# The planning of new compositional action sequences guided by interpretation of ambiguous sensory data in a novel drawing task

> **NIH NIH F32** · ROCKEFELLER UNIVERSITY · 2022 · $74,802

## 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 a
single trial” depends on compositional planning, or mental processes that generate strategies by recombining
previously learned behavioral components. Crucially, this depends on interpreting ambiguous problems (and
associated sensory data) using prior knowledge. 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. In contrast to prior studies of action sequences that are memorized or
externally guided, in this task drawings must be internally generated and depend on cognitive interpretation of
ambiguous sensory data. I will test two central hypotheses: (1) that behavior depends on compositional
planning, based on prior knowledge of actions and sequencing rules, and (2) that frontal cortical activity flexibly
recombines a “library” of trajectories of neural activity corresponding to actions and rules. The first aim will test
the working hypothesis that behavior depends on compositional planning of behavioral programs, or
procedures built from a learned vocabulary of actions (i.e., like strokes for “line” or “arc”) and abstract
sequencing rules (i.e., higher-order procedures, like “repeat”, “connect”). I will apply unsupervised model-fitting
tools to touchscreen and video behavioral data and formally compare alternative models. The second aim is
to identify the dynamic neural representations underlying complex drawings by recording large-scale neural
activity in frontal cortex. I will test the working hypothesis that novel drawings are represented as combinations
of a library of neural activity trajectories encoding actions and sequencing rules. The third aim is to use micro-
stimulation to test the working hypothesis that the causal contribution of neural activity towards planning is
temporally and anatomically specific in a manner that maps onto the latent structure of behavior. I predict that
perturbation of neural trajectories at specific spatio-temporal locations will lead to specific, structured,
behavioral perturbations. The expected outcome is an algorithmic account of how neural activity underlies the
planning of novel complex actions guided by interpretation of ambiguous sensory data. This is significant
because it leads to better understanding of how the brain deploys structured prior knowledge in creative
reasoning and behavior. This research is innovative because it introduces a new behavioral paradigm
focusing on internally-g...

## Key facts

- **NIH application ID:** 10475124
- **Project number:** 5F32MH125573-03
- **Recipient organization:** ROCKEFELLER UNIVERSITY
- **Principal Investigator:** Lucas Y. Tian
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $74,802
- **Award type:** 5
- **Project period:** 2020-09-16 → 2023-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10475124

## Citation

> US National Institutes of Health, RePORTER application 10475124, The planning of new compositional action sequences guided by interpretation of ambiguous sensory data in a novel drawing task (5F32MH125573-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10475124. Licensed CC0.

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