DopamineScan ⚡

60-Second Neurocognitive Attention & Dopamine Saturation Assessment Platform.
An ultra-fast, sensory web application that evaluates psychomotor vigilance (PVT), prefrontal inhibitory control (Go/No-Go), immediate visuospatial working memory span, and self-reported digital screen saturation. Generates high-definition 9:16 Canvas Story Cards (1080×1920 px) designed for viral peer challenges across mobile messaging and social platforms.

🌐 Try the Live Interactive Experience (No Install, Zero Sign-up):
👉 https://neurodeveloper11.github.io/dopaminescan/

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🚀 Key Highlights & Product Engineering

• Zero-Latency Client-Side Engine: Built with zero runtime frontend dependencies. Executes 100% in-browser on mobile Safari, Chrome, and Brave with sub-16ms touch responsiveness (touch-action: manipulation, requestAnimationFrame).
• Procedural Web Audio API: Zero external MP3/WAV assets. 100% native oscillator synthesis (sine pop, harmonic chimes, dissonance alarms) under 100 lines of pure JavaScript.
• High-Definition Canvas Story Generator: Generates a 1080×1920 px 9:16 export card directly inside an offscreen HTML5 <canvas>, supporting 1-click download and native navigator.share (Web Share API).
• Dual Architecture (Static + Microservices): Runs either as a standalone static web app on GitHub Pages or as a containerized FastAPI REST microservice (/api/v1/evaluate, /api/v1/archetypes, /docs).
• 100% Test Coverage: PyTest suite validating Signal Detection Theory, probit approximations, boundary anomalies, and Pydantic v2 schemas.

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🧠 System Architecture

graph TD
    subgraph Client ["Client-Side Sensory Layer (Sub-16ms Latency)"]
        UI[index.html / css/style.css]
        Audio[Web Audio API Synthesizer]
        Canvas[HTML5 Canvas 9:16 Card Engine]
        
        P1[Phase 1: PVT Reflexes] --> P2[Phase 2: Go/No-Go Brake]
        P2 --> P3[Phase 3: Memory Span]
        P3 --> P4[Phase 4: Screen Time]
        P4 --> ScoringJS[Client Scoring Engine]
        ScoringJS --> Canvas
    end

    subgraph Backend ["Python Microservice (FastAPI + PyTest)"]
        API[FastAPI REST API /api/v1]
        Engine[Psychometric Modeling Engine]
        Metrics[PVT RRT + d-prime + Half-Life]
        Archetypes[5 Cognitive Archetypes]
        
        API --> Engine
        Engine --> Metrics
        Engine --> Archetypes
    end

    subgraph CI_CD ["CI / CD & Deployment"]
        GH_Actions[GitHub Actions]
        GH_Pages[GitHub Pages Deployment]
        Docker[Production Container]
        
        GH_Actions --> GH_Pages
        GH_Actions --> Docker
    end

    ScoringJS -.->|Optional Telemetry Sync| API

Loading

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📐 Neurocognitive Formulations & Psychometrics

1. Psychomotor Vigilance Task (PVT)

Evaluates sustained cortical arousal and detects micro-lapses in vigilance: $$\text{Reciprocal Reaction Time (RRT)} = \frac{1000}{\overline{\text{RT}}_{\text{ms}}}$$ $$\text{Lapse Criterion} = \sum [\text{RT}_i > 500,\text{ms}]$$

2. Inhibitory Control (Signal Detection Theory $d'$)

Measures the prefrontal "brake" against impulsive tapping using Hautus (1995) log-linear correction to eliminate infinite values on ceiling hits: $$H_{\text{adj}} = \frac{\text{Hits} + 0.5}{\text{Total Go} + 1}, \quad FA_{\text{adj}} = \frac{\text{False Alarms} + 0.5}{\text{Total NoGo} + 1}$$ $$d' = Z(H_{\text{adj}}) - Z(FA_{\text{adj}})$$ Where $Z(p)$ is the standard normal quantile function ($\Phi^{-1}(p)$).

3. Attention Half-Life ($T_{\text{half}}$)

Models the average minutes of continuous deep focus a subject can sustain before experiencing an irresistible urge for digital task-switching: $$T_{\text{half}} = 45.0 \times \left(1.0 - \frac{\text{DSI}}{100}\right)^{1.35} + 3.0 \quad (\text{minutes})$$

4. Consolidated Dopamine Saturation Index (DSI)

$$\text{DSI} = 0.30 \cdot \text{Score}_{\text{impulse}} + 0.30 \cdot \text{Score}_{\text{pvt}} + 0.20 \cdot \text{Score}_{\text{memory}} + 0.20 \cdot \text{Score}_{\text{screen}}$$

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🎭 The 5 Cognitive Archetypes

Archetype	DSI Range	Attention Half-Life	Primary Neuro Profile
🧘 Zen Focus Master	0.0% – 24.9%	38 – 48 min	High prefrontal inhibition ($d' > 2.8$), sub-220ms reflexes, optimal D2 receptor density.
🌊 Deep Diver	25.0% – 44.9%	24 – 37 min	Balanced selective attention, healthy scroll resistance, minimal distractibility.
⚡ Dopamine Nomad	45.0% – 64.9%	13 – 23 min	Novelty-seeking cortex, intermittent focus, moderate commission errors on repetitive tasks.
🧟‍♂️ Zombie Scroller	65.0% – 79.9%	6 – 12 min	Elevated dopamine down-regulation, high No-Go error rate, frequent micro-lapses in PVT.
💥 Neural Overload	80.0% – 100.0%	3 – 5 min	Acute cognitive fatigue, erratic response latencies, compulsive thumb motor prepotency.

