What Does Comparing Supervision Targets Identify? Separating Supervised Vectors from Candidate Weighting

When one supervision target outperforms another, what does the comparison establish about its supervised vectors? In sample-coupled objectives, changing a target can also change the candidate weights governing its learning updates. We test the adoption consequence of separating these roles: keep paired and candidate vectors fixed and replace their weighting input with reference supervision. In controlled electroencephalography (EEG)-to-visual learning, this substitution turns a coordinate-rescaled target from harmful to beneficial on a cross-image concept-ranking task with model-specific neural templates. A control matching actual AdamW step lengths while retaining its own image-parameter update direction remains harmful. A prospective five-crop test also reproduces a natural target's preference for a reference-informed temperature projection with matched update lengths and positive adoption in the same setting. These interventions retain reference information in weighting; they establish task benefits conditional on that treatment. A negative ordinary comparison therefore need not justify discarding the supervised vectors: their adoption verdict can reverse against the same reference after replacing only the weighting input. Target comparisons support configuration selection, while content attribution requires evidence separating these roles.