# A modeling framework and arena for measuring contextual influences of behavior

> **NIH NIH R34** · GEORGIA INSTITUTE OF TECHNOLOGY · 2024 · $321,976

## Abstract

PROJECT SUMMARY
Social behaviors are essential for survival and reproduction. They also evolve quite rapidly and can
vary even among closely related species. Traditionally, social behaviors are very difficult to study
because of the complexity of their input, requiring conspecifics to trigger aggressive, cooperative,
parental, or reproductive behaviors. Additionally, contextual data is important, such as hierarchical
status and environmental factors can also play a role. T his grant will propose to create a
behavioral arena capable of mimicking natural environments that are required for social reproductive
behaviors, including interactions between a large number of conspecifics, environmental factors
such as male displays, and contextual data such as hierarchical status between various males. Tools
will be created to track animals in this arena and build a computational frame work to measure
and compare social behavioral dynamics. This work will utilize Lake Malawi cichlids, a powerful
evolutionary model for identification of genes and neural circuit changes associated with differences
in behavior. This project will generate new tools and datasets for modeling social behaviors, paving the
way for a large-scale R01.

## Key facts

- **NIH application ID:** 10786801
- **Project number:** 1R34DA059510-01
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Eva Dyer
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $321,976
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10786801

## Citation

> US National Institutes of Health, RePORTER application 10786801, A modeling framework and arena for measuring contextual influences of behavior (1R34DA059510-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10786801. Licensed CC0.

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