# Computational foundations of active visual sensing

> **NIH NIH UF1** · BAYLOR COLLEGE OF MEDICINE · 2022 · $2,804,597

## Abstract

Abstract
Vision is an active process: we move our head and eyes to explore the sensory world. This is particularly
important in situations where a stationary view provides limited information, such as when looking for an object
that is occluded or obscured, which is common in complex natural scenes. However, our understanding of active
vision is limited due to experimental and theoretical challenges, including the difficulty of studying vision in freely
moving animals and the lack of formal theoretical frameworks that integrate visual representations with actions.
In this team project, we will combine expertise in visual neuroscience, behavior, machine learning, and theory,
to determine the behavioral, neural, and computational underpinnings of active sensing. Our approach is based
on a new theoretical framework of Bounded Rational Control (BRC), and a behavioral task in which mice perform
an object recognition task in the presence of occlusion and image corruptions. To enable active sensing, stimuli
in the task are rendered real-time in augmented reality based on the animal's viewpoint. In our first aim, we will
develop models of active sensing based on constrained visual representations in BRC. In the second aim, we
measure behavioral performance (both correct/incorrect responses and full-body movements) during the task,
and in the third aim we will measure neural activity across visual cortical areas during the task. For both Aims
2 and 3, we will fit our models to the corresponding behavioral and neural data, and then perform causal tests
of our models by presenting novel stimuli predicted to elicit specific responses from the model. Together, these
aims will provide a foundational understanding of active vision in the mouse that will support a subsequent U19
proposal taking advantage of genetic tools to investigate the underlying local and long-range neural circuits.

## Key facts

- **NIH application ID:** 10431247
- **Project number:** 1UF1NS126566-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Mackenzie Weygandt Mathis
- **Activity code:** UF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,804,597
- **Award type:** 1
- **Project period:** 2022-03-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10431247

## Citation

> US National Institutes of Health, RePORTER application 10431247, Computational foundations of active visual sensing (1UF1NS126566-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10431247. Licensed CC0.

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