# Investigating Symbolic Computation in the Brain: Neural Mechanisms of Compositionality > **NIH NIH K99** · ROCKEFELLER UNIVERSITY · 2024 · $134,271 ## Abstract PROJECT SUMMARY/ABSTRACT Animals exhibit a remarkable array of creative, adaptive, and flexible behaviors. Birds and primates repurpose new materials to build nests and tools; rats efficiently construct navigational shortcuts, and humans generalize knowledge of one language to efficiently speak another. This ability to dynamically create novel behavior in one or a few trials often depends on compositional planning, or the ability to generate new combinations of a finite number of simple elements in a goal-directed manner. Despite its central importance for understanding cognition and its disorders, the neural mechanisms of compositionality remain unknown as there is a dearth of experimental frameworks for studying compositional planning. To address this critical need for new approaches, this proposal will elucidate neural mechanisms in a novel drawing task that I have developed in the Freiwald lab, in which macaques draw copies of never-before-seen visual figures. Subjects’ behavior exhibits a key signature of compositionality in the ability to construct novel combinations of previously learned elements to draw new images. I will investigate neural and computational mechanisms for compositional action planning by integrating this behavioral task two other innovations: (1) large-scale recordings in 12 frontal cortical areas, each implicated in cognition but never recorded simultaneously, which will allow me to discover how their distinct functions combine to support cognition (Aim 1), and (2) an integrative analysis framework building and comparing neural network (Aim 2) and symbolic (Aim 3) computational models of compositional planning with behavioral and neural data. I will test the main hypothesis that compositionality depends on neural dynamics implementing symbolic cognitive algorithms in hierarchically organized frontal cortical areas. These studies are expected to discover the first mechanisms, in neural substrates and dynamics, of compositional action planning. Further, because of these studies’ intersectional approach - testing neural network (Aim 2) and symbolic (Aim 3) modeling frameworks on the same data - they may unify these two influential approaches to cognition, which would be a foundational advance for the neuroscience of intelligence. Correspondingly, this study will contribute to understanding cognitive disorders, including frontal planning disorders, and to building brain-machine interfaces that decode cognitive plans from cortical activity. This award will also provide me with crucial training to prepare me for transitioning to independence. I will train in computational modeling - building, empirically testing, and interpreting these models - which will support my use of models to generate and test novel neural circuit and computational hypotheses. I will gain important career development skills in lab management and leadership, scientific communication, and grant writing, which will support my long term goal of establishin... ## Key facts - **NIH application ID:** 10875523 - **Project number:** 5K99NS131585-02 - **Recipient organization:** ROCKEFELLER UNIVERSITY - **Principal Investigator:** Lucas Y. Tian - **Activity code:** K99 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $134,271 - **Award type:** 5 - **Project period:** 2023-09-16 → 2025-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10875523 ## Citation > US National Institutes of Health, RePORTER application 10875523, Investigating Symbolic Computation in the Brain: Neural Mechanisms of Compositionality (5K99NS131585-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10875523. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*