Training Data and the Maladaptive Mind

Traditional trauma theory frames adverse childhood experiences as damaging events requiring healing. We propose a computational reframing: trauma represents maladaptive learned patterns arising from suboptimal training environments, functionally equivalent to problems observed in machine learning systems trained on poor-quality data. This framework identifies four categories of developmental "training data problems": direct negative experiences, indirect negative experiences (noisy signals), absence of positive experiences, and limited exposure. We extend the framework to model dissociation as meta-learned protective suppression—second-order learning where the system learns that cognitive engagement itself predicts overwhelm. This provides mechanistic grounding for distinguishing PTSD (catastrophic single-event learning) from CPTSD (chronic adversarial training), generating testable predictions for differential diagnosis and treatment. Computational validation demonstrates that extreme penalties produce overcorrection and weight cascades in neural networks, while limited caregiver diversity produces overfitting to restricted training distributions. The framework serves two audiences: for researchers, it provides mechanistic precision and cross-domain integration; for individuals understanding their experiences, it offers liberation from identity-attachment to trauma—"I learned patterns from adverse conditions" differs fundamentally from "I am broken." This reframing removes emotional defensiveness, suggests tractable interventions including increased caregiver diversity and community-based child-rearing, and makes prevention more tractable than post-hoc therapeutic intervention.