Together with Simon van Gaal, I am co-PI in the Conscious Brain Lab. We use EEG, fMRI and psychophysics to investigate the neural mechanisms involved in consciousness, with special emphasis on the contribution of decision making (confidence, metacognition), perception, attention and working memory to conscious experience. My current line of research explores the impact of perceptual bias and criterion setting on putative correlates of concious and unconscious processing.
Selected recent publications
van Driel, J., Olivers, C. N. L., & Fahrenfort, J. J. (2021). High-pass filtering artifacts in multivariate classification of neural time series data. Journal of Neuroscience Methods, 352(2), 109080.
Fahrenfort, J. J., & van Gaal, S. (2020). Criteria for empirical theories of consciousness should focus on the explanatory power of mechanisms, not on functional equivalence. Cognitive Neuroscience, 72, 1–2.
Kloosterman, N. A., de Gee, J. W., Werkle-Bergner, M., Lindenberger, U., Garrett, D. D., & Fahrenfort, J. J. (2019). Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation. eLife, 8, 795.
Fahrenfort, J. J., van Driel, J., van Gaal, S., & Olivers, C. N. L. (2018). From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM). Frontiers in Neuroscience, 12.
Fahrenfort, J. J., van Leeuwen, J., Olivers, C. N. L., & Hogendoorn, H. (2017). Perceptual integration without conscious access.Proceedings of the National Academy of Sciences, 114(14), 3744–3749.
the Amsterdam Decoding and Modeling Toolbox
To support our own research and that of other research groups, I have developed a Matlab toolbox that allows one to easily perform multivariate analyses (MVPA) on EEG and MEG data, enabling one to determine and model the relationship between experimental conditions and multivariate brain data over time. We published a tutorial paper explaining how to perform decoding analyses and a book chapter explaining how to use forward encoding models using the toolbox. We have also recently published a paper investigating the dangers of high-pass filtering when applied to EEG/MEG in the context of MVPA, in which we introduce an alternative method called trial-masked robust detrending to ameliorate these problems.
You can download the toolbox and play around with the software. Update regularly, the toolbox is constantly being updated. If you use the toolbox, please cite our Frontiers paper when using backward decoding or my book chapter when using the forward encoding models.