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Neuroscience

The Positive Experience Principle: Forecasting Conscious Choices with AI Embeddings

Zheng Su, Mingyan Fang

Featured July 28, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

Conscious systems always try to feel better, and this 'better feeling' can be measured by an AI that learns from what people choose, helping to predict their future actions.

In depth
The paper introduces the Positive Experience Principle (PEP), a unifying law stating that conscious systems inherently move towards states of higher positive subjective experience. This tendency is quantified by a Positive Experience Value (PEV), a scalar metric derived from theoretical Universal Consciousness Code (UCC) configurations. Crucially, the authors propose that AI embeddings can serve as practical proxies for UCCs, allowing computational models to learn and predict human choices by optimizing for expected PEV.

Key Takeaways

  • 1
    The paper proposes the Positive Experience Principle (PEP) as a fundamental, predictive law governing the direction of conscious behavior, moving beyond descriptive theories.
  • 2
    It introduces the Positive Experience Value (PEV), a quantifiable scalar metric for subjective experience, theoretically derived from Universal Consciousness Code (UCC) configurations.
  • 3
    The study demonstrates that AI embeddings can act as proxies for UCCs, enabling computational models to learn the PEV function from human choice data and predict future actions.

Conceptual Flow

HIGH LEVEL
1
Methodology: How Conscious Choices Are Predicted

The paper suggests that conscious systems always pick the option that feels best, and an AI can learn to measure this 'best feeling' to guess what people will do.

Possible Actions
Calculate Expected Value
Choose Best Feeling
2
Results: AI Learns to Predict Human Choices

By looking at what people chose, the AI learned to assign a 'positive feeling score' to different experiences, and then used these scores to predict future choices better than guessing.

Human Choices
AI Learns Values
Predict Future Choices

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