Nick Turk-Browne, PhD
Professor of Psychology and in the Child Study Center and of Neurosurgery and of Psychiatry and Director of the Wu Tsai InstituteCards
About
Titles
Professor of Psychology and in the Child Study Center and of Neurosurgery and of Psychiatry and Director of the Wu Tsai Institute
Biography
Nick Turk-Browne is Director of the Wu Tsai Institute and Professor with primary appointment in the Department of Psychology in the Faculty of Arts and Sciences and secondary appointments in the Departments of Neurosurgery and Psychiatry and the Child Study Center in the School of Medicine. He obtained an HBSc from the University of Toronto in 2004 and a PhD from Yale University in 2009, then served on the faculty at Princeton University from 2009-2017. Nick’s research takes an integrative perspective, using behavioral studies, functional magnetic resonance imaging, intracranial recording/stimulation, and computational modeling to understand how cognitive and neural systems interact in the human brain. He has published extensively on how we perceive and attend to the world, and how we learn from experience and store information in memory. His lab has recently pioneered techniques for brain imaging in awake and behaving infants. Nick's work has been published in Science, Nature Neuroscience, and PNAS, and has been featured in the New York Times, the New Yorker, and the Atlantic. He has been funded by NIH, NSF, Templeton Foundation, Intel, and Meta Reality Labs. He received Young Investigator Awards from the Vision Sciences Society (2016), Cognitive Neuroscience Society (2017), and Society of Experimental Psychologists (2018); the Distinguished Scientific Award for Early Career Contribution to Psychology from the American Psychological Association (2015); and is a Fellow of the Canadian Institute for Advanced Research (since 2016).
Appointments
Department of Psychology
ProfessorPrimaryChild Study Center
ProfessorSecondaryNeurosurgery
ProfessorSecondaryPsychiatry
ProfessorSecondary
Other Departments & Organizations
Education & Training
- PhD
- Yale University, Cognitive Psychology (2009)
- BSc (Hon)
- University of Toronto, Cognitive Science & Artificial Intelligence (2004)
Research
Publications
2024
Sculpting new visual categories into the human brain
Iordan C, Ritvo V, Norman K, Turk-Browne N, Cohen J. Sculpting new visual categories into the human brain. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2410445121. PMID: 39625982, PMCID: PMC11648923, DOI: 10.1073/pnas.2410445121.Peer-Reviewed Original ResearchConceptsHuman brainCategory of visual objectsReal-time functional MRIDomains of cognitionExplicit awarenessNeural biasFunctional MRINeural representationPerceptual distinctivenessVisual categoriesVisual objectsActivity patternsBrainMotor controlResearch paradigmVisual knowledgeParticipantsControl categoryCognitionNeurofeedbackMemoryLearningParadigmCategoriesDecision-makingInducing representational change in the hippocampus through real-time neurofeedback
Peng K, Wammes J, Nguyen A, Iordan C, Norman K, Turk-Browne N. Inducing representational change in the hippocampus through real-time neurofeedback. Philosophical Transactions Of The Royal Society B Biological Sciences 2024, 379: 20230091. PMID: 39428880, PMCID: PMC11491844, DOI: 10.1098/rstb.2023.0091.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingReal-time functional magnetic resonance imagingPatterns of fMRI activityCategorical perception taskReal-time neurofeedbackNeurocognitive mechanismsFMRI sessionStimulus similarityFMRI activationTask demandsBehavioral consequencesUntrained objectsNeural representationPerception taskMemory integrationRepresentational changeEndogenous neuromodulatorNeurofeedbackHippocampusCortical representationIncreased coactivationMagnetic resonance imagingVisual cortexMemoryMultiple theoriesA neural network model of differentiation and integration of competing memories
Ritvo V, Nguyen A, Turk-Browne N, Norman K. A neural network model of differentiation and integration of competing memories. ELife 2024, 12: rp88608. PMID: 39319791, PMCID: PMC11424095, DOI: 10.7554/elife.88608.Peer-Reviewed Original ResearchConceptsNeural network modelUnsupervised neural network modelUnsupervised learning mechanismLearning mechanismLearning modelsNetwork modelComputational explanationInactive memoryNeural representationActive competitorsDiverse setRepresentationMemoryRepresentation of memoryMemory literatureBrain regionsNovel predictionsA neural network model of differentiation and integration of competing memories
