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Contributed Talk Session: Tuesday, August 4, 2:00 – 3:00 pm, Skirball Theater
Poster D in Poster Session D: Wednesday, August 5, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Spatial Structure Facilitates Category Learning through Structured Representations

Michelle B. Hefner¹, Maria Ruz², Christopher Summerfield³; ¹Universidad de Granada, ²University of Granada, ³UK AI Security Institute

Presenter: Michelle B. Hefner

The spatial organization in which objects are learned may influence how they are later categorized. Participants first learned object-location associations in grid configurations with either a structured one-dimensional (1D) layout or an Interdigitated (pseudorandom) layout, and then categorized the same objects in a subsequent task containing no spatial information. Categorization was more accurate following learning in the structured 1D configuration, suggesting that spatial structure facilitates subsequent category learning. To account for this effect, we trained neural networks to learn object embeddings during the object-location association task and subsequently use those embeddings for categorization. In a rich learning regime (small embedding weights), representations preserved spatial structure, resulting in lower loss for 1D relative to Interdigitated conditions. In contrast, a lazy regime (large embedding weights) produced less structured representations and no difference between conditions. These results suggest that spatial structure shapes the geometry of learned representations, facilitating category learning. This framework generates testable predictions for ongoing fMRI work examining whether similar structure is reflected in neural representations during learning.

Topic Area: Memory, Learning & Knowledge Structures