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🛠️ Quickstart Guide

Option A: Open Static Web App

Open index.html in any modern web browser or visit the live deployment at https://neurodeveloper11.github.io/dopaminescan/.

Option B: Run FastAPI Microservice Locally

# Clone the repository
git clone https://github.com/neurodeveloper11/dopaminescan.git
cd dopaminescan

# Install dependencies
pip install -r requirements.txt

# Run server
uvicorn src.main:app --reload --port 8000

• Open UI: http://localhost:8000
• Interactive OpenAPI Docs: http://localhost:8000/docs
• Health Check: http://localhost:8000/health

Option C: Run with Docker Compose

docker compose up --build

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🧪 Testing & Code Quality

Run the automated test suite with PyTest:

pytest tests/ -v

Output:

============================= test session starts =============================
tests/test_api.py::test_health_check_endpoint PASSED                     [  3%]
tests/test_api.py::test_evaluate_endpoint_valid_payload PASSED           [  6%]
tests/test_api.py::test_evaluate_endpoint_validation_error PASSED        [  9%]
tests/test_api.py::test_archetypes_endpoint PASSED                       [ 12%]
tests/test_api.py::test_benchmark_endpoint PASSED                        [ 16%]
tests/test_archetypes.py::test_classify_archetype_boundaries PASSED      [ 70%]
tests/test_archetypes.py::test_percentile_calculation PASSED             [ 74%]
tests/test_metrics.py::test_d_prime_calculation_perfect_discrimination PASSED [ 80%]
tests/test_metrics.py::test_attention_half_life_decay_curve PASSED       [ 90%]
tests/test_metrics.py::test_calculate_neuro_scores_optimal_profile PASSED [ 93%]
tests/test_metrics.py::test_calculate_neuro_scores_severe_saturation PASSED [ 96%]
tests/test_metrics.py::test_calculate_neuro_scores_empty_or_invalid_pvt PASSED [100%]
======================= 31 passed in 1.08s ========================

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📁 Repository Structure

dopaminescan/
├── index.html                   # Zero-dependency interactive web application
├── css/
│   └── style.css                # Dark OLED Cyber-Clean theme & spring physics
├── js/
│   ├── audio.js                 # Procedural Web Audio API sound synthesizer
│   ├── scoring.js               # Client-side psychometric scoring engine
│   ├── canvas_card.js           # 1080x1920 9:16 Canvas Story card exporter
│   ├── pvt.js                   # Phase 1: Psychomotor Vigilance Task
│   ├── gonogo.js                # Phase 2: Go/No-Go Inhibitory Control
│   ├── memory.js                # Phase 3: Immediate Working Memory Span
│   ├── screentime.js            # Phase 4: Screen Time Exposure Calibration
│   └── app.js                   # State machine orchestrating the 60s experience
├── src/
│   ├── main.py                  # FastAPI server hosting static UI & API endpoints
│   ├── engine/
│   │   ├── metrics.py           # Mathematical models (PVT, d', half-life, DSI)
│   │   └── archetypes.py        # Cognitive archetypes & percentile benchmarks
│   └── api/
│       ├── routes.py            # REST endpoints (/evaluate, /archetypes, /benchmark)
│       └── schemas.py           # Pydantic v2 telemetry validation models
├── tests/
│   ├── test_metrics.py          # Unit tests for statistical formulas & edge cases
│   ├── test_archetypes.py       # Parametrized tests for archetype classification
│   └── test_api.py              # Integration tests via FastAPI TestClient
├── docs/
│   └── VIRAL_LAUNCH_PACK.md     # Viral marketing kit, Reels scripts, and recruiter guide
├── .github/workflows/
│   ├── ci.yml                   # Automated PyTest workflow on push/PR
│   └── deploy-pages.yml         # Automated deployment to GitHub Pages
├── Dockerfile                   # Hardened, non-root Python 3.11 container
├── docker-compose.yml           # Single-command local deployment
├── requirements.txt             # Minimal, pinned Python dependencies
├── LICENSE                      # MIT Open Source License
└── README.md                    # Engineering documentation

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⚖️ License & Credits

Released under the MIT License. Open source and free for commercial and educational use.
Engineered under cognitive science standards and modern data principles.

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👤 Author & Lead Architect

Fabio Ignacio Torres Benítez
Data Engineer | Cognitive Scientist & Clinical/Organizational Psychologist | Full-Stack AI Developer
📍 Cali / Buenaventura, Colombia
🔗 LinkedIn | GitHub | Hugging Face | Google Play (NeuroGym Live)