Rough AI truth score
75/100
The cases persuasively show neural diversity, compensation, and the importance of environment. They support possibility claims better than frequency or mechanism claims: seven extraordinary lives cannot establish how typical a trajectory is or isolate one causal explanation.
Based on three central claims · medium-high confidence
Adult learning, cross-modal recruitment, and sensory substitution demonstrate genuine compensatory plasticity. Outcomes depend on timing, residual function, training, social support, and task design, but the possibility and clinical importance of compensation are well established.
Functional outcomes vary with environmental fit, rehabilitation, assistive tools, and social context; laboratory deficits do not translate mechanically into everyday incapacity. This supports Sacks's ecological view without implying that context removes underlying impairment.
The cases are genuine evidence and can reveal phenomena hidden by group averages, but they are selected, heterogeneous, and interpreted through narrative. General mechanisms require converging cohorts, experiments, longitudinal work, and replication beyond the people profiled.
What this number means. It is an AI-generated first-pass judgment of three central factual or causal claims—not a rating, exhaustive fact-check, or human peer review. Claim credits are 100% for supported, 75% for mostly supported, 50% for mixed, and 25% for weak, then averaged and rounded. Lower confidence means the score should move more as better evidence arrives.
Method three-central-claims/0.1.0 · checked 2026-09-01 · 3/3 selected claims assessed · method and source audit