Predictive Information Attribution with Regret Accounting in Sequential Neural Decoding

Neural decoding performance can reflect task structure and behavioral history as well as the current neural observation. We ask how much the observation improves prediction beyond the information available to the decoder. We compare a context predictor $q_0$ with a joint predictor $q_1$ using their log score difference on held-out events. The expected gain satisfies $G=I(Y;X\mid C)+R_0-R_1$, relating conditional information to the difference in predictive regret between the fitted models. Controlled experiments recover three regimes with known ground truth. Persistence produces strong marginal decoding with zero conditional gain. Noisy XOR yields 0.531 conditional bits per sample despite zero marginal information. An underfit context model yields 0.363 fitted bits per sample under a conditional null; capacity matching removes the gain. In auditory attention EEG from 18 participants, supervised previous-trial label feedback reduces gain relative to a prevalence baseline, while a positive versus negative lag contrast remains under interleaved and contiguous splits. For spectral decoding of motor imagery EEG from 40 unseen participants, cue history reduces gain over schedule context by 0.0098 bits per trial. These results show how context and predictor capacity change the interpretation of neural decoding performance.