Yaoda Xu

5.3k total citations · 1 hit paper
75 papers, 3.7k citations indexed

About

Yaoda Xu is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology. According to data from OpenAlex, Yaoda Xu has authored 75 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Cognitive Neuroscience, 13 papers in Computer Vision and Pattern Recognition and 7 papers in Experimental and Cognitive Psychology. Recurrent topics in Yaoda Xu's work include Visual perception and processing mechanisms (49 papers), Face Recognition and Perception (41 papers) and Neural dynamics and brain function (40 papers). Yaoda Xu is often cited by papers focused on Visual perception and processing mechanisms (49 papers), Face Recognition and Perception (41 papers) and Neural dynamics and brain function (40 papers). Yaoda Xu collaborates with scholars based in United States, Canada and South Korea. Yaoda Xu's co-authors include Marvin M. Chun, K. Bettencourt, Maryam Vaziri-Pashkam, Jonathan S. Cant, Jia Liu, Mary C. Potter, Alexander Pollatsek, Nancy Kanwisher, Suzanne Corkin and Manish Singh and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Yaoda Xu

73 papers receiving 3.6k citations

Hit Papers

align trajectories

What are hit papers?

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.

Dissociable neural mechanisms supporting visual short-term memory for objects

2005 759 citations Yaoda Xu, Marvin M. Chun profile →

Peers

Yaoda Xu

CN

ECP

CVPR

SP

DEP

Dwight J. Kravitz United States

Weiwei Zhang United States

Árni Kristjánsson Iceland

David Melcher Italy

Massimo Turatto Italy

Jens‐Max Hopf Germany

Gyula Kovács Germany

Yaffa Yeshurun Israel

Shaun P. Vecera United States

Martin Arguin Canada

Dwight J. Kravitz United States

Yaoda Xu

3.5k ×1.0

CN

499 ×0.9

ECP

425 ×1.3

CVPR

374 ×1.1

SP

274 ×0.9

DEP

Citations per year, relative to Yaoda Xu Yaoda Xu (= 1×) peers Dwight J. Kravitz

Countries citing papers authored by Yaoda Xu

Since Specialization

Citations

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

Fields of papers citing papers by Yaoda Xu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yaoda Xu. 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 Yaoda Xu. The network helps show where Yaoda Xu may publish in the future.

Co-authorship network of co-authors of Yaoda Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Yaoda Xu. A scholar is included among the top collaborators of Yaoda Xu 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 Yaoda Xu. Yaoda Xu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Xu, Yaoda. (2025). Transformed Visual Working Memory Representations in Human Occipitotemporal and Posterior Parietal Cortices. eNeuro. 12(7). ENEURO.0162–25.2025.

2.

Xu, Yaoda. (2024). The human posterior parietal cortices orthogonalize the representation of different streams of information concurrently coded in visual working memory. PLoS Biology. 22(11). e3002915–e3002915. 6 indexed citations

3.

Xu, Yaoda, et al.. (2024). Visual event boundaries trigger forgetting despite active maintenance in visual working memory. Journal of Vision. 24(9). 9–9. 1 indexed citations

4.

Xu, Yaoda. (2023). Parietal-driven visual working memory representation in occipito-temporal cortex. Current Biology. 33(20). 4516–4523.e5. 17 indexed citations

5.

Xu, Yaoda & Maryam Vaziri-Pashkam. (2023). Using DNNs to understand the primate vision: A shortcut or a distraction?. Behavioral and Brain Sciences. 46.

6.

Xu, Yaoda. (2023). Global object shape representations in the primate brain. Trends in Cognitive Sciences. 27(3). 210–211. 2 indexed citations

7.

Xu, Yaoda, et al.. (2023). Comparing the Dominance of Color and Form Information across the Human Ventral Visual Pathway and Convolutional Neural Networks. Journal of Cognitive Neuroscience. 35(5). 816–840. 1 indexed citations

8.

Mocz, Viola, Maryam Vaziri-Pashkam, Marvin M. Chun, & Yaoda Xu. (2022). Predicting Identity-Preserving Object Transformations in Human Posterior Parietal Cortex and Convolutional Neural Networks. Journal of Cognitive Neuroscience. 34(12). 2406–2435. 5 indexed citations

9.

Tang, Kevin, et al.. (2022). The contribution of object identity and configuration to scene representation in convolutional neural networks. PLoS ONE. 17(6). e0270667–e0270667. 3 indexed citations

10.

Xu, Yaoda. (2021). Towards a better understanding of information storage in visual working memory. Visual Cognition. 29(7). 437–445. 8 indexed citations

11.

Xu, Yaoda & Maryam Vaziri-Pashkam. (2021). Limits to visual representational correspondence between convolutional neural networks and the human brain. Nature Communications. 12(1). 2065–2065. 84 indexed citations

12.

Xu, Yaoda & Maryam Vaziri-Pashkam. (2021). Examining the Coding Strength of Object Identity and Nonidentity Features in Human Occipito-Temporal Cortex and Convolutional Neural Networks. Journal of Neuroscience. 41(19). 4234–4252. 18 indexed citations

13.

Xu, Yaoda, et al.. (2021). Joint representation of color and form in convolutional neural networks: A stimulus-rich network perspective. PLoS ONE. 16(6). e0253442–e0253442. 8 indexed citations

14.

Mocz, Viola, Maryam Vaziri-Pashkam, Marvin M. Chun, & Yaoda Xu. (2021). Predicting identity-preserving object transformations across the human ventral visual stream. Journal of Neuroscience. 41(35). JN–RM. 4 indexed citations

15.

Xu, Yaoda. (2020). Revisit once more the sensory storage account of visual working memory. Visual Cognition. 28(5-8). 433–446. 34 indexed citations

16.

Vaziri-Pashkam, Maryam & Yaoda Xu. (2017). Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations. Journal of Neuroscience. 37(36). 8767–8782. 70 indexed citations

17.

Xu, Yaoda, et al.. (2014). Decoding visual object representation in human parietal cortex. Journal of Vision. 14(10). 1307–1307. 1 indexed citations

18.

Cant, Jonathan S. & Yaoda Xu. (2014). The Impact of Density and Ratio on Object-Ensemble Representation in Human Anterior-Medial Ventral Visual Cortex. Cerebral Cortex. 25(11). 4226–4239. 26 indexed citations

19.

Bettencourt, K. & Yaoda Xu. (2011). Retinotopically defined parietal regions and their relationship to parietal areas involved in object individuation and identification. Journal of Vision. 11(11). 245–245. 1 indexed citations

20.

Xu, Yaoda. (2010). The Neural Fate of Task-Irrelevant Features in Object-Based Processing. Journal of Neuroscience. 30(42). 14020–14028. 78 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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