Useful or Merely Different? Paired-Control Evaluation for Neural Conditioning of Language Models

Language models are typically conditioned on text alone, yet many continuous side signals can alter output distributions without the change itself being useful. We study brain-state conditioning as a control problem and introduce a \textbf{paired-control evaluation} principle: matched brain input should improve prediction over both removal controls (ZERO) and mismatch controls (SHUF), not just deform the next-token distribution. We quantify this distinction between generic perturbation and \textbf{useful deformation} with Jensen-Shannon divergence (JSD) and next-token negative log-likelihood (NLL). We evaluate this principle in two regimes. First, in controlled synthetic TVB experiments, brain conditioning strengthens with dimensionality, but only matched brain states improve NLL under the sample-wise pairing constructed in this synthetic regime. Shuffled and Gaussian-matched signals harm prediction, while freezing the brain pathway removes all effects. Second, in real MEG-MASC, we evaluate a constrained target-word likelihood task with a T5 cross-attention model over post-word MEG windows. Matched MEG improves target-word NLL over both zeroed and shuffled MEG on random, story-blocked, and subject-blocked splits, with $\Delta$NLL gains of roughly $0.08$-$0.11$. Under leave-one-subject-out evaluation, both $\Delta$R-Z and $\Delta$R-S remain below zero for multiple held-out subjects. The real effect is small and token-level, changing the top-1 prediction on about $10\%$ of held-out examples, and should be interpreted as controlled likelihood modulation rather than full neural decoding or improvement of a standard full-context autoregressive LM. Together, these results argue that neural-state conditioning should be judged by paired controls, revealing both a useful synthetic sanity check and a modest but real held-out MEG alignment effect.