Miguel Eckstein

Distinguished Professor

Research Area

Cognition, Perception, and Cognitive Neuroscience

Biography

Miguel Eckstein earned a Bachelor Degree in Physics and Psychology at UC Berkeley and a Doctoral Degree in Cognitive Psychology at UCLA. He then worked at the Department of Medical Physics and Imaging, Cedars Sinai Medical Center and NASA Ames Research Center before moving to UC Santa Barbara. He is recipient of the Optical Society of America Young Investigator Award, the Society for Optical Engineering (SPIE) Image Perception Cum Laude Award, Cedars Sinai Young Investigator Award, the National Science Foundation CAREER Award, the National Academy of Sciences Troland Award, a Guggenheim Fellowship and the Anne and Donald Herbert Distinguished Lectureship in Modern Statistical Modeling, Association of Physicists in Medicine.

He has served as the chair of the Vision Technical Group of the Optical Society of America, chair of the Human Performance, Image Perception and Technology Assessment conference of the SPIE Medical Imaging Annual Meeting, Vision Editor of the Journal of the Optical Society of America A, the board of directors of the Vision Sciences Society, the board of editors of Journal of Vision, as a member of National Institute of Health study section panels on Mechanisms of Sensory, Perceptual and Cognitive Processes and Biomedical Imaging Technology, and the Behavioral, Cognitive and Sensory Science advisory board of the National Academy of Sciences.

He has published over 250 articles relating to computational human vision, visual attention, search, perceptual learning, the perception of medical images, artificial intelligence (AI) and human/AI teaming. He has published in journals/conferences spanning a wide range of disciplines: Proceedings of the National Academy of Sciences, Nature Communications, Nature Human Behavior, Current Biology, Journal of Neuroscience, Psychological Science, PLOS Computational Biology, Annual Reviews in Vision Science, Neural Information Processing Systems (NIPS), Computer Vision and Pattern Recognition (CVPR), IEEE Transactions in Medical Imaging, International Conference in Learning Representations (ICLR), Neuroimage, Radiology, Journal of the Optical Society of America A, Medical Physics, Journal of Vision, Journal of Experimental Psychology Human Perception and Performance, Vision Research, and SPIE Medical Imaging.

Research

Research Interests: Finding your toothbrush, recognizing a face, or an object all might seem effortless but behind the scenes the brain devotes over 1/4 of its neural machinery to make these complex tasks seem easy. How does the brain do it? My research uses a wide variety of tools including behavioral psychophysics, eye tracking, electro-encephalography (EEG), functional magnetic resonance imaging (fMRI) and computational modeling to understand how the brain successfully achieves these everyday perceptual tasks. The investigations involve understanding basic visual perception, eye movements, visual attention, perceptual learning and decision making. I utilize the gained knowledge about how the brain accomplishes every day vision in combination with engineering tools to advance various applied problems: 1) understanding visual, cognitive and decision processes by which doctors detect and classify abnormalities in medical images and developing computer models to improve the way in which we display medical images so that doctors can do fewer errors in clinical diagnosis; 2) develop with engineers bio-inspired computer vision systems; 3) improve the interactions between robots/computer systems and humans.

Selected Publications

Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency, Z Wen, P Madinei, MP Eckstein, Neurips, In press, (2026)

Murlidaran, S., Eckstein, M.P. Eye movements during free viewing to maximize scene understanding. Nat Communication, 17, 940 (2025)

Srivastava S, Wang WY, Eckstein MP. Emergent neuronal mechanisms mediating covert attention in convolutional neural networks. Proc Natl Acad Sci USA,122(46):e2411909122, (2025)

Convolutional neural network model observers discount signal-like anatomical structures during search in virtual digital breast tomosynthesis phantoms, A Jonnalagadda, BB Barufaldi, ADA Maidment, SP Weinstein, CK Abbey,…M.P. Eckstein, Journal of Medical Imaging 12 (5), 051809-051809, (2025)

Srivastava S, Wang WY, Eckstein MP. Emergent human-like covert attention in feedforward convolutional neural networks, Current Biology 34(3):579-593.e12 (2024)

Inferential eye movement control while following dynamic gaze NX Han, MP Eckstein, Elife 12, e83187, (2023)

Welbourne, L. E., Jonnalagadda, A., Giesbrecht, B., & Eckstein, M. P. The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships. Communications Biology, 4(1), 1-14, (2021)

Lago, M. A., Jonnalagadda, A., Abbey, C. K., Barufaldi, B. B., Bakic, P. R., Maidment, A. D., .& Eckstein, M. P. Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets. Current Biology, (2021)

Rosedahl, L.A., Eckstein, M.P., Ashby, F.G., Retinal-specific category learning, Nature Human Behaviour 2 (7), 500, (2018)

Eckstein, M.P., Koehler, K., Akbas, E., Humans but not Deep Neural Networks miss giant targets in scenes, 27 (18), 2827-2832, Current Biology, (2017)

Akbas, E., Eckstein, M.P., Object Detection with a Foveated Search Model, PLOS Computational Biology, 13 (10), e1005743, (2017)

Juni, M., Eckstein M.P., The wisdom of the crowds for visual search, Proceedings of the National Academy of Sciences, 201610732, (2017)

Probabilistic computations for attention, eye movements, and search, Eckstein, M.P., Annual Review of Vision Science 3, 319-342, (2017)