v1.0.0: Trauma as Bad Training Data - Official Research Release

Release v1.0.0 - Initial Publication

Trauma as Bad Training Data: A Computational Framework for Developmental Psychology

This release accompanies the preprint publication on Zenodo: DOI: 10.5281/zenodo.17573637

Overview

This work proposes a novel computational framework for understanding childhood developmental trauma by reframing it as "bad training data" in a learning system. Drawing parallels between machine learning training failures and developmental psychology, it offers a mechanistic account that removes moral judgment while preserving insight into how adverse childhood experiences shape adult behavior and cognition.

Key Contributions

1. Four-Category Typology

• Direct Negative Experiences: Abuse as high-magnitude negative weights
• Indirect Negative Experiences: Witnessing trauma/noisy signals
• Absent Positive Experiences: Neglect as class imbalance
• Limited Data Diversity: Nuclear family isolation causing overfitting

2. Computational Models

Four PyTorch implementations demonstrating:

• Gradient cascades from extreme penalties
• Behavioral instability from inconsistent feedback
• Overfitting from limited caregiver exposure (p=0.005)
• Optimal retraining strategies balancing memory preservation with new learning

3. Mechanistic Explanations

• Why physical punishment causes behavioral overcorrection
• How inconsistent caregiving produces anxious attachment
• Why nuclear families propagate generational trauma

4. Actionable Interventions

Prevention strategies derived from ML optimization principles, emphasizing structural changes over post-hoc therapeutic treatment

Citation

If you use this research, please cite appropriately [NOTE: The DOI for the repository and paper differ.]

License

This paper is licensed under CC-BY-4.0 and code MIT. You are free to share and adapt with attribution.

Feedback & Contact

This is a preprint. Feedback and discussion are welcome via GitHub Issues.

Email: contact@farzulla.org

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Keywords: developmental trauma · machine learning · computational psychology · neural networks · attachment theory · gradient descent · child development