Zaifeng Gao

2.1k total citations · 1 hit paper
76 papers, 1.3k citations indexed

About

Zaifeng Gao is a scholar working on Cognitive Neuroscience, Social Psychology and Experimental and Cognitive Psychology. According to data from OpenAlex, Zaifeng Gao has authored 76 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Cognitive Neuroscience, 35 papers in Social Psychology and 10 papers in Experimental and Cognitive Psychology. Recurrent topics in Zaifeng Gao's work include Neural and Behavioral Psychology Studies (42 papers), Visual perception and processing mechanisms (17 papers) and Action Observation and Synchronization (16 papers). Zaifeng Gao is often cited by papers focused on Neural and Behavioral Psychology Studies (42 papers), Visual perception and processing mechanisms (17 papers) and Action Observation and Synchronization (16 papers). Zaifeng Gao collaborates with scholars based in China, United States and Israel. Zaifeng Gao's co-authors include Mowei Shen, Shlomo Bentin, Rende Shui, Jun Yin, Xiaowei Ding, Liezhong Ge, Wei Xu, Marvin J. Dainoff, Fan Wu and Jie Li and has published in prestigious journals such as PLoS ONE, Child Development and Scientific Reports.

In The Last Decade

Zaifeng Gao

73 papers receiving 1.3k 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.

Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI

2022 136 citations Marvin J. Dainoff, Liezhong Ge et al. profile →

Peers

Zaifeng Gao

CN

SP

ECP

DEP

HI

Jari Kätsyri Finland

Markus Huff Germany

Jelmer P. Borst Netherlands

Joshua R. de Leeuw United States

Wilma Bainbridge United States

Olivier Oullier France

Tim Draws Netherlands

Su‐Ling Yeh Taiwan

Mikko Salminen Finland

Frouke Hermens United Kingdom

Jari Kätsyri Finland

Zaifeng Gao

695 ×0.7

CN

463 ×1.1

SP

384 ×1.8

ECP

101 ×0.7

DEP

153 ×1.3

HI

Citations per year, relative to Zaifeng Gao Zaifeng Gao (= 1×) peers Jari Kätsyri

Countries citing papers authored by Zaifeng Gao

Since Specialization

Citations

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

Fields of papers citing papers by Zaifeng Gao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zaifeng Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Zaifeng Gao. A scholar is included among the top collaborators of Zaifeng Gao 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 Zaifeng Gao. Zaifeng Gao 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, Wei, Zaifeng Gao, & Marvin J. Dainoff. (2026). An HCAI Methodological Framework: Putting it Into Action to Enable Human-Centered AI. IEEE Transactions on Human-Machine Systems. 56(1). 78–94. 1 indexed citations

2.

Gao, Qi, et al.. (2024). Trust calibration through perceptual and predictive information of the external context in autonomous vehicle. Transportation Research Part F Traffic Psychology and Behaviour. 107. 537–548. 1 indexed citations

3.

Ding, Xiaowei, et al.. (2024). Selectively maintaining an object’s feature in visual working memory: A comparison between highly discriminable and fine-grained features. Memory & Cognition. 53(3). 853–868.

4.

Gao, Qi, et al.. (2024). Working memory capacity for biological motion: a virtual reality examination. Current Psychology. 43(19). 17291–17299. 1 indexed citations

5.

Shen, Mowei, et al.. (2024). Involuntary and voluntary processes compete for entering the focus of attention of working memory.. Journal of Experimental Psychology Learning Memory and Cognition. 51(3). 337–349. 1 indexed citations

6.

Gao, Zaifeng & Mowei Shen. (2024). Unraveling the binding problem in working memory: insights from the hierarchical binding model. Cognitive Processing. 25(S1). 97–104.

7.

Xu, Wei & Zaifeng Gao. (2024). An Intelligent Sociotechnical Systems (iSTS) Framework: Enabling a Hierarchical Human-Centered AI (hHCAI) Approach. 6(1). 31–46. 6 indexed citations

8.

Xu, Wei, Zaifeng Gao, & Liezhong Ge. (2023). New research paradigms and agenda of human factors science in the intelligence era. Acta Psychologica Sinica. 56(3). 363–363. 3 indexed citations

9.

Guo, Yang, et al.. (2022). Is the social chunking of agent actions in working memory resource-demanding?. Cognition. 229. 105249–105249. 4 indexed citations

10.

Zhang, Delin, Yi Shen, Zhiyun Chen, et al.. (2022). Emotion recognition dysfunction after anesthesia and cardiac surgery. Frontiers in Psychology. 13. 1001493–1001493. 1 indexed citations

11.

Gao, Zaifeng, et al.. (2022). Diverting the focus of attention in working memory through a perceptual task.. Journal of Experimental Psychology Learning Memory and Cognition. 48(6). 876–905. 5 indexed citations

12.

Guo, Yang, et al.. (2020). Visual working-memory capacity load does not modulate distractor processing. Attention Perception & Psychophysics. 82(7). 3291–3313. 7 indexed citations

13.

Tian, Ye, et al.. (2019). Relation Between Working Memory Capacity of Biological Movements and Fluid Intelligence. Frontiers in Psychology. 10. 2313–2313. 4 indexed citations

14.

Huang, Jian, et al.. (2016). Holding Biological Motion in Working Memory: An fMRI Study. Frontiers in Human Neuroscience. 10. 251–251. 31 indexed citations

15.

Ding, Xiaowei, et al.. (2015). Binding biological motion and visual features in working memory.. Journal of Experimental Psychology Human Perception & Performance. 41(3). 850–865. 24 indexed citations

16.

Gao, Zaifeng, Ye Tian, Mowei Shen, & Anat Perry. (2015). Working memory capacity of biological movements predicts empathy traits. Psychonomic Bulletin & Review. 23(2). 468–475. 33 indexed citations

17.

Gao, Zaifeng, et al.. (2013). Coarse-to-Fine Construction for High-Resolution Representation in Visual Working Memory. PLoS ONE. 8(2). e57913–e57913. 32 indexed citations

18.

Gao, Zaifeng, et al.. (2012). A magnetoencephalographic study of face processing: M170, gamma‐band oscillations and source localization. Human Brain Mapping. 34(8). 1783–1795. 50 indexed citations

19.

Zhou, Jifan, Jun Yin, Tong Chen, et al.. (2011). Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory. PLoS ONE. 6(9). e23873–e23873. 15 indexed citations

20.

Gao, Zaifeng, et al.. (2011). Tracking object number or information load in visual working memory: Revisiting the cognitive implication of contralateral delay activity. Biological Psychology. 87(2). 296–302. 39 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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