by Tushar Gautam, software engineer at Ampirial (Bangalore). Find me on [LinkedIn](https://linkedin.com/in/tushargautam) to discuss or collaborate.

neuroscore_dataset-2.csv

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