Overview
SNM-DDR (Satsam NeuroVisceral Model for Dynamic Decision Readiness) is a real-time computational framework designed to detect, model, and predict human decisions and outcomes before and after observable actions. This documentation outlines the functionality and features of the SNM-DDR system, including its predictive capabilities and user interaction.
Introduction to SNM-DDR 0:00
- SNM-DDR stands for Satsam NeuroVisceral Model for Dynamic Decision Readiness.
- It is a computational framework that predicts human decisions based on brain activity.
- The model operates on a mathematical basis and is currently patent pending.
Understanding Decision Making 1:19
- A decision is defined as a combination of multiple formulas within the system.
- The model analyzes parameters accumulated by the brain to predict actions before they occur, referred to as the ‘zero point’ or ‘Shoonya’.
Prototype Research Simulation 1:44
- The current implementation is a prototype designed for research purposes.
- It showcases both successful predictions and areas where the model may fail, emphasizing the importance of understanding errors as part of the learning process.
Predictive Capabilities 2:32
- The system records various inputs: eye movements, actions, and voice.
- For example, when asked which animal would survive longer in the wild, the system predicted the choice before the user made it, demonstrating its predictive accuracy.
Adjustable Parameters 5:17
- Users can set a minimum confidence level for predictions (e.g., 80%).
- Latency settings can be adjusted to prevent premature predictions based on eye movements before a question is fully presented.
Testing the System 6:12
- Users can attempt to ‘fool’ the system by gazing at one option while selecting another.
- The system’s ability to detect discrepancies between gaze and selection demonstrates its robustness.
Performance Metrics 9:39
- After multiple attempts, the system logs performance metrics such as accuracy and confidence levels.
- For instance, out of four attempts, the system correctly predicted three choices, showcasing its effectiveness.
Comprehensive Functionality 10:26
- SNM-DDR is capable of handling various inputs including vision, audio, movement, and emotional responses.
- It integrates multiple forms of data to enhance decision-making predictions.
Conclusion 12:08
- SNM-DDR effectively captures decision-making processes even before actions are taken.
- Future sessions will explore additional functionalities and improvements to the system.
