BrainCause recovers known visual localizations and finds new candidate representations by validating causal specificity via counterfactual stimuli and encoding models, showing activation alone produces many false positives.
Kay, Shinji Nishimoto, and Jack L
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
The authors propose target-space recovery profiles to diagnose which reproducible dimensions of fMRI brain responses are captured by model predictions, showing that accuracy alone can mask alignment mismatches in visual cortex.
Fine-tuning language encoding models on fMRI responses improves prediction performance for ECoG brain signals in frequency bands beyond fMRI resolution.
citing papers explorer
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From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain
BrainCause recovers known visual localizations and finds new candidate representations by validating causal specificity via counterfactual stimuli and encoding models, showing activation alone produces many false positives.
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Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment
The authors propose target-space recovery profiles to diagnose which reproducible dimensions of fMRI brain responses are captured by model predictions, showing that accuracy alone can mask alignment mismatches in visual cortex.
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Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
Fine-tuning language encoding models on fMRI responses improves prediction performance for ECoG brain signals in frequency bands beyond fMRI resolution.