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Research map / Label-efficient and robust learning / Recognition and long-tail

Can fMRI reveal the representation of syntactic structure in the brain?

NeurIPS 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Dense features and foundation priors · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add syntactic structure features (POS/DEP, ConTreGE) to complexity metrics (ablation)voxelwise encoding model · fMRI Harry Potter readingbrain-activity R2Helped
add ConTreGE Incomp on top of POS/DEP, complexity, punctuation (ablation)voxelwise encoding model · fMRI language ROIsbrain-activity R2Helped
use ConTreGE Comp instead of ConTreGE Incomp (ablation)voxelwise encoding model · fMRI language ROIsbrain-activity R2Hurt
use InConTreGE (top-down partial parses) instead of ConTreGE Incomp (ablation)voxelwise encoding model · fMRI language ROIsbrain-activity R2Hurt
add BERT semantic embeddings on top of syntactic features (ablation)voxelwise encoding model · fMRI language networkbrain-activity R2Helped
use ConTreGE Incomp instead of POS/DEP for high-level ancestorsfeature probing · Level 9 ancestor predictionaccuracy (%): 89.59 -> 90.39Helped
use POS/DEP tags instead of ConTreGE Comp for low-level ancestorsfeature probing · Level 2 ancestor predictionaccuracy (%): 66.76 -> 92.23Helped
graph embeddings vs BERT on encoding semantics (lower is less semantic)ConTreGE · GloVe prediction R2R2: 0.185 -> 0.020Helped

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