# Neural basis of causal inference: representations, circuits, and dynamics

> **NIH NIH U19** · UNIVERSITY OF ROCHESTER · 2021 · $2,494,439

## Abstract

Project Summary
The same pattern of neural activity can correspond to multiple events in the world. Signals sweeping across
the retina, for instance, might be generated by a moving object or by the animal's self-motion. The brain
resolves this ambiguity by inferring what events best explain sensory activity. This process, called causal
inference, is a foundation of action-perception loops in all sensory-motor systems. To support adaptive action,
neural representations of variables involved in these computations should be internally consistent. Yet little is
known about how such internal models arise, evolve, and interact. This proposal focuses on the neural
representations, circuits, and dynamics underlying causal inference in perception of object motion and depth
during self-motion. Because the relationships among these variables are defined by physics, not arbitrary
trained associations, and because they are likely represented by different cortical areas, the project will be able
to study how intercortical connections communicate to maintain an internally consistent view of reality. The
overall hypothesis is that causal inference involves computations in parietal and/or prefrontal cortex, and the
resulting signals are fed back to sensory areas to update neural representations of task-related variables.
 Project A will use Bayesian modeling to develop the theoretical framework for studying causal inference in
traditional trial-based tasks, and then combine this approach with real-time rational control theory to model
continuous, dynamic tasks. These models will be used to fit behavioral data and generate quantitative
predictions to compare with behavioral and neural responses in Projects B and C. Using trial-based tasks in
monkeys, Project B will ask how causal inference modulates neural correlates of flow parsing (in which
background motion influences perception of object motion), will examine how sensory representations are
updated by causal inference about object motion, and will use chemical and optogenetic inactivation to identify
the specific neural pathways that are necessary for such updating of sensory representations. In naturalistic,
continuous navigation tasks, Project C will use similar recording and neural manipulation approaches to
examine the neural dynamics of causal inference in monkeys, and will map neural correlates of dynamic
causal inference across the entire mouse brain in high-density neural recordings. The Data Science Core will
formalize procedures for storing and sharing data, and develop a standard data-processing pipeline, while the
Administrative Core will coordinate among the team and manage internal and external advisory committees.
 These comprehensive research efforts are expected to identify direct correlates of causal inference in single
neurons and neural populations and determine how the resulting beliefs about states of the world are
propagated from decision-making regions back to sensory regions of the bra...

## Key facts

- **NIH application ID:** 10225399
- **Project number:** 5U19NS118246-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** GREGORY C DEANGELIS
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $2,494,439
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225399

## Citation

> US National Institutes of Health, RePORTER application 10225399, Neural basis of causal inference: representations, circuits, and dynamics (5U19NS118246-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10225399. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*