CRS Guest Talks, Best Poster Prizes & Travel Awards > CRS Guest Lecturers > John Serences
John Serences is Associate Professor of Psychology at the University of California San Diego (UCSD). Work in his lab employs converging methods including computational modeling, psychophysics, fMRI and EEG to study how attentional factors influence perceptual processing, working memory and decision making. Serences did his undergraduate work at the University of California, San Diego under the guidance of Dr. Harold Pashler, his graduate work at Johns Hopkins University with Dr. Steven Yantis, and a Postdoctoral Fellowship at the Salk Institute with Dr. Geoffrey Boynton. He was a faculty member in the department of Cognitive Sciences at UC Irvine for 1.5 years before moving back to UCSD in 2008. Work in his lab is generously funded by the National Institutes of Mental Health and a James S. McDonnell Foundation Scholar Award.
ACNS 2012: Evaluating Optimal Models of Information Processing in Visual Cortex
Current behavioral goals and motivational drives play a critical role in shaping and refining information processing so that only the most relevant sensory stimuli are perceived, represented in working memory, and allowed to influence decision making. Traditional accounts hold that these "top-down" attentional factors are critically important in information processing precisely because attention enhances the gain of the sensory neurons that are selectively tuned to relevant stimulus features. These models are intuitively appealing, and suggest that attention effectively increases the intensity of important stimuli so that they are easier to perceive, remember, and act upon. Using the early visual system as a model, I will present psychophysical and fMRI evidence challenging this traditional framework by showing that the attention modulates the gain of the most informative sensory neurons given whatever specific perceptual task confronts the observer. Counter-intuitively, enhancing the gain of the most informative sensory neurons often means biasing patterns of neural activity away from the “veridical” pattern that is evoked by a sensory stimulus.