# Neural circuit mechanisms of arithmetic for economic decision-making

> **NIH NIH DP2** · NEW YORK UNIVERSITY · 2020 · $2,377,500

## Abstract

Project Summary
 A central challenge in neuroscience is to understand how the connectivity patterns and dynamics of
local and long-range synaptic inputs enable behaviorally-relevant computations in individual neurons. A
fundamental computation that all mammals perform is determining the value of different outcomes, including
their valence, or whether they are perceived as a gain or a loss. Behavioral economics provides a useful
quantitative framework for describing how people and animals subjectively assign value to outcomes, and use
those value estimates to make decisions. Here, we aim to understand the multi-regional neural circuit
mechanisms by which economic variables driving decision-making are computed and represented by neurons
in the brain.
 A hallmark of economic choice behavior is that people exhibit “reference dependence,” wherein they
evaluate outcomes as gains or losses relative to an internal reference point. A related phenomenon, called
“loss aversion,” refers to the observation that most people are more sensitive to losses than to equivalent
gains. This proposal will combine state-of-the-art viral and transgenic approaches for circuit dissection, in vivo
paired recordings of long-range synaptically connected neurons whose responses have been characterized
during behavior, novel techniques for neurochemical sensing, high-throughput behavioral training of rats, and
quantitative behavioral modeling to identify how neural representations of quantifiable cognitive variables -the
reference point and loss aversion- derive from dynamics and patterns of local and long range synapses.
Specifically, the proposed work will delineate the thalamocortical circuitry supporting reference-dependent
computations, determine the circuit mechanisms of arithmetic subtraction of the reference point from value
signals, and identify neuromodulatory systems driving individual variability in loss aversion. The results will
bridge cellular, circuit, and systems-level descriptions of neural mechanisms underlying consequential
economic judgments, while revealing general neural circuit motifs supporting arithmetic computations including
summation, subtraction, and multiplication.

## Key facts

- **NIH application ID:** 10002804
- **Project number:** 1DP2MH126376-01
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Christine M Constantinople
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $2,377,500
- **Award type:** 1
- **Project period:** 2020-09-05 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002804

## Citation

> US National Institutes of Health, RePORTER application 10002804, Neural circuit mechanisms of arithmetic for economic decision-making (1DP2MH126376-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10002804. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*