• DECODING ORIENTATION AND MOTION HIGH-RESOLUTION fMRI AT 7 TESLA The Tong Lab uses high-resolution fMRI at 7 Tesla to investigate the functional role of the early visual system in visual perception, attentional selection, figure-ground processing, predictive coding, and visual working memory.
  • FACE AND OBJECT PROCESSING FACE AND OBJECT PROCESSING We study the neurocomputational bases of face and object processing using behavioral methods, functional neuroimaging, TMS, and the development of convolutional neural networks (CNNs) as a model for human recognition performance.
  • MECHANISMS OF VISUAL ATTENTION MECHANISMS OF VISUAL ATTENTION Our lab investigates how bottom-up saliency and top-down attentional signals interact in the early visual system. We are also developing and testing a neurocomputational model of object-based attentional selection.
  • VISUAL WORKING MEMORY VISUAL WORKING MEMORY The Tong Lab pursues research on the behavioral and neural bases of visual working memory. Our goal is to characterize and model the neural representations that underlie visual working memory.
  • NEURAL BASES OF VISUAL AWARENESS DEEP LEARNING NETWORKS OF VISUAL PROCESSING A growing focus of the lab is the application and development of deep learning networks as potential models of human visual performance, especially for object recognition tasks. We are currently working with a variety of networks, including AlexNet, VGG-19, GoogleNet, ResNet and Inception-v3. We have also begun constructing our own deep network architectures.



Improved modeling of human vision by incorporating robustness to blur in convolutional neural networks.

Jang, H. & Tong, F. (2024) .

Nature Communications, doi: 10.1038/s41467-024-45679-0

Spikiness and animacy as potential organizing principles of human ventral visual cortex.

Coggan, D. D., & Tong F. (2023).

Cerebral Cortex, doi: 10.1093/cercor/bhad108

Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images.

Jang, H., McCormack, D., & Tong, F. (2021).

PLOS Biology, 18:496-498.

Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing.

Jang, H. & Tong, F. (2021).

Journal of Vision, 18:496-498.


Mar 2024

Former graduate student Hojin Jang departs his postdoc at MIT to begin a new position as Assistant Professor at his Alma Mater, Korea University. Check out his lab here. Congrats Hojin!

Fall 2023

Connor Parde and Ikhwan Jeon join the lab. Welcome!

August 2023

The TONGLAB is awarded an NEI R01 grant to investigate Neural and computational mechanisms underlying robust object recognition. Very excited about this new research project!

August 2022

Lasya Pidaparthi and Zoe Armstrong join the lab. Welcome Lasya and Zoe!

March 2022

The TONGLAB seeks to recruit a research assistant to the lab. For more details, click here.

October 2021

The TONGLAB seeks to recruit a postdoctoral fellow to the lab. For more details, click here.

August 2021

Hojin successfully defends his PhD thesis on "Exploring the robust nature of human visual object recognition through comparisons with convolutional neural networks". Congrats Dr. Jang!

August 2021

Rosanne Rademaker starts her position as a group leader at Max Planck, Ernst Strungmann Institute. Congrats Rosanne!.