Megan H. Papesh

2.2k total citations
38 papers, 1.3k citations indexed

About

Megan H. Papesh is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Social Psychology. According to data from OpenAlex, Megan H. Papesh has authored 38 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Cognitive Neuroscience, 15 papers in Experimental and Cognitive Psychology and 7 papers in Social Psychology. Recurrent topics in Megan H. Papesh's work include Memory Processes and Influences (12 papers), Face Recognition and Perception (11 papers) and Memory and Neural Mechanisms (9 papers). Megan H. Papesh is often cited by papers focused on Memory Processes and Influences (12 papers), Face Recognition and Perception (11 papers) and Memory and Neural Mechanisms (9 papers). Megan H. Papesh collaborates with scholars based in United States. Megan H. Papesh's co-authors include Stephen D. Goldinger, Michael C. Hout, Yi He, Anthony S. Barnhart, Peter N. Steinmetz, David M. Treiman, Kris A. Smith, John T. Wixted, Larry R. Squire and Yoon‐Hee Jang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

Megan H. Papesh

34 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Megan H. Papesh United States 17 825 421 302 142 129 38 1.3k
Ute Leonards United Kingdom 26 1.1k 1.4× 317 0.8× 409 1.4× 83 0.6× 168 1.3× 105 2.0k
Julie D. Golomb United States 21 1.8k 2.2× 448 1.1× 248 0.8× 137 1.0× 208 1.6× 65 2.2k
Emanuela Bricolo Italy 22 1.2k 1.5× 553 1.3× 225 0.7× 161 1.1× 189 1.5× 61 1.6k
Miguel A. Escrig Spain 8 858 1.0× 543 1.3× 392 1.3× 101 0.7× 58 0.4× 14 1.5k
Alejandro Lleras United States 24 1.9k 2.3× 557 1.3× 352 1.2× 170 1.2× 247 1.9× 92 2.4k
Matthew S. Cain United States 24 986 1.2× 400 1.0× 333 1.1× 226 1.6× 162 1.3× 64 1.9k
Mowei Shen China 25 1.3k 1.5× 337 0.8× 538 1.8× 199 1.4× 65 0.5× 164 1.7k
Andrea Hildebrandt Germany 24 1.4k 1.7× 945 2.2× 385 1.3× 225 1.6× 159 1.2× 100 2.4k
Paul T. Sowden United Kingdom 22 716 0.9× 462 1.1× 355 1.2× 141 1.0× 80 0.6× 44 1.6k
Brad Wyble United States 24 2.1k 2.5× 470 1.1× 227 0.8× 103 0.7× 205 1.6× 92 2.5k

Countries citing papers authored by Megan H. Papesh

Since Specialization
Citations

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

Fields of papers citing papers by Megan H. Papesh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Megan H. Papesh. 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 Megan H. Papesh. The network helps show where Megan H. Papesh may publish in the future.

Co-authorship network of co-authors of Megan H. Papesh

This figure shows the co-authorship network connecting the top 25 collaborators of Megan H. Papesh. A scholar is included among the top collaborators of Megan H. Papesh 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 Megan H. Papesh. Megan H. Papesh 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.
Papesh, Megan H., et al.. (2024). Spotting missing or wanted people: racial biases in prospective person memory. Cognitive Research Principles and Implications. 9(1). 68–68.
2.
Papesh, Megan H., et al.. (2024). False Memories of Familiar Faces. Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie). 71(6). 313–323. 1 indexed citations
3.
Wixted, John T., Stephen D. Goldinger, Megan H. Papesh, et al.. (2022). Two kinds of memory signals in neurons of the human hippocampus. Proceedings of the National Academy of Sciences. 119(19). e2115128119–e2115128119. 9 indexed citations
4.
Papesh, Megan H., et al.. (2022). Why Are We so Scent-Imental? Studying Odor-Linked Memories. Frontiers for Young Minds. 10. 2 indexed citations
5.
Hout, Michael C., et al.. (2022). The Oddity Detection in Diverse Scenes (ODDS) database: Validated real-world scenes for studying anomaly detection. Behavior Research Methods. 55(2). 583–599. 2 indexed citations
6.
Papesh, Megan H., et al.. (2021). Eye movements reflect expertise development in hybrid search. Cognitive Research Principles and Implications. 6(1). 7–7. 12 indexed citations
7.
Papesh, Megan H., et al.. (2021). Flexible attention allocation dynamically impacts incidental encoding in prospective memory. Memory & Cognition. 50(1). 112–128. 4 indexed citations
8.
Papesh, Megan H., et al.. (2020). The detail is in the difficulty: Challenging search facilitates rich incidental object encoding. Memory & Cognition. 48(7). 1214–1233. 15 indexed citations
9.
Papesh, Megan H., et al.. (2019). Spotting rare items makes the brain “blink” harder: Evidence from pupillometry. Attention Perception & Psychophysics. 81(8). 2635–2647. 3 indexed citations
10.
Hicks, Jason L., et al.. (2019). Response dynamics of event-based prospective memory retrieval in mouse tracking. Memory & Cognition. 47(5). 923–935. 2 indexed citations
11.
Papesh, Megan H., et al.. (2018). Expectancy effects in the Autonomous Sensory Meridian Response. PeerJ. 6. e5229–e5229. 36 indexed citations
12.
Goldinger, Stephen D., et al.. (2016). The poverty of embodied cognition. Psychonomic Bulletin & Review. 23(4). 959–978. 120 indexed citations
13.
Papesh, Megan H., Stephen D. Goldinger, & Michael C. Hout. (2016). Eye movements reveal fast, voice-specific priming.. Journal of Experimental Psychology General. 145(3). 314–337. 6 indexed citations
14.
Papesh, Megan H.. (2015). Just out of reach: On the reliability of the action-sentence compatibility effect.. Journal of Experimental Psychology General. 144(6). e116–e141. 47 indexed citations
15.
Papesh, Megan H., et al.. (2015). Distributed Representation of Visual Objects by Single Neurons in the Human Brain. Journal of Neuroscience. 35(13). 5180–5186. 21 indexed citations
16.
Papesh, Megan H. & Stephen D. Goldinger. (2014). Infrequent identity mismatches are frequently undetected. Attention Perception & Psychophysics. 76(5). 1335–1349. 35 indexed citations
17.
Hout, Michael C., Megan H. Papesh, & Stephen D. Goldinger. (2012). Multidimensional scaling. Wiley Interdisciplinary Reviews Cognitive Science. 4(1). 93–103. 251 indexed citations
18.
Papesh, Megan H. & Stephen D. Goldinger. (2012). Memory in motion: Movement dynamics reveal memory strength. Psychonomic Bulletin & Review. 19(5). 906–913. 38 indexed citations
19.
Papesh, Megan H., Stephen D. Goldinger, & Michael C. Hout. (2011). Memory strength and specificity revealed by pupillometry. International Journal of Psychophysiology. 83(1). 56–64. 98 indexed citations
20.
Goldinger, Stephen D., Yi He, & Megan H. Papesh. (2009). Deficits in cross-race face learning: Insights from eye movements and pupillometry.. Journal of Experimental Psychology Learning Memory and Cognition. 35(5). 1105–1122. 152 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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