Rachel Wu

2.0k total citations
69 papers, 1.3k citations indexed

About

Rachel Wu is a scholar working on Developmental and Educational Psychology, Cognitive Neuroscience and Experimental and Cognitive Psychology. According to data from OpenAlex, Rachel Wu has authored 69 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Developmental and Educational Psychology, 27 papers in Cognitive Neuroscience and 20 papers in Experimental and Cognitive Psychology. Recurrent topics in Rachel Wu's work include Child and Animal Learning Development (21 papers), Face Recognition and Perception (14 papers) and Neural and Behavioral Psychology Studies (13 papers). Rachel Wu is often cited by papers focused on Child and Animal Learning Development (21 papers), Face Recognition and Perception (14 papers) and Neural and Behavioral Psychology Studies (13 papers). Rachel Wu collaborates with scholars based in United States, United Kingdom and Canada. Rachel Wu's co-authors include Natasha Z. Kirkham, Tim J. Smith, Martin Eimer, Daniel C. Richardson, Kristen Tummeltshammer, Alison Gopnik, Robert Terkeltaub, S. Bookbinder, Frederick T. Murphy and John S. Sundy and has published in prestigious journals such as JAMA, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Rachel Wu

60 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rachel Wu United States 19 485 408 222 170 167 69 1.3k
Laura Hokkanen Finland 29 456 0.9× 200 0.5× 153 0.7× 142 0.8× 64 0.4× 116 1.9k
Emma Chapman United Kingdom 15 597 1.2× 226 0.6× 133 0.6× 126 0.7× 140 0.8× 22 1.3k
Esther Yuet Ying Lau Hong Kong 21 452 0.9× 24 0.1× 576 2.6× 142 0.8× 172 1.0× 99 1.5k
Laura Bell United Kingdom 17 373 0.8× 37 0.1× 97 0.4× 71 0.4× 111 0.7× 46 1.7k
Dustin Baker United States 20 251 0.5× 77 0.2× 126 0.6× 166 1.0× 308 1.8× 36 1.6k
Fritz Renner Germany 23 270 0.6× 137 0.3× 691 3.1× 19 0.1× 261 1.6× 63 1.8k
Patrick Finn United States 21 228 0.5× 293 0.7× 319 1.4× 88 0.5× 29 0.2× 61 1.2k
Vinaya Manchaiah United States 28 1.8k 3.7× 423 1.0× 106 0.5× 56 0.3× 138 0.8× 243 3.2k
Lin Du China 22 466 1.0× 216 0.5× 11 0.0× 180 1.1× 37 0.2× 76 1.4k
John Done United Kingdom 12 378 0.8× 42 0.1× 154 0.7× 22 0.1× 201 1.2× 21 1.3k

Countries citing papers authored by Rachel Wu

Since Specialization
Citations

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

Fields of papers citing papers by Rachel Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rachel Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Rachel Wu. A scholar is included among the top collaborators of Rachel Wu 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 Rachel Wu. Rachel Wu 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.
Wu, Rachel, et al.. (2025). Development and validation of a standardized emotional music database based on multidimensional affective ratings. Frontiers in Psychology. 16. 1695114–1695114.
2.
Wu, Rachel, et al.. (2024). Interrupted Learning across the Lifespan. Human Development. 69(2). 1–14. 1 indexed citations
3.
Hill, Nikki L., Sakshi Bhargava, Emily Bratlee‐Whitaker, et al.. (2024). Just as expected? Older adults’ aging expectations are associated with subjective cognition. Aging & Mental Health. 29(3). 1–8. 1 indexed citations
4.
Kürüm, Esra, et al.. (2023). Impact of community-based technology training with low-income older adults. Aging & Mental Health. 28(4). 638–645. 6 indexed citations
5.
Kürüm, Esra, et al.. (2023). One-year cognitive outcomes from a multiple real-world skill learning intervention with older adults. Aging & Mental Health. 27(11). 2134–2143. 7 indexed citations
6.
Wu, Rachel & Ari B. Friedman. (2023). EMERGENCY DEPARTMENT PRACTICE PATTERNS FOR OLDER ADULTS WITH ABDOMINAL PAIN. Innovation in Aging. 7(Supplement_1). 928–929.
7.
Cheung, Cecilia, et al.. (2021). Cognitive and metacognitive, motivational, and resource considerations for learning new skills across the lifespan. Wiley Interdisciplinary Reviews Cognitive Science. 13(2). e1585–e1585. 22 indexed citations
8.
Wu, Rachel, et al.. (2021). Learning as an Important Privilege: A Life Span Perspective with Implications for Successful Aging. Human Development. 65(1). 51–64. 12 indexed citations
9.
Wu, Rachel, et al.. (2020). Categorization in infancy based on novelty and co-occurrence. Infant Behavior and Development. 62. 101510–101510.
10.
Wu, Rachel, et al.. (2020). Neural target selection as a marker of real‐world familiarity during search for perceptually distinct objects. European Journal of Neuroscience. 53(5). 1517–1532. 1 indexed citations
11.
Bergen, Geertje van, Monique Flecken, & Rachel Wu. (2019). Rapid target selection of object categories based on verbs: Implications for language‐categorization interactions. Psychophysiology. 56(9). e13395–e13395. 1 indexed citations
12.
Wu, Rachel, Ting Qian, & Richard Ν. Aslin. (2019). No Evidence That Abstract Structure Learning Disrupts Novel-Event Learning in 8- to 11-Month-Olds. Frontiers in Psychology. 10. 498–498. 2 indexed citations
13.
Wu, Rachel & Jiaying Zhao. (2017). Prior Knowledge of Object Associations Shapes Attentional Templates and Information Acquisition. Frontiers in Psychology. 8. 843–843. 9 indexed citations
14.
Wu, Rachel, et al.. (2014). Item and category-based attentional control during search for real-world objects: Can you find the pants among the pans?. Journal of Experimental Psychology Human Perception & Performance. 40(4). 1283–1288. 43 indexed citations
15.
Wu, Rachel, Kristen Tummeltshammer, Teodora Gliga, & Natasha Z. Kirkham. (2014). Ostensive signals support learning from novel attention cues during infancy. Frontiers in Psychology. 5. 251–251. 36 indexed citations
16.
Wu, Rachel, et al.. (2013). Rapid guidance of visual search by object categories.. Journal of Experimental Psychology Human Perception & Performance. 40(1). 50–60. 37 indexed citations
17.
Tummeltshammer, Kristen, Rachel Wu, & Natasha Z. Kirkham. (2013). 8-month-olds Know Which Face is Reliable. Cognitive Science. 35(35). 1 indexed citations
18.
Karmiloff‐Smith, Annette, Hannah Broadbent, Emily K. Farran, et al.. (2012). Social Cognition in Williams Syndrome: Genotype/Phenotype Insights from Partial Deletion Patients. Frontiers in Psychology. 3. 168–168. 37 indexed citations
19.
Wu, Rachel, Alison Gopnik, Daniel C. Richardson, & Natasha Z. Kirkham. (2011). Infants learn about objects from statistics and people.. Developmental Psychology. 47(5). 1220–1229. 101 indexed citations
20.
Wu, Rachel, et al.. (2011). Infants use social signals to learn from unfamiliar referential cues. Cognitive Science. 33(33). 1 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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