Najib J. Majaj

5.1k total citations · 2 hit papers
46 papers, 2.7k citations indexed

About

Najib J. Majaj is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Cellular and Molecular Neuroscience. According to data from OpenAlex, Najib J. Majaj has authored 46 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Cognitive Neuroscience, 9 papers in Computer Vision and Pattern Recognition and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Najib J. Majaj's work include Visual perception and processing mechanisms (32 papers), Neural dynamics and brain function (22 papers) and Face Recognition and Perception (10 papers). Najib J. Majaj is often cited by papers focused on Visual perception and processing mechanisms (32 papers), Neural dynamics and brain function (22 papers) and Face Recognition and Perception (10 papers). Najib J. Majaj collaborates with scholars based in United States, Italy and Australia. Najib J. Majaj's co-authors include Denis G. Pelli, M. Palomares, James J. DiCarlo, Ha Hong, J. Anthony Movshon, Daniel Yamins, Ethan A. Solomon, Marialuisa Martelli, Katharine A. Tillman and T. Berger and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Najib J. Majaj

40 papers receiving 2.7k citations

Hit Papers

Crowding is unlike ordinary masking: Distinguishing featu... 2004 2026 2011 2018 2004 2014 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Najib J. Majaj United States 18 2.3k 480 312 268 252 46 2.7k
Bosco S. Tjan United States 27 2.1k 0.9× 392 0.8× 448 1.4× 234 0.9× 98 0.4× 87 2.5k
Timothy Ledgeway United Kingdom 27 2.1k 0.9× 245 0.5× 212 0.7× 617 2.3× 304 1.2× 90 2.4k
Lucia M. Vaina United States 30 2.7k 1.1× 339 0.7× 327 1.0× 291 1.1× 308 1.2× 105 3.2k
Dwight J. Kravitz United States 28 3.5k 1.5× 425 0.9× 499 1.6× 94 0.4× 291 1.2× 58 4.0k
Joseph S. Lappin United States 31 2.5k 1.1× 523 1.1× 530 1.7× 208 0.8× 240 1.0× 105 3.0k
Bart Farell United States 16 1.3k 0.6× 206 0.4× 326 1.0× 179 0.7× 142 0.6× 61 1.6k
Yoram Bonneh Israel 28 1.9k 0.8× 188 0.4× 261 0.8× 262 1.0× 145 0.6× 90 2.1k
Fang Fang China 29 2.7k 1.2× 384 0.8× 522 1.7× 108 0.4× 226 0.9× 139 3.3k
Martin Rolfs Germany 29 3.0k 1.3× 345 0.7× 519 1.7× 83 0.3× 158 0.6× 104 3.6k
David L. Sheinberg United States 26 2.9k 1.2× 543 1.1× 478 1.5× 72 0.3× 491 1.9× 52 3.4k

Countries citing papers authored by Najib J. Majaj

Since Specialization
Citations

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

Fields of papers citing papers by Najib J. Majaj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Najib J. Majaj. 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 Najib J. Majaj. The network helps show where Najib J. Majaj may publish in the future.

