Garga Chatterjee
- Cognitive Neuroscience top 2%
- Experimental and Cognitive Psychology top 2%
- Computer Vision and Pattern Recognition top 5%
- Social Psychology top 5%
- Developmental and Educational Psychology top 10%
- Co-authors
- Ken NakayamaLaura GermineJeremy WilmerChristopher F. ChabrisBradley DuchaineEric LokenMark WilliamsMargaret E. Gerbasi
- Topics
- Face Recognition and Perception (6 papers)Evolutionary Psychology and Human Behavior (3 papers)Visual perception and processing mechanisms (2 papers)
- Cited by
- Cognitive NeuroscienceExperimental and Cognitive PsychologyComputer Vision and Pattern Recognition
- Journals
- Proceedings of the National Academy of SciencesNeuropsychologiaBritish Journal of Ophthalmology
- Partner nations
- United StatesIndiaAustralia
In The Last Decade
Garga Chatterjee
12 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 133
- Cognitive Neuroscience 828
- Experimental and Cognitive Psychology 433
- Computer Vision and Pattern Recognition 298
- Social Psychology 192
- Developmental and Educational Psychology 104
Countries citing papers authored by Garga Chatterjee
This map shows the geographic impact of Garga Chatterjee'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 Garga Chatterjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Garga Chatterjee more than expected).
Fields of papers citing papers by Garga Chatterjee
This network shows the impact of papers produced by Garga Chatterjee. 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 Garga Chatterjee. The network helps show where Garga Chatterjee may publish in the future.
Co-authorship network of co-authors of Garga Chatterjee
This figure shows the co-authorship network connecting the top 25 collaborators of Garga Chatterjee. A scholar is included among the top collaborators of Garga Chatterjee 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 Garga Chatterjee. Garga Chatterjee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 12 | |
| 3 | 40 | |
| 4 | 72 | |
| 5 | 12 | |
| 6 | Is the Web as good as the lab? Comparable performance from Web and lab in cognitive/perceptual experimentsbreakdown → | 517 |
| 7 | 107 | |
| 8 | 31 | |
| 9 | 35 | |
| 10 | 48 | |
| 11 | 364 | |
| 12 | 34 |
About Garga Chatterjee
Garga Chatterjee is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Applied Psychology, having authored 12 papers that have together received 1.3k indexed citations. Recurring topics across this work include Face Recognition and Perception (6 papers), Evolutionary Psychology and Human Behavior (3 papers) and Visual perception and processing mechanisms (2 papers). The work is most often cited by research in Cognitive Neuroscience (828 citations), Experimental and Cognitive Psychology (433 citations) and Computer Vision and Pattern Recognition (298 citations). Garga Chatterjee has collaborated with scholars based in United States, India and Australia. Frequent co-authors include Ken Nakayama, Laura Germine, Jeremy Wilmer, Christopher F. Chabris, Bradley Duchaine, Eric Loken, Mark Williams, Margaret E. Gerbasi, Tapan Kumar Gandhi and Amy Kalia. Their work appears in journals such as Proceedings of the National Academy of Sciences, Neuropsychologia and British Journal of Ophthalmology.
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.