# Neural and computational mechanisms underlying robust object recognition

> **NIH NIH R01** · VANDERBILT UNIVERSITY · 2024 · $396,488

## Abstract

Deep neural networks (DNNs) for object classification have been argued to provide the most promising state-
of-the-art models of the visual system, accompanied by claims that they have attained or even surpassed
human-level performance. However, mounting evidence has revealed that DNNs fail catastrophically when
faced with more noisy or degraded viewing conditions. By contrast, the human visual system is far more
robust. To better understand and model human vision, one must determine whether the brittle nature of DNN
performance arises from flaws in their architectural design, imperfections in their learning protocols, or
inadequate sampling of relevant training experiences. This project will investigate the neurocomputational
bases of robust object recognition, focusing on challenge conditions of visual noise and blur, to develop new
DNN models that can provide a better account of human behavioral and neural responses to object images
that will vary from clear to severely degraded. Both feedforward and recurrent DNN architectures will be
evaluated, and the critical sets of training experiences needed for DNNs to attain robustness will be
determined. In Aim 1, we will evaluate what types of DNNs can adequately predict human behavioral and
neural responses to objects embedded in noise on an image-by-image basis. Correspondences between fMRI
responses at multiple levels of the human visual pathway will be compared with layer-wise DNN
representations to evaluate the goodness of fit for DNN model predictions. In Aim 2, we will determine what
types of DNNs can better account for human behavioral and neural responses to blurry object images. We will
further explore how training with blurry images modifies the visual representations learned by DNNs, leading to
greater robustness to other types of image degradation and greater sensitivity to shape information. In Aim 3,
we will investigate whether perceptual training with noisy or blurry objects can allow humans to acquire even
greater robustness. We will then determine whether human improvements in behavioral and neural
performance can be effectively modeled by DNNs that undergo comparable regimens in visual training. As a
whole, this project will lead to the development of powerful new DNN models that provide a better account of
human behavioral and neural responses across a wide range of challenging viewing conditions. By developing
a better neurocomputational model of the intact human visual system, we will be better positioned to eventually
develop models of central visual disorders, which can arise from neurodevelopmental or neurological
disorders, stroke, head injury, brain tumors or other diseases. The advancement of more robust, human-like
DNNs is also highly relevant to AI applications in computer vision and medical image processing.

## Key facts

- **NIH application ID:** 10873149
- **Project number:** 5R01EY035157-02
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** FRANK TONG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $396,488
- **Award type:** 5
- **Project period:** 2023-08-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10873149

## Citation

> US National Institutes of Health, RePORTER application 10873149, Neural and computational mechanisms underlying robust object recognition (5R01EY035157-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10873149. Licensed CC0.

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