Abstract
A neural vocalization decoder can perform well on randomly held-out observations while depending on regularities that disappear when an entire vocal unit is absent from training. This article develops an evidence framework around structured generalization error rather than a new decoder experiment. A study of zebra finch vocalizations provides the motivating comparison: the apparent ranking of neural representations depends on how the evaluation withholds vocal material. We distinguish interpolation, unseen-unit transfer, session transfer, and translation to human communication as different empirical claims. The proposed evaluation record joins each split to a defined nuisance variation, fits every learned representation within the training boundary, and reports uncertainty at the level of genuinely independent recordings or subjects. Efficient adaptation may make these comparisons affordable, but cannot establish transfer by itself. A useful decoder benchmark should therefore identify the variation that a deployed system must survive before choosing the split on which it will be scored. Keywords: neural decoding; vocalization; structured generalization; latent dynamics; evaluation design; brain-computer interfaces.
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