Author

Tanja Krumpe

University of TübingenORCID 0000-0001-7033-7088Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:electronic health records· 3 Research Focus:cell type annotation· 2 Approach:machine learning (classical)· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Organism:human· 1 Approach:statistical model· 1

Frequent Co-Authors

Peter H. GerjetsiD· 3 shared Wolfgang Rosenstiel· 3 shared Christian ScharingeriD· 2 shared Martin SpüleriD· 2 shared Martin SpÃ¼ler· 1 shared

Activity by Year

2018-2019 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 9 mutual co-author links

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3 shared papersNeuroscience

Peter H. GerjetsORCID

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3 papers

Recent·Most cited

NeuroscienceBrain-Computer InterfacesPeer-reviewedOct 2, 2019

Decision confidence: EEG correlates of confidence in different phases of a decision task

Tanja Krumpe, Peter H. Gerjets, Wolfgang Rosenstiel, Martin Spüler

This study identifies EEG correlates of decision confidence across different phases of a recognition memory task, showing confidence is most reflected during feedback processing.

8 citations

electronic health recordsstatistical model

𝕏 PostBluesky

NeurosciencebioRxivPreprintDec 20, 2018

Using a machine learning approach to complement group level statistics in experimental psychology: A case study to reveal different levels of inhibition in a modified Flanker Task

Tanja Krumpe, Christian Scharinger, Wolfgang Rosenstiel…·Martin SpÃ¼ler

This paper uses machine learning to analyze EEG data from a modified Flanker Task, revealing three levels of inhibitory control that group-level statistics miss.

1 citation

humanelectronic health records

𝕏 PostBluesky

NeuroscienceBiological PsychologyPeer-reviewedNov 1, 2018

Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning

Tanja Krumpe, Christian Scharinger, Wolfgang Rosenstiel…·Martin Spüler

This paper uses machine learning to analyze EEG data and finds distinct and shared neural correlates for the executive functions updating and inhibition.

8 citations

electronic health recordsmachine learning (classical)

𝕏 PostBluesky