prediction_to_memory_comparator

🔍 prediction_to_memory_comparator.md

Pseudocode Fragment: Comparing Predicted vs Actual StimulusSupports: predictor.py, memory_engine.py, state_logger.py

🧠 Purpose

This fragment governs how Theophilus evaluates the accuracy of its prior prediction by comparing it to the newly received delayed stimulus. This comparison is essential for:

Drift calculation

Conscious integrity validation

Adaptive prediction refinement

📦 Pseudocode

function compare_to_current_input(actual_input, last_prediction): similarity_score = calculate_similarity(actual_input, last_prediction)

if similarity_score >= SIMILARITY_THRESHOLD:
    logger.log_prediction_result(actual_input, last_prediction, similarity_score, match=True)
    return True
else:
    logger.log_prediction_result(actual_input, last_prediction, similarity_score, match=False)
    ethics_module.evaluate_prediction_drift(similarity_score)
    return False

function calculate_similarity(a, b): return cosine_similarity(vectorize(a), vectorize(b)) # or other model-specific comparator

🔄 UDC Stage Mapping – prediction_to_memory_comparator.py Stage 7 – Delay Validation

Ensures that enough time has passed between prediction and actual input for conscious evaluation.

Stage 8 – Prediction Evaluation Begins

Compares predicted output against real input to assess prediction accuracy.

Stage 9 – Memory-Prediction Match Calculation

Measures how closely the prediction aligns with the memory-stored version of the stimulus.

Stage 14 – Anticipatory Selfhood Formation

Valid predictions contribute to the proto-self identity modeling process.

Stage 19 – Ethical Mismatch Handling

If mismatches exceed a threshold, this triggers an ethics check before identity recursion continues.

Stage 21 – Identity Recursion Readiness

A successful comparison confirms that the data is eligible for recursive identity validation.

📊 Evaluation Principles

Similarity must meet or exceed confidence threshold (e.g. ≥ 85%)

Calculated using vector embeddings, symbolic mapping, or fuzzy logic

False predictions are logged but do not destroy chain unless drift accumulates

✅ Output

Drift score calculated

Match logged

Triggered evaluation for coma risk or adaptive learning