A global predicted-fMRI drive signal from TRIBE does not predict YouTube replay heatmaps
Paper • 2607.01400 • Published
video_id stringlengths 11 11 | youtube_url stringlengths 43 43 | category stringlengths 1 9 | duration_s float64 | analyzed_s float64 60 60 ⌀ | n_timesteps float64 46 60 ⌀ | raw_pearson_r float64 -0.6 0.73 ⌀ | partial_r_position_controlled float64 -0.68 0.63 ⌀ | baseline_loudness_partial_r float64 -0.61 0.43 ⌀ | baseline_motion_partial_r float64 -0.5 0.32 ⌀ |
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TRIBE Replay Heatmaps
Video-ID manifest and per-video results for the paper A global predicted-fMRI drive signal from TRIBE does not predict YouTube replay heatmaps.
Paper: https://arxiv.org/abs/2607.01400
Code: https://github.com/mercurialsolo/tribe-replay-heatmaps
Summary
We run TRIBE, the winning model of the Algonauts 2025 brain-encoding challenge combining Llama-3.2, V-JEPA2 and Wav2Vec-BERT, on 48 YouTube videos. We reduce its predicted cortical response to a per-second global field power engagement curve and test whether that curve predicts the crowd-sourced most-replayed heatmap of each video. The position-controlled partial correlation is +0.058, 95% CI -0.04 to 0.15, t=1.21 at 47 degrees of freedom, p=0.23. It is indistinguishable from zero and not above loudness or motion baselines.
Files
video_ids.txt: the 50 YouTube video IDs used in the study, one per line.
videos.csv: per-video table with columns id, youtube_url, category, duration_s, analyzed_s, n_timesteps, raw_pearson_r, partial_r_position_controlled, baseline_loudness_partial_r, baseline_motion_partial_r.
Data policy
We do not redistribute video files or fMRI data. Videos are identified only by their YouTube ID. The most-replayed heatmaps are public YouTube metadata fetched per ID. Everything needed to reproduce the analysis is derivable from the IDs plus the code.
Citation
Please cite the paper at https://arxiv.org/abs/2607.01400 and the TRIBE model.
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