David SheinbergProfessor of Neuroscience, Director of Neuroscience Graduate Program
I received my AB in Computer Science and Psychology from Yale College and my PhD in Cognitive Science at Brown. Following postdoctoral fellowships at Baylor College of Medicine in Houston and the Max Planck Institute in Tuebingen, Germany, I returned to Brown as a faculty member in the Department of Neuroscience in 2000.
Dai J, Ozden I, Brooks DI, Wagner F, May T, Agha NS, Brush B, Borton D, Nurmikko AV, Sheinberg DL "Modified toolbox for optogenetics in the nonhuman primate," Neurophoton., 2(3), 031202 (2015).doi:10.1117/1.NPh.2.3.031202.
Xia R, Guan S, & Sheinberg DL (2015). A multilayered story of memory retrieval, Neuron, 86, 610-612. [neuron]
Sigurdardottir HM & Sheinberg DL (in press). The effects of short-term and long-term learning on the responses of lateral intraparietal neurons to visually presented objects. J Cog Neurosci.
May T, Ozden I, Brush B, Borton D, Wagner F, Agha N, Sheinberg DL, Nurmikko AV (2014) Detection of optogenetic stimulation in somatosensory cortex by nonhuman primates - towards artificial tactile sensation. PLoS ONE 9(12): e114529. doi:10. 1371/journal.pone.0114529 [PLoS ONE]
Brooks DI, Sigurdardottir HM, & Sheinberg DL (2014). The neurophysiology of attention and object recognition in visual scenes. In K Kverga & M Bar (eds.) Scene Vision. Cambridge MA: MIT Press, 85-104.
Dai J, Brooks DI, & Sheinberg DL (2014). Optogenetic and electrical stimulation systematically bias visuospatial choice in primates, Current Biology, 24, 63-69. [current biology] [press] [optics.org]
Sigurdardottir HM, Michalak SM, & Sheinberg DL (2014). Shape beyond recognition: Form-derived directionality and its effects on visual attention and motion perception, J Exp Psychol: General, 143(1), 434-454. doi: 10.1037/a0032353. [APA pdf] [pubmed] [PeePs]
Woloszyn L & Sheinberg DL (2012). Effects of long-term visual experience on responses of distinct classes of single units in inferior temporal cortex, Neuron.
Mruczek REB & Sheinberg DL (2012). Stimulus selectivity and response latency in putative inhibitory and excitatory neurons of the primate inferior temporal cortex, J Neurophys.
Monosov IE, Sheinberg DL, & Thompson KG (2011). The effects of prefrontal cortex inactivation on object responses of single neurons in the inferotemporal cortex during visual search, J. Neurosci. 2011;31 15956-15961.
Sheinberg DL & Tarr MJ (2010). Objects of Expertise. In I Gauthier and D Bub (Eds.), Perceptual Expertise: Bridging Brain And Behavior, Oxford University Press: New York.
Monosov IE, Sheinberg DL, & Thompson KG (2010) Paired neuron recordings in the prefrontal and inferotemporal cortices reveal that spatial selection precedes object identification during visual search, Proceedings of the National Academy of Sciences.
Woloszyn L & Sheinberg DL (2009). Shape representation in inferotemporal cortex. In Larry R. Squire, Editor-in-Chief, Encyclopedia of Neuroscience, Academic Press, Oxford, 777-785.
Woloszyn L & Sheinberg DL (2009). Neural dynamics in inferior temporal cortex during a visual working memory task, J Neurosci, 29, 5494-5507.
Anderson B & Sheinberg DL (2008). Effects of temporal context and temporal expectancy on neural activity in inferior temporal cortex, Neuropsychologia, 46, 947-957.
Scott LS, Tanaka JW, Sheinberg DL & Curran T (2008). The role of category learning in the acquisition and retention of perceptual expertise: A behavioral and neurophysiological study. Brain Research, 19, 204-215.
Anderson B, Mruczek REB, Kawasaki K & Sheinberg DL (2008). Effects of familiarity on neural activity in monkey inferior temporal lobe, Cereb Cortex, 18, 2540-2552.
Singer J & Sheinberg DL (2008). A method for the real-time rendering of formless dot field structure-from-motion stimuli, Journal of Vision, 8, 1-8.
Kawasaki K & Sheinberg DL (2008). Learning to recognize visual objects with microstimulation in inferior temporal cortex. J Neurophys. 100, 197-211.
Anderson B, Sanderson MI & Sheinberg DL (2007). Joint decoding of visual stimuli by IT neurons' spike counts is not improved by simultaneous recording. Exp Brain Res, 176, 1-11.
