tribe-fmri-encoding-validation

name	tribe-fmri-encoding-validation
description	Validation methodology for deep multimodal brain-encoding models (TRIBE, the 2025 Algonauts challenge winner) against behavioral engagement metrics. Tests whether predicted fMRI signals forecast aggregate population behavior (YouTube replay heatmaps, neuroforecasting). Shows predicted neural drive fails to predict re-watch despite high encoding accuracy — null result with Bayes factor bounds and equivalence tests. Activation: brain encoding validation, TRIBE, neuroforecasting, predicted fMRI, fMRI engagement, brain encoding behavioral prediction, Algonauts challenge, fMRI replay heatmap, global field power fMRI, YouTube replay
metadata	{"arxiv_id":"2607.01400","published":"2026-07-01","authors":"Barada Sahu, Shivesh Pandey","tags":["brain-encoding","TRIBE","neuroforecasting","fMRI","behavioral-prediction","validation"]}

TRIBE fMRI Encoding Validation

Deep multimodal brain-encoding models (TRIBE: Llama-3.2 + V-JEPA2 + Wav2Vec-BERT) predict fMRI responses to naturalistic video with high accuracy. This methodology tests whether predicted neural signals inherit the behavioral predictive power of measured neural signals (neuroforecasting).

Key Finding: Null Result with Strong Bounds

Running TRIBE on 48 YouTube videos, reduced predicted cortical response to per-second engagement curve (global field power). Correlated against "most replayed" heatmap:

• Pooled position-controlled partial correlation: r = +0.058 (95% CI [-0.04, 0.15]; p = 0.23)
• Not significantly above loudness/motion baselines (loudness r = 0.12; paired p = 0.42)
• Bayes Factor: BF₀₁ = 3.2 (moderate evidence for null)
• Equivalence test excludes effects above r = 0.14
• Split-half reliability of target: 0.82 (ceiling r = 0.90) — rules out noisy-label artifact