about
Parametric study of EEG sensitivity to phase noise during face processingBrain classification reveals the right cerebellum as the best biomarker of dyslexia.Age-related delay in information accrual for faces: evidence from a parametric, single-trial EEG approach.LIMO EEG: a toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data.Vocal attractiveness increases by averaging.Inverting faces elicits sensitivity to race on the N170 component: a cross-cultural study.How parallel is visual processing in the ventral pathway?Improving standards in brain-behavior correlation analysesA robust and representative lower bound on object processing speed in humans.Beyond differences in means: robust graphical methods to compare two groups in neuroscience.Single-trial analyses: why bother?Visual Object Categorization in the Brain: What Can We Really Learn from ERP Peaks?Processing scene context: fast categorization and object interference.Quantifying the Time Course of Visual Object Processing Using ERPs: It's Time to Up the Game.Robust correlation analyses: false positive and power validation using a new open source matlab toolbox.Modeling Single-Trial ERP Reveals Modulation of Bottom-Up Face Visual Processing by Top-Down Task Constraints (in Some Subjects).Tracing the Flow of Perceptual Features in an Algorithmic Brain Network.Robust statistics show no evidence for a relationship between fiber density and memory performance.The time course of visual influences in letter recognition.Single-trial EEG dynamics of object and face visual processing.Early ERPs to faces and objects are driven by phase, not amplitude spectrum information: evidence from parametric, test-retest, single-subject analyses.A few simple steps to improve the description of group results in neuroscience.Spatial scaling factors explain eccentricity effects on face ERPs.Limits of event-related potential differences in tracking object processing speed.Reliability of ERP and single-trial analyses.Controlling interstimulus perceptual variance does not abolish N170 face sensitivity.Time course and robustness of ERP object and face differences.How do amplitude spectra influence rapid animal detection?Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes.Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes.Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise.Large-scale replication study reveals a limit on probabilistic prediction in language comprehension.Beyond differences in means: robust graphical methods to compare two groups in neuroscienceTaking the MAX from neuronal responsesProcessing of one, two or four natural scenes in humans: the limits of parallelismAnimal and human faces in natural scenes: How specific to human faces is the N170 ERP component?Spatiotemporal analyses of the N170 for human faces, animal faces and objects in natural scenesPromoting and supporting credibility in neuroscienceHealthy aging delays the neural processing of face features relevant for behavior by 40 ms
P50
Q33373831-ADC6F808-C3A5-4150-AD66-BD029744CA48Q33474551-37D5CE0E-1D7A-4583-AB5E-B734DD55343AQ33501353-D9A293DB-763E-4665-B6A1-919BD293C219Q33846050-3B55C2CF-AEC4-4929-816F-18B98D71F6D6Q34096584-2FFE6A00-D45D-4B45-91AE-00DD927021E1Q34097776-25E05BB1-4613-4F4C-B31E-2423ED9B7F7EQ35873927-2F21DEA9-DBB0-4E08-8990-335832CE33C5Q35929121-E939A752-7D61-4B63-8BC7-B083F787DECCQ37169468-678323AB-EA92-46B0-8F37-D8D20D80B173Q38764162-149C65E3-3D8E-4816-B608-B4E132B21700Q38979490-F400F56F-1E30-4698-A29E-F14127226A2BQ39516556-33929EED-D743-496C-8733-49713B833C80Q39819281-AEEB9EA6-0BE4-4B9D-A507-D5909FAD786BQ41428104-F9E513F6-2A51-40FC-B823-E832284A27C0Q41764930-23C78406-FE94-4732-B968-ED3010AB14EFQ42053957-F00222C7-4118-45F2-9659-29047BD90F12Q42641835-21FE5774-9946-434C-B0E3-A193B79632D7Q42905407-52227487-CEDF-4053-B7D3-7186D0255534Q43535993-F9401179-718A-4F38-A8A1-361B23739272Q48181062-BCCB2F9D-3240-4CAF-895A-6962D07D196CQ48252937-D2A18C5C-B21E-4D0A-B35C-0E498916C21BQ48448622-4339E2E6-4E81-47AB-B5A3-AF68A481D239Q48670847-B6FB7095-4E52-4895-9A64-3774D3DD5302Q48906719-154E3E36-68C8-403E-95B5-380638098309Q49070214-8186ECEB-0577-4200-99D6-D43A21D8C97CQ50969846-29F6A5EB-EC89-4C68-B912-AC8427911E27Q51878171-F1A11C4B-862C-4059-8DAE-5E3163F8D80BQ51924749-C0665749-3D66-49A5-8479-F681BC763E54Q51942479-1AA86420-03F7-4D9E-B609-607B411856D4Q51947348-29AFDE31-5374-4466-9DF1-2583C027BC08Q53111787-0AEC79A0-45BD-4121-A3E2-48D66FDA323EQ55052647-590A5A2B-1601-432C-9275-35C958DF85CDQ57303190-6E14B9C0-35B5-4E6E-B441-9856150518D9Q73133444-57756B75-2B87-444E-B3D8-6B04205FB8BCQ76392353-E96A3318-1EC3-4328-BF85-08274AC17252Q76400062-E4D7EC69-13E1-41BA-84AA-E60B952EAC3FQ81073564-6CB8D803-7719-41B0-AC0E-C9F1C4028BA7Q90285838-54F70718-6012-4A5E-835E-B297BFBEBC9AQ91545970-AD043826-5091-4D3E-860A-38F28C5BB81E
P50
description
hulumtues
@sq
onderzoeker
@nl
researcher
@en
հետազոտող
@hy
name
Guillaume A. Rousselet
@ast
Guillaume A. Rousselet
@en
Guillaume A. Rousselet
@es
Guillaume A. Rousselet
@nl
Guillaume A. Rousselet
@sl
type
label
Guillaume A. Rousselet
@ast
Guillaume A. Rousselet
@en
Guillaume A. Rousselet
@es
Guillaume A. Rousselet
@nl
Guillaume A. Rousselet
@sl
prefLabel
Guillaume A. Rousselet
@ast
Guillaume A. Rousselet
@en
Guillaume A. Rousselet
@es
Guillaume A. Rousselet
@nl
Guillaume A. Rousselet
@sl
P1053
B-4701-2009
P106
P1153
7006401879
P21
P2456
P31
P3829
P496
0000-0003-0006-8729