# Project A:  Theoretical framework for studying causal inference in trial-based and continuous tasks

> **NIH NIH U19** · UNIVERSITY OF ROCHESTER · 2021 · $523,651

## Abstract

Project Summary
The same pattern of neural activity can correspond to multiple events in the world. The brain resolves this
ambiguity by inferring which causal model best explains a sensory input pattern, and generating beliefs about
the sensory variables in this model. The neural basis of causal inference is difficult to study, however, because
this internal model is only partly accessible through behavior. Normative modeling provides a powerful way to
circumvent this problem: if these computations are close enough to optimal, beliefs inferred by normative
models can be used to identify potential neural correlates. This project's goal is to develop normative models of
the motion tasks investigated experimentally in Projects B and C, to generate trial-by-trial as well as dynamic
moment-by-moment predictions of key latent variables in the computation, and to investigate their neural
implementation using data collected in those projects. These models will be fit to behavioral data to determine
how the brain uses causal inference applied to retinal image motion to infer the animal's self-motion, to decide
whether or not the object is moving in the world, and to infer the object's velocity and depth. For the trial-based
tasks in Project B, Aim 1 will start with the generative model of sensory inputs and invert it to produce causal
inferences. Preliminary work has extended and unified previous efforts into a novel Bayesian model that uses
retinal motion and depth to segment visual scenes during self-motion. Psychophysical tests show that this
static model agrees with perceptual experience. This model will be used to predict neural responses in cortical
motion-processing areas MT and MSTd by assuming that these responses represent Bayesian posterior
beliefs. In Aim 2, because the real world is not static, the team will develop a dynamic model that describes
normative causal inference and inverse rational control in real-time. This model will predict which latent
variables the brain needs to track in the continuous, naturalistic tasks of Project C. Preliminary work shows that
a simplified model using dynamic causal inference can keep a running estimate of self-motion velocity and of
whether an object is stationary or moving. Aim 2 will extend this model to more complex sensory inputs and to
support object motion dynamics on timescales similar to those of inference. It will also develop a real-time
rational control model to generate quantitative hypotheses about the neural correlates of goal-directed control
for animals acting upon the percepts from causal inference. We will fit this model to observed behavior to
reverse-engineer animals' beliefs during goal-directed control. When the proposed work is complete, the static
model will link three physically interconnected variables—object motion, self motion, and depth—which may be
computed and represented in different neural populations, to predict how beliefs about these variables
influence each other and...

## Key facts

- **NIH application ID:** 10225403
- **Project number:** 5U19NS118246-02
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Ralf Manfred Haefner
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $523,651
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10225403

## Citation

> US National Institutes of Health, RePORTER application 10225403, Project A:  Theoretical framework for studying causal inference in trial-based and continuous tasks (5U19NS118246-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10225403. Licensed CC0.

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