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.
Uncertainty, Neuromodulation, and Attention
20051.2k citationsAngela J. Yu, Peter Dayanprofile →
Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration
This map shows the geographic impact of Angela J. Yu'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 Angela J. Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Angela J. Yu more than expected).
This network shows the impact of papers produced by Angela J. Yu. 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 Angela J. Yu. The network helps show where Angela J. Yu may publish in the future.
Co-authorship network of co-authors of Angela J. Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Angela J. Yu.
A scholar is included among the top collaborators of Angela J. Yu 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 Angela J. Yu. Angela J. Yu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yu, Angela J., et al.. (2018). Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task. TUbilio (Technical University of Darmstadt). 31. 5176–5185.2 indexed citations
Zhang, Shunan, et al.. (2015). A Bayesian hierarchical model of local-global processing: Visual crowding as a case-study. TUbilio (Technical University of Darmstadt).2 indexed citations
Zhang, Shunan, He Huang, & Angela J. Yu. (2014). Sequential effects: A Bayesian analysis of prior bias on reaction time and behavioral choice. Cognitive Science. 36(36).16 indexed citations
Zhang, Shunan & Angela J. Yu. (2013). Cheap but Clever: Human Active Learning in a Bandit Setting. TUbilio (Technical University of Darmstadt).13 indexed citations
12.
Zhang, Shunan & Angela J. Yu. (2013). Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting. TUbilio (Technical University of Darmstadt). 26. 2607–2615.35 indexed citations
13.
Shenoy, Pradeep & Angela J. Yu. (2012). Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making. TUbilio (Technical University of Darmstadt). 25. 2123–2131.8 indexed citations
Shenoy, Pradeep, Angela J. Yu, & Rajesh P. N. Rao. (2010). A rational decision making framework for inhibitory control. TUbilio (Technical University of Darmstadt). 23. 2146–2154.23 indexed citations
16.
Frazier, Peter I. & Angela J. Yu. (2007). Sequential Hypothesis Testing under Stochastic Deadlines. TUbilio (Technical University of Darmstadt). 20. 465–472.69 indexed citations
Dayan, Peter & Angela J. Yu. (2005). Norepinephrine and Neural Interrupts. TUbilio (Technical University of Darmstadt). 18. 243–250.7 indexed citations
19.
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
20.
Dayan, Peter & Angela J. Yu. (2002). Expected and Unexpected Uncertainty: ACh and NE in the Neocortex. TUbilio (Technical University of Darmstadt). 15. 173–180.74 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.