Ritvo V, Nguyen A, Turk-Browne N, Norman K. A neural network model of differentiation and integration of competing memories. ELife 2024, 12 DOI: 10.7554/elife.88608.3.Peer-Reviewed Original ResearchNeural network modelUnsupervised neural network modelUnsupervised learning mechanismLearning mechanismLearning modelsNetwork modelComputational explanationInactive memoryNeural representationActive competitorsDiverse setRepresentationMemoryRepresentation of memoryMemory literatureBrain regionsNovel predictionsThe representational dynamics of visual expectations in the brain
Caplette L, Kurumisawa T, Borges H, Cortes-Briones J, Turk-Browne N. The representational dynamics of visual expectations in the brain. Journal Of Vision 2024, 24: 1362. DOI: 10.1167/jov.24.10.1362.Peer-Reviewed Original ResearchAcute Stress Effects on Statistical Learning and Episodic Memory
Sherman B, Huang I, Wijaya E, Turk-Browne N, Goldfarb E. Acute Stress Effects on Statistical Learning and Episodic Memory. Journal Of Cognitive Neuroscience 2024, 36: 1741-1759. PMID: 38713878, PMCID: PMC11223726, DOI: 10.1162/jocn_a_02178.Peer-Reviewed Original ResearchEpisodic memoryAcute stressStatistical learningEffects of acute stressStress-induced cortisol levelsImpaired episodic memoryModulate learning processesAcute stress effectsImpact hippocampal functionEpisodic encodingRodent workEvidence of learningHippocampal functionStress effectsHippocampal pathwaysLearning tasksBehavioral experimentsPreliminary evidenceCortisol levelsNo stressMemoryMultiple measuresHippocampusScene categoriesParticipantsAttention and Memory
Sherman B, Turk-Browne N. Attention and Memory. 2024, 587-613. DOI: 10.1093/oxfordhb/9780190917982.013.21.Peer-Reviewed Original ResearchControl of attentionWorking memoryBrain systemsEpisodic memoryMemory encodingAttentional selectionConscious awarenessAbstract AttentionComplex sensory inputMemoryBehavioral mechanismsSensory inputPervasive roleDownstream consequencesBidirectional interactionsPerceptionModulation processAttentionHuman perceptionMindfulnessBrainVigilanceCurrent environmentEncodingRetrievalComputational reconstruction of mental representations using human behavior
Caplette L, Turk-Browne N. Computational reconstruction of mental representations using human behavior. Nature Communications 2024, 15: 4183. PMID: 38760341, PMCID: PMC11101448, DOI: 10.1038/s41467-024-48114-6.Peer-Reviewed Original ResearchConceptsMental representationsGoal of cognitive scienceVisual featuresNeural networkMultiple visual conceptsDeep neural networksConceptual representationCognitive scienceVisual conceptsSemantic spaceSemantic featuresHuman behaviorParticipantsNetworkRepresentationStimuliBehaviorFeaturesImagesComputer reconstructionTaskInfant neuroscience: how to measure brain activity in the youngest minds
Turk-Browne N, Aslin R. Infant neuroscience: how to measure brain activity in the youngest minds. Trends In Neurosciences 2024, 47: 338-354. PMID: 38570212, DOI: 10.1016/j.tins.2024.02.003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsFunctional magnetic resonance imagingFunctional near-infrared spectroscopyMeasure brain activityBrain activityApplication of functional magnetic resonance imagingHuman infantsCognitive neuroscienceCognitive tasksInfant brainAwake infantsMagnetic resonance imagingNear-infrared spectroscopyMagnetoencephalographyMindfulnessResonance imagingNon-invasive methodInfantsElectroencephalographySystematic 1 Hz direct electrical stimulation for seizure induction: A reliable method for localizing seizure onset zone and predicting seizure freedom
Sivaraju A, Quraishi I, Collins E, McGrath H, Ramos A, Turk-Browne N, Zaveri H, Damisah E, Spencer D, Hirsch L. Systematic 1 Hz direct electrical stimulation for seizure induction: A reliable method for localizing seizure onset zone and predicting seizure freedom. Brain Stimulation 2024, 17: 339-345. PMID: 38490472, DOI: 10.1016/j.brs.2024.03.011.Peer-Reviewed Original ResearchSeizure inductionSeizure onset zoneSurgical outcomesHabitual seizuresOnset zonePredicting seizure freedomExcellent surgical outcomesProspective cohort studyMann-Whitney testConsecutive patientsFisher's exactSeizure freedomRefractory epilepsyCohort studyPost-surgeryInsular regionsMann-WhitneyPatientsIntracranial EEGSeizuresLocalizing seizure onset zonesStimulationElectrical stimulationMedial temporal regionsTemporal regions
News & Links
News
- May 10, 2024
Autism conference at Yale highlights latest research & clinical advances
- June 27, 2022
Two Yale School of Public Health professors appointed to Wu Tsai Institute at Yale
- March 17, 2021Source: YaleNews
Babies Pay Attention with Down Payment from Immature Brain Region
- December 15, 2019
Kavli Workshop on Jan. 8: "Current Perspectives on the Generation and Analysis of Complex Data in Neuroscience"