Mark D. Lescroart

878 total citations
21 papers, 507 citations indexed

About

Mark D. Lescroart is a scholar working on Cognitive Neuroscience, Human-Computer Interaction and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mark D. Lescroart has authored 21 papers receiving a total of 507 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Cognitive Neuroscience, 5 papers in Human-Computer Interaction and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mark D. Lescroart's work include Face Recognition and Perception (12 papers), Visual perception and processing mechanisms (9 papers) and Neural dynamics and brain function (8 papers). Mark D. Lescroart is often cited by papers focused on Face Recognition and Perception (12 papers), Visual perception and processing mechanisms (9 papers) and Neural dynamics and brain function (8 papers). Mark D. Lescroart collaborates with scholars based in United States, Taiwan and Italy. Mark D. Lescroart's co-authors include Jack L. Gallant, Irving Biederman, Kenneth J. Hayworth, James Gao, Alexander G. Huth, Dustin Stansbury, Xiaomin Yue, Jun Kitazono, Takayuki Sato and Masato Okada and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Mark D. Lescroart

19 papers receiving 501 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark D. Lescroart United States 9 446 105 59 38 25 21 507
Peter F. Schade United States 7 378 0.8× 93 0.9× 68 1.2× 50 1.3× 20 0.8× 10 457
Maryam Vaziri-Pashkam United States 14 476 1.1× 76 0.7× 52 0.9× 81 2.1× 34 1.4× 38 593
David A. Tovar United States 7 422 0.9× 55 0.5× 68 1.2× 55 1.4× 11 0.4× 11 477
Dean Wyatte United States 10 252 0.6× 84 0.8× 31 0.5× 35 0.9× 19 0.8× 14 374
Kathryn Bonnen United States 10 265 0.6× 228 2.2× 35 0.6× 39 1.0× 19 0.8× 24 536
Eamon Caddigan United States 9 455 1.0× 187 1.8× 62 1.1× 48 1.3× 7 0.3× 17 574
Heiko H. Schütt Germany 9 343 0.8× 72 0.7× 75 1.3× 47 1.2× 9 0.4× 17 424
Anthony Stigliani United States 11 578 1.3× 99 0.9× 91 1.5× 67 1.8× 39 1.6× 16 643
Anna Kosovicheva United States 12 340 0.8× 42 0.4× 80 1.4× 79 2.1× 16 0.6× 36 439
Pouya Bashivan United States 8 509 1.1× 135 1.3× 39 0.7× 28 0.7× 25 1.0× 23 657

Countries citing papers authored by Mark D. Lescroart

Since Specialization
Citations

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

Fields of papers citing papers by Mark D. Lescroart

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark D. Lescroart. 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 Mark D. Lescroart. The network helps show where Mark D. Lescroart may publish in the future.

Co-authorship network of co-authors of Mark D. Lescroart

This figure shows the co-authorship network connecting the top 25 collaborators of Mark D. Lescroart. A scholar is included among the top collaborators of Mark D. Lescroart 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 Mark D. Lescroart. Mark D. Lescroart 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.
2.
Huth, Alexander G., et al.. (2024). Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses. Nature Communications. 15(1). 5531–5531. 4 indexed citations
3.
Lescroart, Mark D., et al.. (2023). A framework for generalizable neural networks for robust estimation of eyelids and pupils. Behavior Research Methods. 56(4). 3959–3981. 2 indexed citations
4.
Lescroart, Mark D., et al.. (2021). Ergonomic Design Development of the Visual Experience Database Headset. 1–4. 2 indexed citations
5.
Lescroart, Mark D., et al.. (2021). Characterizing the Performance of Deep Neural Networks for Eye-Tracking. 1–4. 1 indexed citations
6.
MacNeilage, Paul R., et al.. (2021). Pupil Tracking Under Direct Sunlight. 1–4. 6 indexed citations
7.
Lescroart, Mark D. & Jack L. Gallant. (2018). Human Scene-Selective Areas Represent 3D Configurations of Surfaces. Neuron. 101(1). 178–192.e7. 66 indexed citations
8.
Lescroart, Mark D., Nancy Kanwisher, & Julie D. Golomb. (2016). No Evidence for Automatic Remapping of Stimulus Features or Location Found with fMRI. Frontiers in Systems Neuroscience. 10. 53–53. 13 indexed citations
9.
Lescroart, Mark D., Pulkit Agrawal, & Jack L. Gallant. (2016). Both convolutional neural networks and voxel-wise encoding models of brain activity derived from ConvNets represent boundary-and surface-related features. Journal of Vision. 16(12). 756–756. 2 indexed citations
10.
Gao, James, Alexander G. Huth, Mark D. Lescroart, & Jack L. Gallant. (2015). Pycortex: an interactive surface visualizer for fMRI. Frontiers in Neuroinformatics. 9. 23–23. 98 indexed citations
11.
Lescroart, Mark D., Dustin Stansbury, & Jack L. Gallant. (2015). Fourier power, subjective distance, and object categories all provide plausible models of BOLD responses in scene-selective visual areas. Frontiers in Computational Neuroscience. 9. 135–135. 71 indexed citations
12.
Lescroart, Mark D. & Jack L. Gallant. (2015). A model of surface depth and orientation predicts BOLD responses in human scene-selective cortex. Journal of Vision. 15(12). 573–573. 1 indexed citations
13.
Sato, Takayuki, et al.. (2013). Object Representation in Inferior Temporal Cortex Is Organized Hierarchically in a Mosaic-Like Structure. Journal of Neuroscience. 33(42). 16642–16656. 44 indexed citations
14.
Lescroart, Mark D. & Irving Biederman. (2012). Cortical Representation of Medial Axis Structure. Cerebral Cortex. 23(3). 629–637. 53 indexed citations
15.
Hayworth, Kenneth J., Mark D. Lescroart, & Irving Biederman. (2011). Neural encoding of relative position.. Journal of Experimental Psychology Human Perception & Performance. 37(4). 1032–1050. 28 indexed citations
16.
Biederman, Irving, Mark D. Lescroart, & Kenneth J. Hayworth. (2010). Sensitivity to object-centered relations in LOC. Journal of Vision. 7(9). 1030–1030. 3 indexed citations
17.
Hayworth, Kenneth J., Mark D. Lescroart, & Irving Biederman. (2010). Explicit relation coding in the Lateral Occipital Complex. Journal of Vision. 8(6). 35–35. 2 indexed citations
18.
Biederman, Irving, et al.. (2009). Adaptation to objects in the lateral occipital complex (LOC): Shape or semantics?. Vision Research. 49(18). 2297–2305. 57 indexed citations
19.
Yue, Xiaomin, et al.. (2009). Adaptation in the fusiform face area (FFA): Image or person?. Vision Research. 49(23). 2800–2807. 47 indexed citations
20.
Lescroart, Mark D., Irving Biederman, Xiaomin Yue, & Jules Davidoff. (2008). A cross-cultural study of the representation of shape: Sensitivity to generalized cone dimensions. Visual Cognition. 18(1). 50–66. 6 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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