brain-to-language-source-attribution

name	brain-to-language-source-attribution
title	What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval
description	Source attribution framework for MEG-to-audio brain decoding that separates decoding performance into structural shortcuts, stimulus-evoked evidence, and contextual aggregation
tags	["brain-decoding","source-attribution","meg","language-decoding","gcb","group-context-bias","neuroscience","computational-neuroscience"]
created	2026-05-26T00:00:00.000Z

What Are We Actually Decoding? Source Attribution for Non-Invasive Brain-to-Language Retrieval

arXiv: 2605.24524 Authors: Xinyu Zhang, Sichao Liu, Runhao Lu, Alexandra Woolgar, Lihui Wang Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC)

Summary

This paper addresses a critical methodological challenge in non-invasive neural language decoding: how to properly attribute decoding performance to neural sources versus confounding factors. The authors recast stimulus-locked MEG-to-audio retrieval as an auditing framework that separates apparent performance into three sources:

1. Structural shortcuts — non-neural nuisances like signal duration that can inflate results
2. Window-level stimulus-locked evidence — genuine neural evidence at the individual time-window level
3. Cross-window contextual aggregation — integration of evidence across multiple windows

Key Contributions

1. Structured Auditing Framework: Separates brain-to-language decoding performance into three distinct sources with diagnostic tests for each

2. Structural Leakage Isolation: Signal-blind Gaussian noise reaches 66.3% Rank@1 under variable-length decoding but collapses to near chance once fixed-duration windows and stimulus-identity splits are enforced — demonstrating that variable-length decoding creates a structural shortcut

3. Group Context Bias (GCB): An inference-time additive logit bias that pools sentence-consistent evidence across windows, making the contextual source measurable:

   • R@1 shifts from 44% to 52% on Gwilliams dataset
   • R@1 shifts from 22% to 29% on MOUS dataset
   • Effect collapses under random-grouping perturbations
   • Vanishes when local evidence is attenuated in MEG or near chance in EEG

4. Oracle Diagnostic: 95.7% of Top-1 errors select the wrong sentence, localising the residual bottleneck to sentence-level competition

Methodology

• Framework: Stimulus-locked MEG-to-audio retrieval with controlled source attribution
• Controls: Fixed-duration windows, stimulus-identity splits, signal-blind baselines
• Intervention: Group Context Bias (GCB) — an auditable score-space intervention
• Validation: Two datasets (Gwilliams, MOUS) with MEG and EEG modalities

Implications

• Brain-to-language decoding performance should be source-attributed, not merely reported
• Variable-length decoding without proper controls overestimates neural evidence
• GCB provides a principled way to measure contextual aggregation effects
• Framework applicable to other neural decoding tasks beyond language

Activation Keywords

brain-to-language-source-attribution, meg-audio-retrieval, structural-shortcut-detection, group-context-bias, neural-decoding-evaluation, source-attribution-framework, brain-decoding-methodology