# Testing hybrid theories of action-selection

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2022 · $584,390

## Abstract

Project summary
Current theories suggest that mammalian behavior arises from an interaction of different action-
selection systems that process information about the past (memory), present (perception), and future
(goals to achieve, outcomes to avoid) differently. Current theories dichotomize systems into planning
and procedural systems, but newer theories suggest more complex, hybrid algorithms may exist within
mammalian decision-systems. Current views of psychiatry are based on dysfunctions in the information
processing of those decision systems, which means that treating and alleviating those disorders will be
enhanced by a better understanding of the information processing that underlies decision making. 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
conflicts between these decision systems. We will build on our established expertise in neural ensemble
recording and computational analysis to examine the information processing of decision systems,
particularly in questions of conflicts between these systems. Using DREADD manipulation and neural
ensemble recording technologies, we propose to identify the mechanisms and computations that
underlie action-selection processes under exogenously and internally-driven strategy changes.

## Key facts

- **NIH application ID:** 10441653
- **Project number:** 2R01MH112688-06
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** A DAVID REDISH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $584,390
- **Award type:** 2
- **Project period:** 2017-04-01 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10441653

## Citation

> US National Institutes of Health, RePORTER application 10441653, Testing hybrid theories of action-selection (2R01MH112688-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10441653. Licensed CC0.

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