Thomas Naselaris
- Cognitive Neuroscience top 0.5%
- Computer Vision and Pattern Recognition top 2%
- Experimental and Cognitive Psychology top 2%
- Social Psychology top 5%
- Cellular and Molecular Neuroscience top 5%
- Co-authors
- Jack L. GallantKendrick KayRyan PrengerShinji NishimotoStephen M. KosslynEmily A. HolmesJoel PearsonAn T. Vu
- Topics
- Neural dynamics and brain function (25 papers)Face Recognition and Perception (16 papers)Visual perception and processing mechanisms (12 papers)
- Partner nations
- United StatesUnited KingdomMexico
In The Last Decade
Thomas Naselaris
40 papers receiving 4.0k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Cognitive Neuroscience 3.4k
- Computer Vision and Pattern Recognition 552
- Experimental and Cognitive Psychology 461
- Social Psychology 352
- Cellular and Molecular Neuroscience 327
Countries citing papers authored by Thomas Naselaris
This map shows the geographic impact of Thomas Naselaris'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 Thomas Naselaris with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Naselaris more than expected).
Fields of papers citing papers by Thomas Naselaris
This network shows the impact of papers produced by Thomas Naselaris. 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 Thomas Naselaris. The network helps show where Thomas Naselaris may publish in the future.
Co-authorship network of co-authors of Thomas Naselaris
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Naselaris. A scholar is included among the top collaborators of Thomas Naselaris 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 Thomas Naselaris. Thomas Naselaris is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | 31 | |
| 3 | 3 | |
| 4 | A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligencebreakdown → | 211 |
| 5 | 50 | |
| 6 | 0 | |
| 7 | 34 | |
| 8 | 17 | |
| 9 | 4 | |
| 10 | 65 | |
| 11 | 68 | |
| 12 | 23 | |
| 13 | 45 | |
| 14 | Reconstructing Visual Experiences from Brain Activity Evoked by Natural Moviesbreakdown → | 528 |
| 15 | 22 | |
| 16 | Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images | 3 |
| 17 | 70 | |
| 18 | 31 | |
| 19 | 38 | |
| 20 | 21 |
About Thomas Naselaris
Thomas Naselaris is a scholar working on Cognitive Neuroscience, Biophysics and Sensory Systems, having authored 43 papers that have together received 4.1k indexed citations. Recurring topics across this work include Neural dynamics and brain function (25 papers), Face Recognition and Perception (16 papers) and Visual perception and processing mechanisms (12 papers). The work is most often cited by research in Cognitive Neuroscience (3.4k citations), Experimental and Cognitive Psychology (461 citations) and Biophysics (191 citations). Thomas Naselaris has collaborated with scholars based in United States, United Kingdom and Mexico. Frequent co-authors include Jack L. Gallant, Kendrick Kay, Ryan Prenger, Shinji Nishimoto, Stephen M. Kosslyn, Emily A. Holmes, Joel Pearson, An T. Vu, Bin Yu and Yuval Benjamini. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.
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.