Miguel Eckstein earned a BS in Physics and Psychology at UC Berkeley and a PhD 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, and the National Academy of Sciences Troland Award. 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, and as a member of various National Institute of Health study section panels. He served from 2005 to 2011 as the Vision Editor of the Journal of the Optical Society of America A and is currently on the board of editors of Journal of Vision, and the board of directors of the Vision Sciences Society. He has published over 120 articles relating to computational human vision, visual attention and search, perceptual learning and the perception of medical images.
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.
- Ludwig,. C., Eckstein, M.P., Foveal analysis and peripheral selection during active visual sampling, Proceedings of the National Academy of Sciences, E291-9, (2014)
- Eckstein, M. P., Mack, S. C., Liston, D. B., Bogush, L., Menzel, R., & Krauzlis, R. J. Rethinking human visual attention: Spatial cueing effects and optimality of decisions by honeybees, monkeys and humans, Vision research, 85:5-19 (2013)
- 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)
- Abbey, C. K., Eckstein, M. P. Modeling observer performance for optimizing medical image acquisition and processing. In IS&T/SPIE Electronic Imaging (pp. 82910S-82910S). International Society for Optics and Photonics (2012)
- 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)
- Eckstein MP, Beutter BR, Pham BT, Shimozaki SS, Stone LS. Similar neural representations of the target for saccades and perception during search, J Neuroscience, 27, 1266-70, (2007)
- 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)
- Shimozaki, S. S., Kingstone, A., Olk, B., Stowe, R., & Eckstein, M. P., Classification images of two right hemisphere patients: A window into the attentional mechanisms of spatial neglect, Brain Research, 1080, 26-52, (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
- Zhang, Y., Pham, B.T., Eckstein, M.P., (2004) Automated optimization of JPEG 2000 Encoder Options Based on Model Observer Performance for detecting variable signals in X-ray coronary angiograms, IEEE Transactions on Medical Imaging, 23, 459-474
- Bochud, F.O., Abbey, C.K., Eckstein, M.P., (2004) Search for lesions in mammograms: statistical characterization of observer responses, Medical Physics, 31, 24-36.
- Eckstein, M.P., Shimozaki, S.S., Abbey, C.K., (2002) The footprints of visual attention in the Posner paradigm revealed by classification images. Journal of Vision, 2(1), 25-45.
- Abbey, C. K. & Eckstein, M. P. (2002) Classification image analysis: Estimation and statistical inference for two-alternative forced-choice experiments. Journal of Vision, 2(1), 66-78.
- Eckstein, M. P., Thomas, J.P, Palmer, J., Shimozaki,S.S., (2000) A signal detection model predicts effects of set size on visual search accuracy for feature, conjunction, triple conjunction and disjunction displays, Perception & Psychophysics, 62,425-451.
- Eckstein, M.P., (1998) The lower efficiency for conjunctions is due to noise and not serial attentional processing, Psychological Science, 9, 111-118.
- Eckstein M.P, Visual Search: a retrospective, Journal of Vision, 11, (2011)
- Zhang S., Eckstein M.P., Evolution and Optimality of similar neural mechanisms for perception actions during search, PLoS Comput Biol., 9, 6, (2010)