# How does longer temporal context enhance multimodal narrative video processing in the brain?

arXiv: https://arxiv.org/abs/2602.07570 · HTML: https://mlsanity.com/paper/2602.07570/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 59 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Builds on (most cited first, 17 of 17)

- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [MERLOT: Multimodal Neural Script Knowledge Models](https://mlsanity.com/paper/2106.02636/) (2021)
- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)](https://mlsanity.com/paper/1905.11833/) (2019)
- [InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning](https://mlsanity.com/paper/2205.12673/) (2022)
- [Self-supervised models of audio effectively explain human cortical responses to speech](https://mlsanity.com/paper/2205.14252/) (2022)
- [Brain encoding models based on multimodal transformers can transfer across language and vision](https://mlsanity.com/paper/2305.12248/) (2023)
- [Training language models to summarize narratives improves brain alignment](https://mlsanity.com/paper/2212.10898/) (2022)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [Speech language models lack important brain-relevant semantics](https://mlsanity.com/paper/2311.04664/) (2023)
- [Revealing Vision-Language Integration in the Brain with Multimodal Networks](https://mlsanity.com/paper/2406.14481/) (2024)
- [Vision-Language Integration in Multimodal Video Transformers (Partially) Aligns with the Brain](https://mlsanity.com/paper/2311.07766/) (2023)
- [DATE: Dynamic Absolute Time Enhancement for Long Video Understanding](https://mlsanity.com/paper/2509.09263/) (2025)
- [Multi-modal brain encoding models for multi-modal stimuli](https://mlsanity.com/paper/2505.20027/) (2025)
- [Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)](https://mlsanity.com/paper/2505.20029/) (2025)

## Abstract (arXiv)

Understanding how humans and artificial intelligence systems process complex narrative videos is a fundamental challenge at the intersection of neuroscience and machine learning. This study investigates how the temporal context length of video clips (3--24 s clips) and the narrative-task prompting shape brain-model alignment during naturalistic movie watching. Using fMRI recordings from participants viewing full-length movies, we examine how brain regions sensitive to narrative context dynamically represent information over varying timescales and how these neural patterns align with model-derived features. We find that increasing clip duration substantially improves brain alignment for multimodal large language models (MLLMs), whereas unimodal video models show little to no gain. Further, shorter temporal windows align with perceptual and early language regions, while longer windows preferentially align higher-order integrative regions, mirrored by a layer-to-cortex hierarchy in MLLMs. Finally, experiments with four narrative-task prompts show that they elicit task-specific, region-dependent brain alignment patterns and context-dependent shifts in clip-level tuning in higher-order regions. Our work positions long-form narrative movies as a principled testbed for studying long-timescale temporal integration in long-context MLLMs and its relationship to cortical responses during narrative comprehension.