# Resolving conflicts between decision-making algorithms

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $420,135

## Abstract

Project summary
Current theories suggest that action-selection in the mammalian brain depends
on an interaction between multiple, neurally-separable algorithms. The
existence of multiple decision-systems opens up novel questions that do not exist
within a unitary decision-maker: What happens when these systems select
conflicting actions? How are those conflicts resolved? A number of disorders
(OCD, eating disorders, drug addiction) and a number of RDOC-related
dysfunctions (compulsivity, habits, and issues of cognitive and “self-” control)
have all been proposed to depend on resolutions of conflicts between these
decision-systems. Recently developed human tasks have proved capable of
putting these decision-systems into conflict for study. We have translated and
validated a rodent version of this new human task. We will build on our
established expertise in neural ensemble recording and computational analysis
to examine how conflicts between these systems is resolved. Using DREADD
manipulation and neural ensemble recording technologies, we propose to identify
the mechanisms and computations that underlie conflict resolution between
these decision-systems.

## Key facts

- **NIH application ID:** 10106479
- **Project number:** 5R01MH112688-05
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** A DAVID REDISH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $420,135
- **Award type:** 5
- **Project period:** 2017-04-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10106479

## Citation

> US National Institutes of Health, RePORTER application 10106479, Resolving conflicts between decision-making algorithms (5R01MH112688-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10106479. Licensed CC0.

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