Peissig JJ, Singer J, Kawasaki K & Sheinberg DL (2007). Effects of long-term object familiarity on event-related potentials in the monkey. Cereb Cortex, 17, 1323-1334.
Mruczek REB & Sheinberg DL (2007). Activity of inferior temporal cortical neurons predicts recognition choice behavior and recognition time during visual search. J Neurosci, 27, 2825-2836.
Amarasingham A, Chen TL, Geman S, Harrison MT & Sheinberg DL (2006). Spike count reliability and the Poisson hypothesis. J Neurosci, 26, 801-809.
Singer J & Sheinberg DL (2006). Holistic processing unites face parts across time. Vision Res, 46, 1838-1847.
Anderson B, Peissig JJ, Singer J & Sheinberg DL (2006). XOR style tasks for testing visual object processing in monkeys. Vision Res, 46, 1804-1815.
Scott LS, Tanaka JW, Sheinberg DL, Curran T (2006). A reevaluation of the electrophysiological correlates of expert object processing, J Cog Neurosci, 18, 1453-1465.
Sheinberg DL, Peissig JJ, Kawasaki K & Mruczek REB (2006). Initial saccades predict manual recognition choices in the monkey. Vision Res, 46, 3812-3822.
Anderson B, Harrison MT & Sheinberg DL (2006). A multielectrode study of the inferotemporal cortex in the monkey: effects of grouping on spike rates and synchrony. Neuroreport, 17, 407-411.
Tanaka J, Curran T, Sheinberg DL (2005). The training and transfer of real-world, perceptual expertise. Psych Sci, 16,145-151.
Mruczek REB & Sheinberg DL (2005). Distractor familiarity leads to more efficient search for complex stimuli. Perception and Psychophysics, 67, 1016-1031.
Tse PU, Logothetis NK, & Sheinberg, DL (2004). The distribution of microsaccade directions need not reveal the location of attention. Psych Science.
Logothetis NK, Leopold DA, & Sheinberg DL (2003). Neural mechanisms of perceptual organization. In N. Osaka (Ed.) Neural basis of consciousness. Advances in Consciousness Research: Vol. 49 (pp. 87-103). Amsterdam, Netherlands: John Benjamins Publishing Company.
Tse PU, Sheinberg DL, Logothetis NK (2003) Attentional enhancement opposite a peripheral flash revealed by change blindness. Psych Sci, 14, 91-99.
Sheinberg DL, Logothetis NK (2002) Perceptual learning and the development of complex visual representations in temporal cortical neurons. In M. Fahle and T. Poggio (Eds.), Perceptual Learning, MIT Press, 95-124.
Tse PU, Sheinberg DL, Logothetis N.K. (2002) Fixational eye movements are not affected by abrupt onsets in the periphery. Vision Res, 42, 1663-1669.
Sheinberg DL, Logothetis NK (2001) Noticing familiar objects in real world scenes: The role of temporal cortical neurons in natural vision, J Neurosci, 21, 1340-1350.
Logothetis NK, Leopold DA, Sheinberg DL (1997) The neurophysiological basis of bistable percepts. In Y. Miyashita, T. Ihui, M. Kawato, K. Tanaka, and J Murata (Eds.) Proceedings of the IIIA. Kyoto.
Sheinberg DL, Logothetis NK (1997) The role of temporal cortical areas in perceptual organization, PNAS, 94, 3408-3413.
Zelinsky GJ, Sheinberg, DL (1997) Eye movements during parallel/serial visual search, JEP [HPP], 25, 244-262. [pubmed]
Blake A, Bülthoff HH, Sheinberg DL (1996) Shape from texture: Ideal observers and human psychophysics. In D. Knill and W. Richards (Eds.) Perception as Bayesian Inference, Cambridge, MIT Press, 287--322.
Logothetis NK, Sheinberg DL (1996) Recognition and representation of visual objects in primates: Psychophysics and physiology. In Llinas, R. and Churchland, P. (Eds.) The Mind-Brain Continuum, Cambridge, MIT Press, 147-172.
Logothetis NK, Leopold DA, Sheinberg DL (1996) What is rivalling during binocular rivalry? Nature, 380, 621-624. [pubmed]
Zelinsky GJ and Sheinberg DL (1996) Saccades reflect differences in visual search efficiency. In A.G. Gale (Ed.), Proceedings of the Third International Conference on Visual Search. London: Taylor & Francis Ltd.
