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Contributed Talk Session: Tuesday, August 4, 10:15 – 11:15 am, Skirball Theater
Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

A structured population code for a symbolic action grammar

Lucas Y. Tian¹, Daniel J. Hanuska¹, Kedar Garzón Gupta¹, Yue Liu², Xiao-Jing Wang³, Joshua B. Tenenbaum⁴, Winrich Freiwald¹; ¹Rockefeller University, ²Florida Atlantic University, ³New York University, ⁴Massachusetts Institute of Technology

Presenter: Lucas Y. Tian

A hallmark of intelligence is the ability to flexibly solve problems by reusing, or generalizing, prior knowledge. Compositional generalization—the ability to recombine discrete units of knowledge in a rule-based (or grammatical) manner—is considered essential for solving problems never previously encountered, by allowing access to a combinatorial space of new representations (e.g., drawing a new animal by recombining legs, heads, and arms). Compositionality is broadly implicated, from language and reasoning to communication and action, and is a basis of imagination and symbolic abstraction. Yet, despite this importance, its neural basis remains unclear. Two question are critical: How does the brain (i) represent discrete units of knowledge (symbols), and (ii) implement grammars to recombine those units? To address the first question, a recent study by Tian et al. (2025) established a model system for compositional generalization, combining a compositional drawing task with large-scale neural recordings in macaque monkeys, and identified a neural population encoding discrete, recombinable action categories action symbols in ventral premotor cortex (PMv). Here, we build on this system to address the second question—how does the brain implement action grammars? We trained macaques to use grammatical rules to draw geometric figures, revealing a striking capacity for macaques to generalize action grammars. Using multi-area neuronal recordings and microstimulation, we identified population activity patterns that explicitly code for variables in grammars, specifically in the presupplementary motor area (preSMA). The geometric structure of activity encodes “slot subspaces” that can explain behavioral capacities for generalization. Thus, the grammatical ability to recombine discrete actions into novel sequences reflects a structured population code in preSMA. This finding establishes a foundation for understanding how PMv, preSMA, and interconnected areas recombine symbols in a grammatical fashion to enable compositionality.

Topic Area: Methods, Tools, Theory & Neural Coding