GR
Achievements
Publications
- Robust correlation analyses: False positive and power validation using a new open source matlab toolbox DOI: 10.3389/fpsyg.2012.00606
- Early ERPs to faces: Aging, luminance, and individual differences DOI: 10.3389/fpsyg.2013.00268
- Improving standards in brain-behavior correlation analyses DOI: 10.3389/fnhum.2012.00119
- Early ERPs to faces and objects are driven by phase, not amplitude spectrum information: Evidence from parametric, test-retest, single-subject analyses DOI: 10.1167/12.13.12
- Does filtering preclude us from studying ERP time-courses? DOI: 10.3389/fpsyg.2012.00131
- Visual object categorization in the brain: What can we really learn from ERP peaks? DOI: 10.3389/fnhum.2011.00156
- Single-trial analyses: Why bother? DOI: 10.3389/fpsyg.2011.00322
- Robust statistics show no evidence for a relationship between fiber density and memory performance DOI: 10.1073/pnas.1109188108
- Reliability of ERP and single-trial analyses DOI: 10.1016/j.neuroimage.2011.06.052
- Quantifying the time course of visual object processing using ERPs: It's time to up the game DOI: 10.3389/fpsyg.2011.00107
- Modeling single-trial ERP reveals modulation of bottom-up face visual processing by top-down task constraints (in some subjects) DOI: 10.3389/fpsyg.2011.00137
- LIMO EEG: A toolbox for hierarchical linear modeling of electroencephalographic data DOI: 10.1155/2011/831409
- Vocal Attractiveness Increases by Averaging DOI: 10.1016/j.cub.2009.11.034
- Neural repetition suppression to identity is abolished by other-race faces DOI: 10.1073/pnas.1005751107
- Inverting faces elicits sensitivity to race on the N170 component: A cross-cultural study DOI: 10.1167/10.1.15
- Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task DOI: 10.3389/fpsyg.2010.00019
- Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise DOI: 10.1167/9.1.2
- How do amplitude spectra influence rapid animal detection? DOI: 10.1016/j.visres.2009.09.021
- Electrophysiological evidence for an early processing of human voices DOI: 10.1186/1471-2202-10-127
- Brain classification reveals the right cerebellum as the best biomarker of dyslexia DOI: 10.1186/1471-2202-10-67
- Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach DOI: 10.1186/1471-2202-10-114
- Time course and robustness of ERP object and face differences DOI: 10.1167/8.12.3
- Parametric study of EEG sensitivity to phase noise during face processing DOI: 10.1186/1471-2202-9-98
- Early interference of context congruence on object processing in rapid visual categorization of natural scenes DOI: 10.1167/8.13.11
- Single-trial EEG dynamics of object and face visual processing DOI: 10.1016/j.neuroimage.2007.02.052
- Processing scene context: Fast categorization and object interference DOI: 10.1016/j.visres.2007.09.013
- Limits of event-related potential differences in tracking object processing speed DOI: 10.1162/jocn.2007.19.8.1241
- Controlling interstimulus perceptual variance does not abolish N170 face sensitivity [1] DOI: 10.1038/nn0707-801
- Spatial scaling factors explain eccentricity effects on face ERPs DOI: 10.1167/5.10.1
- How long to get to the "gist" of real-world natural scenes? DOI: 10.1080/13506280444000553
- Spatiotemporal analyses of the N170 for human faces, animal faces and objects in natural scenes DOI: 10.1097/00001756-200412030-00009
- Processing of one, two or four natural scenes in humans: The limits of parallelism DOI: 10.1016/j.visres.2003.11.014
- Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes DOI: 10.1016/j.cogbrainres.2003.11.010
- How parallel is visual processing in the ventral pathway? DOI: 10.1016/j.tics.2004.06.003
- Comparing animal and face processing in the context of natural scenes using a fast categorization task DOI: 10.1016/j.neucom.2004.01.127
- Animal and human faces in natural scenes: How specific to human faces is the N170 ERP component? DOI: 10.1167/4.1.2
- Taking the MAX from neuronal responses DOI: 10.1016/S1364-6613(03)00023-8
- Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes DOI: 10.1167/3.6.5
- Parallel processing in high-level categorization of natural images DOI: 10.1038/nn866
- Visual Object Categorization in the Brain: What Can We Really Learn from ERP Peaks? DOI: http://dx.doi.org/10.3389/fnhum.2011.00156
- Taking the MAX from neuronal responses DOI: http://dx.doi.org/10.1016/s1364-6613(03)00023-8
- Spatiotemporal analyses of the N170 for human faces, animal faces and objects in natural scenes DOI: http://dx.doi.org/10.1097/00001756-200412030-00009
- Single-Trial Analyses: Why Bother? DOI: http://dx.doi.org/10.3389/fpsyg.2011.00322
- Reliability of ERP and single-trial analyses DOI: http://dx.doi.org/10.1016/j.neuroimage.2011.06.052
- Quantifying the Time Course of Visual Object Processing Using ERPs: It's Time to Up the Game DOI: http://dx.doi.org/10.3389/fpsyg.2011.00107
- Processing scene context: Fast categorization and object interference DOI: http://dx.doi.org/10.1016/j.visres.2007.09.013
- Processing of one, two or four natural scenes in humans: the limits of parallelism DOI: http://dx.doi.org/10.1016/j.visres.2003.11.014
- Parallel processing in high-level categorization of natural images DOI: http://dx.doi.org/10.1038/nn866
- LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data DOI: http://dx.doi.org/10.1155/2011/831409
- Improving standards in brain-behavior correlation analyses DOI: http://dx.doi.org/10.3389/fnhum.2012.00119
- How parallel is visual processing in the ventral pathway? DOI: http://dx.doi.org/10.1016/j.tics.2004.06.003
- How long to get to the “gist” of real-world natural scenes? DOI: http://dx.doi.org/10.1080/13506280444000553
- How do amplitude spectra influence rapid animal detection? DOI: http://dx.doi.org/10.1016/j.visres.2009.09.021
- Does Filtering Preclude Us from Studying ERP Time-Courses? DOI: http://dx.doi.org/10.3389/fpsyg.2012.00131