Blake A, Bülthoff, HH, Sheinberg, DL (1993) Shape from texture: Ideal observers and human psychophysics. Vision Res, 33, 1723-1737.
Sheinberg DL, Zelinsky GJ (1993) A cortico-collicular model of saccadic target selection. In J. Rensbergen, V. Devijver, and G. d'Ydewalle (Eds.) Proceedings of the 6th European Conference on Eye Movements, Amsterdam, North-Holland.
Research in my lab explores how we identify objects and events in the real world, where both the observer and the environment change over time. The brain must process a dynamic stream of sensory information and efficiently parse this information to reach conclusions about the presence or absence of noteworthy objects to which actions should be directed. By studying the activity of neural circuits involved in this process, we aim to better understand mechanisms underlying perception.
As we interact with our surroundings, the gap between looking and knowing is usually quick and effortless. But by what processes is this accomplished? How do we make sense of what we see?
Current projects in the lab are exploring the dynamic nature of visual processing. In the real world, visual observers constantly update their view of their environment by changing their direction of gaze. At the same time, objects in the environment are often themselves animate, and the characteristic dynamic behaviors of these objects provide rich information about the identities and actions of objects in the world. We are particularly interested in how the brain acquires a dynamic stream of visual information and efficiently parses this information to reach conclusions about the presence or absence of noteworthy objects to which actions should be directed.
We are also investigating how representations in the brain are dynamic, changing over time to reflect the learned structure of individual objects and the context in which these objects appear. Perceptual learning and visual expertise are perhaps the most obvious forms of plasticity that continue to manifest in adult animals, and we believe changes in receptive field properties of individual cells and networks of cells underlie changes in perceptual capacities. Our aim is to simultaneously explore changes in behavioral performance associated with expertise along with changes in neural response properties that can be recorded from individual neurons while these same tasks are being carried out. Changes in larger-scale neural response are also hotly debated, and we are investigating how EEG signals track increasing familiarity of learned images.
As we gain expertise with a new class of visual objects, our facility with instances of that class changes - we become better able to discriminate between individual exemplars, and we are less able to focus exclusively on isolated parts. We are studying how the parts that make up an object are used to identify the object, and how the neural representations of those parts come together to form a representation of the whole. Do these processes depend on our familiarity with the objects in question, or with the object classes? Are there particular kinds of objects, such as faces, that have specific dedicated modes of processing?
By simultaneously recording from multiple neurons in actively behaving subjects, we hope to understand which neural features show a quantitative variation that parallels ability. Subjects perform classification tasks relying on conjunctions of visual stimuli; behavioral measures indicate learning of these conjunctions and when they are treated as configurations versus new whole objects. We record from inferotemporal neurons and use these data to develop and evaluate statistical methods for understanding how it is that we "know" when two objects "go together" - the binding problem. Are the joint firing statistics of two inferotemporal neurons respectively selective for two stimuli more informative for predicting grouping status than each neuron considered independently?
To directly test possible causal effects of neural activity in parts of the ventral visual pathway on perceptual decisions and looking behavior, we are testing effects of stimulation of small populations of these cells. By carefully controlling the timing and duration of stimulation, we seek to demonstrate the role that cells in inferotemporal cortex play in forming perceptual decisions about the structure of visual objects or the presence of known objects in complex environments.
Dynamic visual activity in temporal cortex, April 2004 - June 2018. National Institutes of Health (PI: Sheinberg, RO1-EY014681).
NIH NINDS 1U01NS090557 (NIH BRAIN Initiative),” Large-scale Electrophysiological Recording and Optogenetic Control System” (PIs: Goodell, Gray, Sheinberg, Pesaran)
NSF Science of Learning Center “Temporal Learning Center”, Oct 2006-Sept 2015, (Senior Investigator; PI: Gary Cottrell, UCSD, SBE-0542013).
DARPA Brain Reorganization and Plasticity to Accelerate Injury Recovery: Multi-Scale and Multi-Modal models Enabled by: Next Generation Neurotechnology (PI: Shenoy, N66001-10-C-2010). April 2010-March 2013.
ONR Bidirectional vision (Co-PI, Lead PI: R O’Reilly, BAA-13-022). May 2014-August 2017.
Norman Prince Neuroscience Institute and Brown Institute for Brain Science Seed Grant, “Visual Processing in Body Dysmorphic Disorder” (PIs: Sheinberg and Phillips).
Brown DAENS Award, “Modification of Perception in Body Dysmorphic Disorder and Neural Correlates” (PIs: Sheinberg and Phillips)