Michelle R. Greene
- Cognitive Neuroscience top 2%
- Computer Vision and Pattern Recognition top 1%
- Experimental and Cognitive Psychology top 5%
- Human-Computer Interaction top 2%
- Social Psychology top 10%
- Co-authors
- Aude OlivaJeremy M. WolfeMelissa L.‐H. VõKarla K. EvansLi Fei-FeiDiane M. BeckTimothy F. BradySoojin Park
- Topics
- Visual Attention and Saliency Detection (28 papers)Visual perception and processing mechanisms (22 papers)Face Recognition and Perception (17 papers)
- Partner nations
- United StatesFranceGermany
In The Last Decade
Michelle R. Greene
46 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 104
- Cognitive Neuroscience 1.2k
- Computer Vision and Pattern Recognition 722
- Experimental and Cognitive Psychology 200
- Human-Computer Interaction 166
- Social Psychology 142
Countries citing papers authored by Michelle R. Greene
This map shows the geographic impact of Michelle R. Greene'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 Michelle R. Greene with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle R. Greene more than expected).
Fields of papers citing papers by Michelle R. Greene
This network shows the impact of papers produced by Michelle R. Greene. 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 Michelle R. Greene. The network helps show where Michelle R. Greene may publish in the future.
Co-authorship network of co-authors of Michelle R. Greene
This figure shows the co-authorship network connecting the top 25 collaborators of Michelle R. Greene. A scholar is included among the top collaborators of Michelle R. Greene 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 Michelle R. Greene. Michelle R. Greene is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 4 | |
| 6 | 4 | |
| 7 | 29 | |
| 8 | 27 | |
| 9 | 55 | |
| 10 | 42 | |
| 11 | 53 | |
| 12 | 53 | |
| 13 | 1 | |
| 14 | 3 | |
| 15 | 14 | |
| 16 | 344 | |
| 17 | 49 | |
| 18 | The Briefest of Glances: The Time Course of Natural Scene Understanding | 23 |
| 19 | Natural Scene Categorization from Conjunctions of Ecological Global Properties | 9 |
| 20 | 23 |
About Michelle R. Greene
Michelle R. Greene is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Human-Computer Interaction, having authored 52 papers that have together received 1.7k indexed citations. Recurring topics across this work include Visual Attention and Saliency Detection (28 papers), Visual perception and processing mechanisms (22 papers) and Face Recognition and Perception (17 papers). The work is most often cited by research in Cognitive Neuroscience (1.2k citations), Computer Vision and Pattern Recognition (722 citations) and Human-Computer Interaction (166 citations). Michelle R. Greene has collaborated with scholars based in United States, France and Germany. Frequent co-authors include Aude Oliva, Jeremy M. Wolfe, Melissa L.‐H. Võ, Karla K. Evans, Li Fei-Fei, Diane M. Beck, Timothy F. Brady, Soojin Park, Bruce C. Hansen and Tommy Liu. Their work appears in journals such as Journal of Neuroscience, NeuroImage and Trends in Cognitive Sciences.
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.