3D View Prediction Models of the Dorsal Visual Stream

• Published in: arXiv.org 2023-09
• Main Authors: Sarch, Gabriel, Hsiao-Yu, Fish Tung, Wang, Aria, Prince, Jacob, Tarr, Michael
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Brain; Prediction models; Recurrent neural networks; Three dimensional models
• ISSN: 2331-8422

Deep neural network representations align well with brain activity in the ventral visual stream. However, the primate visual system has a distinct dorsal processing stream with different functional properties. To test if a model trained to perceive 3D scene geometry aligns better with neural responses in dorsal visual areas, we trained a self-supervised geometry-aware recurrent neural network (GRNN) to predict novel camera views using a 3D feature memory. We compared GRNN to self-supervised baseline models that have been shown to align well with ventral regions using the large-scale fMRI Natural Scenes Dataset (NSD). We found that while the baseline models accounted better for ventral brain regions, GRNN accounted for a greater proportion of variance in dorsal brain regions. Our findings demonstrate the potential for using task-relevant models to probe representational differences across visual streams.