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, and a Guggenheim Fellowship. 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, and as a member of National Institute of Health study section panels on Mechanisms of Sensory, Perceptual and Cognitive Processes and Biomedical Imaging Technology.
He has published over 170 articles relating to computational human vision, visual attention, search, perceptual learning, the perception of medical images. He has published in journals/conferences spanning a wide range of disciplines: Proceedings of the National Academy of Sciences, 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, Academic 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 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.
- 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)
- Domain specificity of oculomotor learning after changes in sensory processing, Y Tsank, MP Eckstein, Journal of Neuroscience, 1208-17, (2017)
- Deza, A., Ecktein, M.P. Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?, Neural Information Processing Systems (NIPS), 29, (2016)
- JR Peters, J.R., Srivastava, V., Taylor, G.S., Surana, A., Eckstein, M.P., Bullo, F. Human Supervisory Control of Robotic Teams: Integrating Cognitive Modeling with Engineering Design, Control Systems, IEEE 35, 57-80, (2015)
- Ludwig,. C., Eckstein, M.P., Foveal analysis and peripheral selection during active visual sampling, Proceedings of the National Academy of Sciences, E291-9, (2014)
- Preston, T. J., Guo, F., Das, K., Giesbrecht, B., & Eckstein, M. P. Neural Representations of Contextual Guidance in Visual Search of Real-World Scenes, The Journal of Neuroscience, 33(18), 7846-7855 (2013)
- Peterson, M. F., & Eckstein, M. P. Individual Differences in Eye Movements During Face Identification Reflect Observer-Specific Optimal Points of Fixation , Psychological science, 24(7), 1216-25 (2013)
- Peterson, M. F., & Eckstein, M. P. Looking just below the eyes is optimal across face recognition tasks. Proceedings of the National Academy of Sciences, 109(48), E3314-E3323 (2012)
- Eckstein M.P., Das K., Pham, B.T., Peterson M., Abbey, C.K., Sy J., Giesbrecht, B., Neural decoding of collective wisdom with multi-brain computing, Neuroimage, 59. 94-108, (2012)
- Guo F., Preston T.J., Das K., Giesbrecht B., Eckstein M.P., Feature-independent neural coding of target detection during search of natural scenes, Journal of Neuroscience, 32, 9499-510, (2012)
- Eckstein M.P, Visual Search: a retrospective, Journal of Vision, 11, (2011)
- Das K., Giesbrecht B., Eckstein M.P., Predicting variations of perceptual performance across individuals from neural activity using pattern classifiers, Neuroimage, 51(4):1425-37 (2010)
- Zhang S., Eckstein M.P., Evolution and Optimality of similar neural mechanisms for perception actions during search, PLoS Comput Biol., 9, 6, (2010)
- Eckstein, M.P., Drescher B., Shimozaki, S.S., Attentional cues in real scenes, saccadic targeting and Bayesian priors, Psychological Science, 17, 973-80 (2006)
- Zhang, Y, Pham, B.T., Eckstein, M. P., The Effect of Nonlinear Human Visual System Components on Performance of a Channelized Hotelling Observer Model in Structured Backgrounds, IEEE Transactions on Medical Imaging, 25, 1348-1362 (2006)
- Avi Caspi, Brent R. Beutter, and Miguel P. Eckstein, (2004) The time course of visual information accrual guiding eye movement decisions, Proceedings of the National Academy of Sciencies,101: 13086-13090
- Eckstein, M.P., (1998) The lower efficiency for conjunctions is due to noise and not serial attentional processing, Psychological Science, 9, 111-118.