This project implements a digital behavioral modification program grounded in operant conditioning theory. It is tailored to an individual with ADHD, using real-time behavioral logging, preference-based reinforcement, and adaptive consequence delivery.
This application simulates a 6-week behavior intervention for improving cleanliness-related behaviors using:
- Psychometric Profiling (SPSRQ → RSS/ASQ)
- Top-5 Reinforcer/Punisher Identification
- Weekly Digital Sticker Chart Logging
- Two Phase, Continuous, Fixed and Variable Ratio Scheduling
- Bliss Point (RDH) and Distress Point (PAH) Visualization
- Dynamic Reinforcement Scheduling
📊 Reinforcement Scheduling Logic Continuous Reinforcement (Weeks 1-2): Any positive behavior triggers a reward.
Fixed Ratio (Weeks 3-4): 15 behaviors/week must be completed.
Variable Ratio (Weeks 5-6): Threshold varies randomly between 15–30 behaviors.
Threshold met → Reinforcer unlocked (administered manually) Threshold missed → Punisher administered (if ASQ-based sensitivity)
🧪 Behavioral Theory Integrated Reinforcer Deprivation Hypothesis (RDH): Restricted access increases reward strength.
Bliss Point and Distress Point models included in visualizations.
📦 Output Files Ronda_Montelli_sticker_data.csv — Exported top reinforcers or punishers with behavioral relevance
sticker_log.csv — Log of weekly behavior tracking, stored automatically
target_behaviors.csv — Editable file to customize intervention behaviors and goals
✏️ Authors Marcus C. Rodriguez (Research Design, Implementation)
📚 References De Houwer & Hughes (2020) – The Psychology of Learning
Timberlake & Allison (1974) – Response Deprivation Theory
Kahneman & Tversky (1979) – Prospect Theory
Torrubia et al. (2001) - SPSRQ
| File | Description |
|---|---|
behavior_assessment.py |
Collects SPSRQ, RSS/ASQ data, identifies reinforcer/punisher sensitivity, and exports top 5 most effective punishers/reiniforcers to a CSV. |
sticker_chart.py |
GUI to log weekly behavior and administer imported csv personalized reinforcers/punishers. |
target_behaviors.csv |
Input CSV defining the targeted cleanliness behaviors and desired modified behaviors. |
Create a virtual environment and install:
pip install streamlit pandas numpy matplotlib