  Integrating information from multiple modalities is arguably one of the
essential prerequisites for grounding artificial intelligence systems with an
understanding of the real world. Recent advances in video transformers that
jointly learn from vision, text, and sound over time have made some progress
toward this goal, but the degree to which these models integrate information
from modalities still remains unclear. In this work, we present a promising
approach for probing a pre-trained multimodal video transformer model by
leveraging neuroscientific evidence of multimodal information processing in the
brain. Using brain recordings of participants watching a popular TV show, we
analyze the effects of multi-modal connections and interactions in a
pre-trained multi-modal video transformer on the alignment with uni- and
multi-modal brain regions. We find evidence that vision enhances masked
prediction performance during language processing, providing support that
cross-modal representations in models can benefit individual modalities.
However, we don't find evidence of brain-relevant information captured by the
joint multi-modal transformer representations beyond that captured by all of
the individual modalities. We finally show that the brain alignment of the
pre-trained joint representation can be improved by fine-tuning using a task
that requires vision-language inferences. Overall, our results paint an
optimistic picture of the ability of multi-modal transformers to integrate
vision and language in partially brain-relevant ways but also show that
improving the brain alignment of these models may require new approaches.
