# Model-based credit assignment

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $384,992

## Abstract

Abstract
How the brain forms, tunes, and uses predictive models that specify the causal links between stimuli in the
environment, our choices, and their outcomes is a fundamental question in Psychology and Neuroscience. A
great deal of progress has been made identifying the neural computations theorized to form and update
predictive models. This research has played a central role in the rise of computational psychiatry, but its
relevance to clinical disorders has been limited in part by the use of relatively simple learning/choice paradigms
that capture only a narrow subset of the structural complexity of real-world learning.
 In order to make sound predictions in a complex world, the brain needs to attribute good and bad
outcomes to their most likely causes, a problem known as “credit assignment”. There is little understanding of
how outcomes are attributed to their most likely causes in structured real-world environments. Most real-world
learning occurs in complex and structured environments, such as hierarchical systems (e.g. seasonal events,
social hierarchies, contextual rules, etc.). Recent evidence suggests that humans can use an understanding of
the environment’s causal structure to attribute outcomes to their most likely causes (which I call “model-based
credit assignment)”, rather than simply attributing them to the most recently experienced stimuli and choices that
were made (which I call “model-free” credit assignment), as standard models have proposed.
 The purpose of the present proposal is to develop the first neural model of model-based credit assignment.
We hypothesize that the brain reinstates the cause when a reinforcement outcome is experienced to associate
with the outcome. In other words, so that “fire-together/wire-together” plasticity mechanisms can link a choice
with an outcome, the choice representation and the outcome representation must both be active at the same
time even though the causal choice or event may have actually occurred some time beforehand.
 To test this and other predictions, we will integrate mathematical descriptions of learning and decision
making with “representational” analysis methods that allow inferences to be made about the information
represented in brain areas, applied to fMRI and scalp EEG data. fMRI will reveal how neural learning signals
update neural representations of likely causes during learning, while EEG will reveal the timing of the
hypothesized reinstatement. These experiments will set the stage to apply the insights gained to investigate how
the brain attributes outcomes to more abstract “latent” causes in hierarchically structured environments prevalent
in the real world. The proposed project will thus move this general program of research strategy toward learning
tasks that better reflect the complexity and structure in many real-world learning/choice situations important for
both typical and atypical individuals.

## Key facts

- **NIH application ID:** 10083230
- **Project number:** 5R56MH119116-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Erie D Boorman
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $384,992
- **Award type:** 5
- **Project period:** 2020-01-10 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10083230

## Citation

> US National Institutes of Health, RePORTER application 10083230, Model-based credit assignment (5R56MH119116-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10083230. Licensed CC0.

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