ReGraph: A Computational Account of Emergent Generalization in the "What" and "Where" Dual Visual Streams

Where generalization capacity—the ability to extract context-invariant relational structures—first emerges remains a central question in AI and neuroscience. In the brain, the foundation for this capacity lies upstream of the hippocampus, within the entorhinal cortex, where parallel pathways dissociate relational structure in the medial entorhinal cortex (MEC) from sensory content in the lateral entorhinal cortex (LEC). However, as Eichenbaum argued, this structure–content factorization likely originates earlier, driven by the segregation of the dorsal ('where') and ventral ('what') visual streams. Supporting this, grid-like firing patterns—a defining cellular signature of MEC context-invariant representations—also appear in preceding neocortical regions (e.g., retrosplenial cortex) along the dorsal pathway. While these findings suggest that relational structures do not arise *de novo* in the hippocampal formation, how such representations are computationally formed along upstream pathways remains unknown. To investigate this *in silico*, we developed **ReGraph**, a dual visual-stream model implemented on a recurrent graph architecture with several biological inductive biases, including retina-driven stream-specialized encoding, dorsal-to-ventral modulation, and dynamic lateral connectivity. Trained on the action benchmark Something-Something V2, ReGraph revealed a pathway-specific emergence of relational mapping: context-invariant codes and grid-like spatial bases uniquely co-emerged along the extended dorsal stream. In contrast, the absence of these representations in single-stream models, unmodulated dual-stream variants, and standard action-recognition baselines (e.g., VideoMAE, SlowFast) implies that our selected inductive biases are indeed critical prerequisites for the emergence of relational structures. Crucially, the grid-like bases turned out not to be mere byproducts of architectural design, as our post-hoc analyses demonstrated that these bases could serve as reusable routing templates for information processing via lateral connectivity. Together, our findings provide a computational account suggesting that generalization may not be a faculty that emerges abruptly within a dedicated region, but a property that already takes shape as sensory information is parsed into factorized streams of hierarchical visual processing.