Co-authorship network of co-authors of Najib J. Majaj

This figure shows the co-authorship network connecting the top 25 collaborators of Najib J. Majaj. A scholar is included among the top collaborators of Najib J. Majaj 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 Najib J. Majaj. Najib J. Majaj 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.
Subramanian, Ajay, et al.. (2025). Benchmarking the speed–accuracy tradeoff in object recognition by humans and neural networks. Journal of Vision. 25(1). 4–4.
2.
Kurzawski, Jan W., et al.. (2025). Human V4 size predicts crowding distance. Nature Communications. 16(1). 3876–3876. 1 indexed citations
3.
Majaj, Najib J., et al.. (2025). Neural Sensitivity to Radial Frequency Patterns in the Visual Cortex of Developing Macaques. Journal of Neuroscience. 45(34). e0179252025–e0179252025.
4.
Majaj, Najib J., et al.. (2024). Developmentally stable representations of naturalistic image structure in macaque visual cortex. Cell Reports. 43(8). 114534–114534. 3 indexed citations
5.
Kurzawski, Jan W., et al.. (2023). EasyEyes — A new method for accurate fixation in online vision testing. Frontiers in Human Neuroscience. 17. 1255465–1255465. 2 indexed citations
6.
Lieber, Justin D., et al.. (2023). Sensitivity to naturalistic texture relies primarily on high spatial frequencies. Journal of Vision. 23(2). 4–4. 2 indexed citations
7.
Long, Michael A., Kalman A. Katlowitz, Mario A. Svirsky, et al.. (2016). Functional Segregation of Cortical Regions Underlying Speech Timing and Articulation. Neuron. 89(6). 1187–1193. 100 indexed citations
8.
Hallum, Luke E., Romesh D. Kumbhani, Corey M. Ziemba, et al.. (2015). Population representation of visual information in areas V1 and V2 of amblyopic macaques. Vision Research. 114. 56–67. 45 indexed citations
9.
Majaj, Najib J., et al.. (2014). Neural correlates of amblyopia in foveal and parafoveal visual cortex of amblyopic macaque monkeys. Journal of Vision. 14(10). 689–689. 1 indexed citations
10.
Tailby, Chris, Najib J. Majaj, & J. Anthony Movshon. (2010). Binocular Integration of Pattern Motion Signals by MT Neurons and by Human Observers. Journal of Neuroscience. 30(21). 7344–7349. 29 indexed citations
11.
Martelli, Marialuisa, Najib J. Majaj, & Denis G. Pelli. (2010). Words and faces: eccentricity distinguishes crowding from context. Journal of Vision. 2(7). 608–608. 3 indexed citations
12.
Majaj, Najib J., Matthew A. Smith, & J. Anthony Movshon. (2010). Contrast gain control in macaque area MT. Journal of Vision. 1(3). 401–401.
13.
Pelli, Denis G., et al.. (2009). Grouping in object recognition: The role of a Gestalt law in letter identification. Cognitive Neuropsychology. 26(1). 36–49. 29 indexed citations
14.
Dhruv, Neel T., Chris Tailby, Sach Sokol, Najib J. Majaj, & Peter Lennie. (2009). Nonlinear Signal Summation in Magnocellular Neurons of the Macaque Lateral Geniculate Nucleus. Journal of Neurophysiology. 102(3). 1921–1929. 8 indexed citations
15.
Majaj, Najib J., Matteo Carandini, & J. Anthony Movshon. (2007). Motion Integration by Neurons in Macaque MT Is Local, Not Global. Journal of Neuroscience. 27(2). 366–370. 110 indexed citations
16.
Smith, Matthew A., Najib J. Majaj, & J. Anthony Movshon. (2005). Dynamics of motion signaling by neurons in macaque area MT. Nature Neuroscience. 8(2). 220–228. 153 indexed citations
17.
Tailby, Chris, Samuel G. Solomon, Neel T. Dhruv, Najib J. Majaj, & Peter Lennie. (2005). Habituation reveals cardinal chromatic mechanisms in striate cortex of macaque. Journal of Vision. 5(8). 80–80. 2 indexed citations
18.
Pelli, Denis G., M. Palomares, & Najib J. Majaj. (2004). Crowding is unlike ordinary masking: Distinguishing feature integration from detection. Journal of Vision. 4(12). 12–12. 640 indexed citations breakdown →
19.
Majaj, Najib J., et al.. (2002). The role of spatial frequency channels in letter identification. Vision Research. 42(9). 1165–1184. 190 indexed citations
20.
Majaj, Najib J., Matteo Carandini, & J. Anthony Movshon. (1999). Local integration of features for the computation of pattern direction by neurons in macaque area MT. UCL Discovery (University College London). 5 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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