6/ 💭We draw a metaphor to a dynamic mixture of expert models, reflecting changing neural preferences in task and temporal integration across time, and suggest that an ideal single model would require task-independent training and an architecture enabling dynamic switching.

5/ ⚠️ Overall, our results challenge the conventional view of a temporal processing hierarchy progressing from low- to high-level representations, as typically observed in image perception.

4/ Additionally, we find state space models show superior alignment to intermediate posterior activity through mid-level action features, in which self-supervised pretraining is also beneficial.

3/ In contrast, responses in frontal electrodes best align with high-level static action representations and show no temporal correspondence to the video.

2/ We find responses in posterior electrodes, after initial alignment to hierarchical static object processing, best align to mid-level temporally-integrative representations of actions and closely match the unfolding video content. 🎥

1/ To do so, we propose Cross-Temporal Representational Similarity Analysis (CT-RSA) 📈💡, which matches the best time-unfolded model features to dynamically evolving brain responses, revealing novel insights on how continuous visual input is integrated in the brain.

6/ 💭 We draw a metaphor to a dynamic mixture of expert models, reflecting changing neural preference in task and temporal integration across time, and suggest that an ideal single model would require task-independent training and an architecture enabling dynamic switching.

5/ ⚠️ Overall, our results challenge the conventional view of a temporal processing hierarchy progressing from low- to high-level representations, as typically observed in image perception.

4/ Additionally, we find state space models show superior alignment to intermediate posterior activity through mid-level action features, in which self-supervised pretraining is also beneficial.

3/ In contrast, responses in frontal electrodes best align with high-level static action representations and show no temporal correspondence to the video.

2/ We find responses in posterior electrodes, after initial alignment to hierarchical static object processing, best align to mid-level representations of temporally-integrative actions and closely match the unfolding video content. 🎥

1/ To do so we propose Cross-Temporal Representational Similarity Analysis (CT-RSA) 📈💡, which matches the best time-unfolded model features to dynamically evolving brain responses, revealing novel insights on how continuous visual input is integrated in the brain.