# Project 1: Latent-cause inference as a fundamental cognitive process

> **NIH NIH P50** · PRINCETON UNIVERSITY · 2024 · $403,248

## Abstract

PROJECT 1 – Latent cause inference as a fundamental cognitive process
Latent cause inference is a fundamental cognitive process that allows us to group experiences together, so that
what happened in past events can easily influence what we do in new (similar) situations. Different
individuals might group events differently into latent causes, depending on the parameters of latent cause
inference in their brain: how readily they create new latent causes to explain novel experiences, how persistent
they believe latent causes are across time, and how similar they expect events to be that are ascribed to a
shared latent cause.
Because latent cause inference is at the heart of perception, learning, evaluation, and action selection,
alterations in the process due to extreme parameters may lead to psychopathology. For instance, over-splitting
of latent causes can prevent updating of previous knowledge with new contradicting information (see Project
4), possibly leading to a disorder of compulsion (Project 2) or anxiety (Project 3). Parameters of the latent
cause inference process may therefore provide useful biomarkers for vulnerability to psychopathology, and
this cognitive process may present an important transdiagnostic dimension that should be added to the NIMH
Research Domain Criteria (RDoC) framework.
In Project 1, our goal is to characterize fundamental individual differences in latent cause inference and relate
them to mental health symptom dimensions in a large general population sample (Aim 1.1), test their potential
relevance to several key mental health conditions (generalized anxiety, obsessive compulsive disorder, and
schizophrenia; Aim 1.2), and understand the circuitry that realizes latent cause inference in the brain, and its
alteration in psychiatric conditions (Aim 1.3). Towards these aims, we will use three behavioral tasks, one
laboratory task designed specifically to measure latent cause inference (the Microbes Task), and two
naturalistic event segmentation tasks that measure individual differences in perception of boundaries in
naturalistic streams of events (movies or stories).
By characterizing latent cause inference across mental health conditions and using multiple tasks, the results of
Project 1 will provide a comprehensive assessment of the potential use of latent cause inference as an RDoC
construct. Moreover, data from this project will provide strong constraints for the computational model of
latent cause inference developed by Core C, which will be used to analyze data from all projects in this Center.
In this way, association of latent cause inference parameters with a variety of symptom dimensions will help
constrain and interpret results in Projects 2 and 3, and the results of Aim 1.3 will potentially inform neural
recordings in Project 4.

## Key facts

- **NIH application ID:** 10862338
- **Project number:** 1P50MH136296-01
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Yael Niv
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $403,248
- **Award type:** 1
- **Project period:** 2024-08-12 → 2029-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862338

## Citation

> US National Institutes of Health, RePORTER application 10862338, Project 1: Latent-cause inference as a fundamental cognitive process (1P50MH136296-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10862338. Licensed CC0.

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