People over Segments: A Data-Axis Scaling Law for Person-Identity Contrastive PPG Pretraining

Anonymous (ICLR 2027 double-blind submission)

Summary

On a real-world PPG cohort of 114k people and >200 controlled pretraining runs, splits a fixed segment budget into K people × M segments/person for person-identity contrastive SSL. More distinct people is the stronger axis; the people axis reduces its error share ~2× faster per decade than segments; more people also improve demographic prediction. Fitted law yields a budget-adaptive acquisition rule (recruit more people at small budgets, shift to more segments/person as budget grows), validated by training the recommended points.