Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)

• URL: http://arxiv.org/abs/2510.06235v1
• Date: Thu, 02 Oct 2025 15:24:16 GMT
• Title: Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
• Authors: Robert Scholz, Kunal Bagga, Christine Ahrends, Carlo Alberto Barbano,
• Abstract summary: We present our submission to the Algonauts 2025 Challenge.<n>The goal is to predict fMRI brain responses to movie stimuli.<n>Our approach integrates multimodal representations from large language models, video encoders, audio models, and vision-language models.
• Score: 1.7266027274320124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present our submission to the Algonauts 2025 Challenge, where the goal is to predict fMRI brain responses to movie stimuli. Our approach integrates multimodal representations from large language models, video encoders, audio models, and vision-language models, combining both off-the-shelf and fine-tuned variants. To improve performance, we enhanced textual inputs with detailed transcripts and summaries, and we explored stimulus-tuning and fine-tuning strategies for language and vision models. Predictions from individual models were combined using stacked regression, yielding solid results. Our submission, under the team name Seinfeld, ranked 10th. We make all code and resources publicly available, contributing to ongoing efforts in developing multimodal encoding models for brain activity.

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