Paul M. Bays

10.9k total citations · 3 hit papers
90 papers, 7.1k citations indexed

About

Paul M. Bays is a scholar working on Cognitive Neuroscience, Social Psychology and Experimental and Cognitive Psychology. According to data from OpenAlex, Paul M. Bays has authored 90 papers receiving a total of 7.1k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Cognitive Neuroscience, 11 papers in Social Psychology and 9 papers in Experimental and Cognitive Psychology. Recurrent topics in Paul M. Bays's work include Neural and Behavioral Psychology Studies (53 papers), Visual perception and processing mechanisms (49 papers) and Neural dynamics and brain function (41 papers). Paul M. Bays is often cited by papers focused on Neural and Behavioral Psychology Studies (53 papers), Visual perception and processing mechanisms (49 papers) and Neural dynamics and brain function (41 papers). Paul M. Bays collaborates with scholars based in United Kingdom, United States and Australia. Paul M. Bays's co-authors include Masud Husain, Daniel M. Wolpert, Raquel Catalão, Wei Ji, Sebastian Schneegans, Sukhwinder S. Shergill, Chris Frith, J. Randall Flanagan, Nikos Gorgoraptis and Louise Marshall and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Paul M. Bays

86 papers receiving 7.0k citations

Hit Papers

Dynamic Shifts of Limited... 2008 2026 2014 2020 2008 2014 2009 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Paul M. Bays United Kingdom 38 6.4k 1.3k 1.0k 501 402 90 7.1k
Febo Cincotti Italy 57 7.8k 1.2× 968 0.8× 746 0.7× 419 0.8× 837 2.1× 249 9.0k
Giorgio Ganis United States 32 4.1k 0.6× 1.7k 1.3× 1.6k 1.5× 277 0.6× 200 0.5× 69 5.9k
Francesco Di Russo Italy 44 6.2k 1.0× 1.1k 0.9× 1.5k 1.5× 419 0.8× 302 0.8× 157 7.5k
Jeffrey D. Schall United States 60 11.8k 1.9× 673 0.5× 880 0.8× 395 0.8× 168 0.4× 151 12.7k
Jay Pratt Canada 46 6.5k 1.0× 1.3k 1.0× 2.1k 2.0× 249 0.5× 263 0.7× 290 8.8k
Vilayanur S. Ramachandran United States 37 4.7k 0.7× 2.6k 2.1× 2.4k 2.3× 621 1.2× 218 0.5× 122 7.3k
Floris P. de Lange Netherlands 60 10.0k 1.6× 2.2k 1.7× 2.1k 2.0× 935 1.9× 294 0.7× 177 12.1k
Stefan Debener Germany 59 12.2k 1.9× 1.1k 0.8× 2.8k 2.7× 1.0k 2.1× 621 1.5× 179 13.9k
Frank Pollick United Kingdom 34 2.9k 0.5× 2.1k 1.6× 1.1k 1.1× 245 0.5× 248 0.6× 170 4.6k
René Marois United States 44 6.5k 1.0× 1.0k 0.8× 1.4k 1.3× 368 0.7× 78 0.2× 90 8.0k

Countries citing papers authored by Paul M. Bays

Since Specialization
Citations

This map shows the geographic impact of Paul M. Bays'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 Paul M. Bays with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul M. Bays more than expected).

Fields of papers citing papers by Paul M. Bays

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul M. Bays. 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 Paul M. Bays. The network helps show where Paul M. Bays may publish in the future.

