Poster Presentation

Contributed Talk Session: Wednesday, August 5, 2:00 – 3:00 pm, Skirball Theater
Poster E in Poster Session E: Thursday, August 6, 10:30 am – 12:15 pm, Kimmel Center, Shorin & Rosenthal Rooms

Lateral Recurrence as a Domain-Selective Mechanism

Amirhossein Farzmahdi¹, Hossein Adeli¹, Wang Boran¹, Chase King¹, Nikolaus Kriegeskorte¹; ¹Columbia University

Presenter: Amirhossein Farzmahdi

Recurrent connections pervade the primate ventral stream, yet their computational role remains debated. One view holds that recurrence supports iterative inference and can stabilize or refine representations across inputs (Rao & Ballard, 1999; Lee & Mumford, 2003); another suggests that experience shapes recurrence to selectively refine behaviorally relevant information. Face-selective regions exhibit refined identity representations across hierarchical stages (Freiwald & Tsao, 2010; Chang & Tsao, 2017), and recurrent architectures better capture primate visual dynamics (Kar et al., 2019; Kietzmann et al., 2019). However, direct evidence for domain-selective recurrence remains limited because anatomy, experience, and task demands covary in cortex. We disentangle these factors using lateral-recurrent convolutional networks with identical architectures trained on different objectives, face identification versus object categorization. We track representational changes across recurrent time through decoding, population geometry, and single-unit analyses. We find a double dissociation: recurrence strengthens identity information in the trained domain while leaving the untrained domain largely unchanged. This selectivity emerges in deeper layers, scales with recurrence strength, and reflects the alignment of category preference to identity sensitivity at the single-unit level. These results argue that lateral recurrence is a learned, task-specific refinement mechanism and generate testable predictions for time-resolved recordings in domain-selective cortex.

Topic Area: Computational Models of Vision & Visual Cortex