Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Peter Dayan'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 Peter Dayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Dayan more than expected).
This network shows the impact of papers produced by Peter Dayan. 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 Peter Dayan. The network helps show where Peter Dayan may publish in the future.
Co-authorship network of co-authors of Peter Dayan
This figure shows the co-authorship network connecting the top 25 collaborators of Peter Dayan.
A scholar is included among the top collaborators of Peter Dayan 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 Peter Dayan. Peter Dayan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Stojić, Hrvoje, Jacob Lund Orquin, Peter Dayan, Raymond J. Dolan, & Maarten Speekenbrink. (2020). Uncertainty in learning, choice, and visual fixation. Proceedings of the National Academy of Sciences. 117(6). 3291–3300.21 indexed citations
Guez, Arthur, Nicolas Heess, David Silver, & Peter Dayan. (2014). Bayes-Adaptive Simulation-based Search with Value Function Approximation. UCL Discovery (University College London). 27. 451–459.7 indexed citations
Niv, Yael, Nathaniel D. Daw, & Peter Dayan. (2005). How fast to work: Response vigor, motivation and tonic dopamine. MPG.PuRe (Max Planck Society). 18. 1019–1026.67 indexed citations
16.
Yu, Angela J. & Peter Dayan. (2004). Inference, Attention, and Decision in a Bayesian Neural Architecture. TUbilio (Technical University of Darmstadt). 17. 1577–1584.58 indexed citations
Káli, Szabolcs & Peter Dayan. (2002). Replay, Repair and Consolidation. UCL Discovery (University College London). 15. 19–26.2 indexed citations
19.
Dayan, Peter. (1996). A Hierarchical Model of Visual Rivalry. MPG.PuRe (Max Planck Society). 48–54.2 indexed citations
20.
Schraudolph, Nicol N., Peter Dayan, & Terrence J. Sejnowski. (1993). Temporal Difference Learning of Position Evaluation in the Game of Go. MPG.PuRe (Max Planck Society). 6. 817–824.61 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.