- Controlling interstimulus perceptual variance does not abolish N170 face sensitivity DOI: http://dx.doi.org/10.1038/nn0707-801
- Comparing animal and face processing in the context of natural scenes using a fast categorization task DOI: http://dx.doi.org/10.1016/j.neucom.2004.01.127
- Brain classification reveals the right cerebellum as the best biomarker of dyslexia DOI: http://dx.doi.org/10.1186/1471-2202-10-67
- A statistical framework for neuroimaging data analysis based on mutual information estimated via a Gaussian copula
- The Evolution of Data Sharing Practices in the Psychological Literature
- Beyond differences in means: robust graphical methods to compare two groups in neuroscience
- Single-subject analyses of magnetoencephalographic evoked responses to the acoustic properties of affective non-verbal vocalizations
- With age comes representational wisdom in social signals
- Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: a simulation study
- Eye coding mechanisms in early human face event-related potentials
- Controlling interstimulus perceptual variance does not abolish N170 face sensitivity
- Comparing animal and face processing in the context of natural scenes using a fast categorization task
- Does Filtering Preclude Us from Studying ERP Time-Courses?
- Early ERPs to faces: Aging, luminance, and individual differences
- Brain classification reveals the right cerebellum as the best biomarker of dyslexia
- The Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres
- Healthy aging delays the neural processing of face features relevant for behavior by 40 ms
- A few simple steps to improve the description of group results in neuroscience.
- Reaction times and other skewed distributions: problems with the mean and the median
- Reaction Times and other Skewed Distributions
- Using simulations to explore sampling distributions: an antidote to hasty and extravagant inferences
- Rating Norms Should be Calculated from Cumulative Link Mixed Effects Models
- Reaction times and other skewed distributions: problems with the mean and the median
- The Percentile Bootstrap: A Primer With Step-by-Step Instructions in R
- Promoting and supporting credibility in neuroscience
- The percentile bootstrap: a primer with step-by-step instructions in R
- Rating norms should be calculated from cumulative link mixed effects models
- An introduction to the bootstrap: a versatile method to make inferences by using data-driven simulations
- Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task
- Single-trial EEG dynamics of object and face visual processing
- Spatial scaling factors explain eccentricity effects on face ERPs
- Robust correlation analyses: False positive and power validation using a new open source matlab toolbox
- Early ERPs to faces and objects are driven by phase, not amplitude spectrum information: Evidence from parametric, test-retest, single-subject analyses
- Robust statistics show no evidence for a relationship between fiber density and memory performance
- Neural repetition suppression to identity is abolished by other-race faces
- Modeling single-trial ERP reveals modulation of bottom-up face visual processing by top-down task constraints (in some subjects)
- Inverting faces elicits sensitivity to race on the N170 component: A cross-cultural study
- Vocal Attractiveness Increases by Averaging
- Electrophysiological evidence for an early processing of human voices
- Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach
- Rapid visual categorization of natural scene contexts with equalized amplitude spectrum and increasing phase noise
- Early interference of context congruence on object processing in rapid visual categorization of natural scenes
- Parametric study of EEG sensitivity to phase noise during face processing
- Time course and robustness of ERP object and face differences
- Limits of event-related potential differences in tracking object processing speed
- Interaction of top-down and bottom-up processing in the fast visual analysis of natural scenes
- Animal and human faces in natural scenes: How specific to human faces is the N170 ERP component?
- Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes
- Tracing the Flow of Perceptual Features in an Algorithmic Brain Network.
- A robust and representative lower bound on object processing speed in humans.
- The time course of visual influences in letter recognition.
- Beyond differences in means: robust graphical methods to compare two groups in neuroscience
- A Guide to Robust Statistical Methods in Neuroscience
- Spatiotemporal analyses of the N170 for human faces, animal faces and objects in natural scenes
- How do amplitude spectra influence rapid animal detection?
- How long to get to the “gist” of real-world natural scenes?
- How parallel is visual processing in the ventral pathway?
- Improving standards in brain-behavior correlation analyses
- LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data
- Parallel processing in high-level categorization of natural images
- Processing of one, two or four natural scenes in humans: the limits of parallelism
- 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
- Reliability of ERP and single-trial analyses
- Single-Trial Analyses: Why Bother?
- Taking the MAX from neuronal responses
- Visual Object Categorization in the Brain: What Can We Really Learn from ERP Peaks?
- An Updated Guide to Robust Statistical Methods in Neuroscience
- Can Prediction Error Explain Predictability Effects on the N1 during Picture-Word Verification?
- Data-driven estimates of the reproducibility of univariate BWAS are biased.
- Using cluster-based permutation tests to estimate MEG/EEG onsets: how bad is it?
- A quantile shift approach to main effects and interactions in a 2-by-2 design