What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval
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Computer Science > Machine Learning
arXiv:2605.24524 (cs)
[Submitted on 23 May 2026]
Title:What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval
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Abstract:In non-invasive neural language decoding, results can be inflated by sources that are not stimulus-evoked neural evidence: decoder priors, embedding-based metrics, and non-neural structural nuisances such as signal duration. The methodological challenge is therefore attribution: a reported gain is more informative when it can be traced to a specific source. We recast stimulus-locked MEG-to-audio retrieval as an auditing framework that separates apparent performance into three sources - structural shortcuts, window-level stimulus-locked evidence, and cross-window contextual aggregation - and provides a diagnostic for each. Signal-blind Gaussian noise reaches 66.3% Rank@1 (R@1) under variable-length decoding but collapses to near chance once fixed-duration windows and stimulus-identity splits are enforced, isolating structural leakage. Under these controls, fixed-window retrieval recovers measurable MEG-audio discriminability, while an oracle sentence-bucket diagnostic shows that 95.7% of Top-1 errors select the wrong sentence, localising the residual bottleneck to sentence-level competition. We audit this contextual source with Group Context Bias (GCB), an inference-time additive logit bias that pools sentence-consistent evidence across windows while leaving the base retrieval scores and candidate pool fixed. Used as a score-space intervention, GCB makes the contextual source measurable: R@1 shifts from 44% to 52% on Gwilliams and from 22% to 29% on MOUS under the same fixed setting. GCB is auditable under this design: its effect collapses under random-grouping perturbations and vanishes when local evidence is attenuated in MEG or is near chance in EEG, supporting its use as a controlled source-attribution intervention. These results suggest that brain-to-language performance should be source-attributed, not merely reported.
| Comments: | 35 pages, 7 figures, 25 tables |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2605.24524 [cs.LG] |
| (or arXiv:2605.24524v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24524
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval, by Xinyu Zhang and 4 other authors
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