Matthias Guggenmos

1.3k total citations
27 papers, 776 citations indexed

About

Matthias Guggenmos is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matthias Guggenmos has authored 27 papers receiving a total of 776 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cognitive Neuroscience, 3 papers in Cellular and Molecular Neuroscience and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matthias Guggenmos's work include Neural dynamics and brain function (13 papers), Neural and Behavioral Psychology Studies (9 papers) and Visual perception and processing mechanisms (8 papers). Matthias Guggenmos is often cited by papers focused on Neural dynamics and brain function (13 papers), Neural and Behavioral Psychology Studies (9 papers) and Visual perception and processing mechanisms (8 papers). Matthias Guggenmos collaborates with scholars based in Germany, United States and Switzerland. Matthias Guggenmos's co-authors include Philipp Sterzer, Radoslaw Martin Cichy, Gregor Wilbertz, Martin N. Hebart, Andreas Heinz, Maria Garbusow, Miriam Sebold, Michael N. Smolka, U. Zimmermann and Christian Sommer and has published in prestigious journals such as Journal of Neuroscience, NeuroImage and Scientific Reports.

In The Last Decade

Matthias Guggenmos

26 papers receiving 768 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matthias Guggenmos Germany 15 566 157 89 69 58 27 776
Archy O. de Berker United Kingdom 13 746 1.3× 148 0.9× 93 1.0× 32 0.5× 67 1.2× 15 1.0k
Tianzhen Chen China 18 326 0.6× 125 0.8× 127 1.4× 42 0.6× 84 1.4× 56 759
Aneta Brzezicka Poland 18 763 1.3× 239 1.5× 79 0.9× 37 0.5× 57 1.0× 49 1.0k
Thomas E. Hazy United States 11 725 1.3× 151 1.0× 140 1.6× 87 1.3× 66 1.1× 12 889
Márk Molnár Hungary 17 638 1.1× 100 0.6× 71 0.8× 24 0.3× 61 1.1× 31 737
Vincent Valton United Kingdom 13 349 0.6× 193 1.2× 84 0.9× 18 0.3× 84 1.4× 20 629
Diankun Gong China 17 583 1.0× 202 1.3× 47 0.5× 51 0.7× 60 1.0× 39 778
Heiner Stuke Germany 14 396 0.7× 167 1.1× 96 1.1× 15 0.2× 121 2.1× 34 660
Patrick H. Khader Germany 18 845 1.5× 115 0.7× 73 0.8× 36 0.5× 58 1.0× 29 1.0k
Todd D. Watson United States 13 683 1.2× 99 0.6× 67 0.8× 20 0.3× 60 1.0× 20 920

Countries citing papers authored by Matthias Guggenmos

Since Specialization
Citations

This map shows the geographic impact of Matthias Guggenmos's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthias Guggenmos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Guggenmos more than expected).

Fields of papers citing papers by Matthias Guggenmos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthias Guggenmos. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthias Guggenmos. The network helps show where Matthias Guggenmos may publish in the future.

Co-authorship network of co-authors of Matthias Guggenmos

This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Guggenmos. A scholar is included among the top collaborators of Matthias Guggenmos based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthias Guggenmos. Matthias Guggenmos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Guggenmos, Matthias. (2022). Reverse engineering of metacognition. eLife. 11. 14 indexed citations
3.
Sterzer, Philipp, et al.. (2022). The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback. PLoS Computational Biology. 18(10). e1010580–e1010580. 8 indexed citations
4.
Guggenmos, Matthias. (2021). Measuring metacognitive performance: type 1 performance dependence and test-retest reliability. Neuroscience of Consciousness. 2021(1). niab040–niab040. 32 indexed citations
5.
Rothkirch, Marcus, et al.. (2020). Unreliable feedback deteriorates information processing in primary visual cortex. NeuroImage. 214. 116701–116701. 1 indexed citations
6.
Guggenmos, Matthias, Katharina Schmack, Ilya M. Veer, et al.. (2020). A multimodal neuroimaging classifier for alcohol dependence. Scientific Reports. 10(1). 298–298. 23 indexed citations
7.
Gayet, Surya, Matthias Guggenmos, Thomas B. Christophel, et al.. (2020). No evidence for mnemonic modulation of interocularly suppressed visual input. NeuroImage. 215. 116801–116801. 7 indexed citations
8.
Stuke, Heiner, et al.. (2019). Sustained effects of corrupted feedback on perceptual inference. Scientific Reports. 9(1). 5537–5537. 1 indexed citations
9.
Sekutowicz, Maria, Matthias Guggenmos, Sören Kuitunen‐Paul, et al.. (2019). Neural Response Patterns During Pavlovian-to-Instrumental Transfer Predict Alcohol Relapse and Young Adult Drinking. Biological Psychiatry. 86(11). 857–863. 21 indexed citations
10.
Guggenmos, Matthias, Philipp Sterzer, & Radoslaw Martin Cichy. (2018). Multivariate pattern analysis for MEG: A comparison of dissimilarity measures. NeuroImage. 173. 434–447. 100 indexed citations
11.
Guggenmos, Matthias, Katharina Schmack, Maria Sekutowicz, et al.. (2017). Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Translational Psychiatry. 7(12). 1279–1279. 59 indexed citations
12.
Sebold, Miriam, Stephan Nebe, Maria Garbusow, et al.. (2017). When Habits Are Dangerous: Alcohol Expectancies and Habitual Decision Making Predict Relapse in Alcohol Dependence. Biological Psychiatry. 82(11). 847–856. 111 indexed citations
13.
Gayet, Surya, Matthias Guggenmos, Thomas B. Christophel, et al.. (2017). Visual Working Memory Enhances the Neural Response to Matching Visual Input. Journal of Neuroscience. 37(28). 6638–6647. 49 indexed citations
14.
Wilbertz, Gregor, et al.. (2017). Combined fMRI- and eye movement-based decoding of bistable plaid motion perception. NeuroImage. 171. 190–198. 14 indexed citations
15.
Guggenmos, Matthias, Katharina Schmack, & Philipp Sterzer. (2016). WeiRD - a fast and performant multivoxel pattern classifier. 15. 1–4. 3 indexed citations
16.
Guggenmos, Matthias, et al.. (2015). Chances and limits of single-station seismic event clustering by unsupervised pattern recognition. Geophysical Journal International. 201(3). 1801–1813. 31 indexed citations
17.
Guggenmos, Matthias, Volker Thoma, John­–Dylan Haynes, et al.. (2015). Spatial attention enhances object coding in local and distributed representations of the lateral occipital complex. NeuroImage. 116. 149–157. 9 indexed citations
18.
Guggenmos, Matthias, Volker Thoma, Radoslaw Martin Cichy, et al.. (2014). Non-holistic coding of objects in lateral occipital complex with and without attention. NeuroImage. 107. 356–363. 14 indexed citations
19.
Seymour, Kiley, et al.. (2013). Altered Contextual Modulation of Primary Visual Cortex Responses in Schizophrenia. Neuropsychopharmacology. 38(13). 2607–2612. 50 indexed citations
20.
Ostwald, Dirk, Bernhard Spitzer, Matthias Guggenmos, et al.. (2012). Evidence for neural encoding of Bayesian surprise in human somatosensation. NeuroImage. 62(1). 177–188. 82 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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