Co-authorship network of co-authors of Paul M. Bays

This figure shows the co-authorship network connecting the top 25 collaborators of Paul M. Bays. A scholar is included among the top collaborators of Paul M. Bays 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 Paul M. Bays. Paul M. Bays 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.
Valè, Nicola, et al.. (2025). Divisive attenuation based on noisy sensorimotor predictions accounts for excess variability in self-touch. Journal of Neurophysiology. 134(1). 407–428. 1 indexed citations
2.
Harrison, William J., Paul M. Bays, & Reuben Rideaux. (2023). Neural tuning instantiates prior expectations in the human visual system. Nature Communications. 14(1). 5320–5320. 10 indexed citations
3.
Tomić, Ivan & Paul M. Bays. (2023). A dynamic neural resource model bridges sensory and working memory. eLife. 12. 2 indexed citations
4.
Taylor, Robert, Ivan Tomić, David Aagten‐Murphy, & Paul M. Bays. (2022). Working memory is updated by reallocation of resources from obsolete to new items. Attention Perception & Psychophysics. 85(5). 1437–1451. 5 indexed citations
5.
Schneegans, Sebastian, Robert Taylor, & Paul M. Bays. (2020). Stochastic sampling provides a unifying account of visual working memory limits. Proceedings of the National Academy of Sciences. 117(34). 20959–20968. 47 indexed citations
6.
Bays, Paul M.. (2018). Failure of self-consistency in the discrete resource model of visual working memory. Cognitive Psychology. 105. 1–8. 12 indexed citations
7.
Taylor, Robert & Paul M. Bays. (2018). Efficient Coding in Visual Working Memory Accounts for Stimulus-Specific Variations in Recall. Journal of Neuroscience. 38(32). 7132–7142. 29 indexed citations
8.
Schneegans, Sebastian & Paul M. Bays. (2018). Drift in Neural Population Activity Causes Working Memory to Deteriorate Over Time. Journal of Neuroscience. 38(21). 4859–4869. 44 indexed citations
9.
Schneegans, Sebastian & Paul M. Bays. (2018). New perspectives on binding in visual working memory. British Journal of Psychology. 110(2). 207–244. 47 indexed citations
10.
Bays, Paul M.. (2017). Reassessing the Evidence for Capacity Limits in Neural Signals Related to Working Memory. Cerebral Cortex. 28(4). 1432–1438. 17 indexed citations
11.
Schneegans, Sebastian & Paul M. Bays. (2017). Restoration of fMRI Decodability Does Not Imply Latent Working Memory States. Journal of Cognitive Neuroscience. 29(12). 1977–1994. 35 indexed citations
12.
Richter, Franziska R., Rose A. Cooper, Paul M. Bays, & Jon S. Simons. (2016). Distinct neural mechanisms underlie the success, precision, and vividness of episodic memory. eLife. 5. 153 indexed citations
13.
Wijdenes, Leonie Oostwoud, Louise Marshall, & Paul M. Bays. (2015). Evidence for Optimal Integration of Visual Feature Representations across Saccades. Journal of Neuroscience. 35(28). 10146–10153. 59 indexed citations
14.
Marshall, Louise & Paul M. Bays. (2013). Obligatory encoding of task-irrelevant features depletes working memory resources. Journal of Vision. 13(2). 21–21. 59 indexed citations
15.
Marshall, Louise & Paul M. Bays. (2012). Obligatory encoding of task-irrelevant features depletes working memory resources. Journal of Vision. 12(9). 853–853. 5 indexed citations
16.
Zokaei, Nahid, Nikos Gorgoraptis, Bahador Bahrami, Paul M. Bays, & Masud Husain. (2011). Visual Working Memory for Motion Sequences. Journal of Vision. 11(11). 1263–1263. 2 indexed citations
17.
Bays, Paul M.. (2010). Precision versus capacity of working memory in schizophrenic and healthy individuals. UCL Discovery (University College London). 1 indexed citations
18.
Bays, Paul M. & Masud Husain. (2008). Dynamic Shifts of Limited Working Memory Resources in Human Vision. Science. 321(5890). 851–854. 813 indexed citations breakdown →
19.
Voss, Martin, Paul M. Bays, John C. Rothwell, & Daniel M. Wolpert. (2007). An improvement in perception of self-generated tactile stimuli following theta-burst stimulation of primary motor cortex. Neuropsychologia. 45(12). 2712–2717. 47 indexed citations
20.
Bays, Paul M. & Daniel M. Wolpert. (2006). Computational principles of sensorimotor control that minimize uncertainty and variability. The Journal of Physiology. 578(2). 387–396. 270 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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