# Computational Modeling Core (C)

> **NIH NIH P50** · PRINCETON UNIVERSITY · 2024 · $403,247

## Abstract

CORE C – Computational Modeling Core
The computational modeling core will provide computational model development and model-based data
analysis tools for all the Projects in the Center. A central approach of this Center, and of much work in
computational psychiatry, is the use of formal computational models to quantify otherwise abstract functions
and connect them to basic neural and cognitive mechanisms. All of the Center's projects concern a class of
computational models known as latent cause inference, which describe how humans and animals cluster their
experiences so as to identify different underlying contexts in which different rules apply. Such inference has
important effects on learning and decision making, and we hypothesize that dysfunction in these processes is
implicated in numerous mental illnesses. A key approach for formalizing and testing such hypotheses across
all our Projects is estimating the free parameters of the latent cause model: a set of interpretable “knobs” that
directly control the behavior of the model. Thus, from an individual's behavior or brain measurements, we can
determine the model parameters that best explain the data, and test whether these differ in psychopathology.
To support this approach, the Computational Modeling Core has three aims. The first is to develop a single,
uniform latent cause model appropriate to the tasks and experimental measurements across all the Projects.
The second aim is to build data analysis tools to fit this model to multivariate, multimodal experimental
datasets. This involves estimating the model's parameters both for each individual and, using hierarchical
modeling techniques, at the group-level and for condition averages. These estimates allow us to conduct
statistical tests such as whether the model's parameters are different between healthy and clinical groups or
whether the parameters change in a graded fashion with symptoms. Our third aim is to develop tools to
estimate graded dimensions of psychopathology (e.g., depression, anxiety) from self-report psychiatric
symptom data collected in the Projects. For this, we will use modern hierarchical factor analysis techniques to
characterize accurately the separate dimensions of illness, while coping with high levels of comorbidity
between them.
By concentrating modeling infrastructure in a single Core, all Center Projects will benefit from a unified model
that was constrained by data from multiple tasks. The shared model and model-fitting tools will allow easy
comparison of results across Projects, and cross-Project insights to emerge. By implementing uniform statistical
best practices for data analyses, this Core’s work will also promote the rigor and reproducibility of all the
Projects.

## Key facts

- **NIH application ID:** 10862343
- **Project number:** 1P50MH136296-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Nathaniel Douglass Daw
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $403,247
- **Award type:** 1
- **Project period:** 2024-08-12 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862343

## Citation

> US National Institutes of Health, RePORTER application 10862343, Computational Modeling Core (C) (1P50MH136296-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10862343. Licensed CC0.

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