Joe Schmidt is an Assistant Professor in the Department of Psychology at the University of Central Florida.  He received his Ph.D. in Experimental & Cognitive Psychology in 2012 from Stony Brook University.  After receiving his doctorate, he spent two years as a Post-doctorate Research Fellow at the University of South Carolina’s Institute for Mind and Brain.  After his Post-doctorate position, he spent a year as a Research Support Specialist at SR Research (an eye tracking company).  His primary research interest focuses on the interaction of memory and attentional systems and how they affect our broader cognitive functions.  By simultaneously tracking eye movements and recording EEG/ERP, he can measure both overt and covert shifts of attention which can then be related to aspects of memory representations. Much of his research focuses on how changes to a target representation held in memory affect our ability to guide attention to a target object in the world around us.  Given that memory and attentional processes are involved in most tasks, his research interests are quite broad.  Lab website:



Miuccio, M. T., Zelinsky, G. J., & Schmidt, J. (2022). Are all real‐world objects created equal? Estimating the “set size” of the search target in visual working memory. Psychophysiology, 59(4), e13998.

Phelps, A. M., Alexander, R. G., & Schmidt, J. (2022). Negative cues minimize visual search specificity effects. Vision Research, 196, 108030.

Adamo, S. H., Gereke, B., Shomstein, S, & Schmidt, J. (2021) From “Satisfaction of Search” to “Subsequent Search
Misses”: A review of multiple‐target search errors across radiology and cognitive science. Cognitive Research: Principles and Implications 6‐021‐00318‐w

Ercolino, A. M., Patel, P., Bohil, C. J., Neider, M. B., & Schmidt, J. (2020) Target specificity improves search, but how universal is the benefit? Attention, Perception, & Psychophysics.‐020‐02111‐1

Palazzo, S., Spampinato, C., Kavasidis, I., Giordano, D., Schmidt, J., & Shah, M. (2020) Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).

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