Miguel P. Eckstein

7.8k total citations
231 papers, 5.6k citations indexed

About

Miguel P. Eckstein is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Miguel P. Eckstein has authored 231 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 126 papers in Cognitive Neuroscience, 87 papers in Computer Vision and Pattern Recognition and 41 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Miguel P. Eckstein's work include Visual perception and processing mechanisms (88 papers), Visual Attention and Saliency Detection (47 papers) and Neural dynamics and brain function (39 papers). Miguel P. Eckstein is often cited by papers focused on Visual perception and processing mechanisms (88 papers), Visual Attention and Saliency Detection (47 papers) and Neural dynamics and brain function (39 papers). Miguel P. Eckstein collaborates with scholars based in United States, Switzerland and United Kingdom. Miguel P. Eckstein's co-authors include Craig K. Abbey, Steven S. Shimozaki, Matthew Peterson, François Bochud, Binh T. Pham, M. Carrasco, James S. Whiting, Barry Giesbrecht, Albert J. Ahumada and Brent R. Beutter and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Neuroscience and NeuroImage.

In The Last Decade

Miguel P. Eckstein

210 papers receiving 5.5k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Miguel P. Eckstein 3.7k 1.7k 988 619 574 231 5.6k
Eli Peli 3.7k 1.0× 2.5k 1.5× 1.6k 1.6× 230 0.4× 58 0.1× 351 8.5k
Andrew B. Watson 6.5k 1.8× 4.9k 2.9× 408 0.4× 607 1.0× 64 0.1× 139 11.1k
Wilson S. Geisler 6.5k 1.7× 3.5k 2.0× 261 0.3× 526 0.8× 49 0.1× 160 9.7k
Huiguang He 2.4k 0.6× 543 0.3× 658 0.7× 1.1k 1.7× 59 0.1× 180 3.9k
Paul Sajda 3.8k 1.0× 460 0.3× 598 0.6× 380 0.6× 30 0.1× 177 6.0k
Stephen L. Macknik 4.7k 1.3× 520 0.3× 554 0.6× 553 0.9× 38 0.1× 140 6.7k
Walter F. Bischof 2.2k 0.6× 853 0.5× 147 0.1× 453 0.7× 65 0.1× 146 3.5k
Felix A. Wichmann 4.6k 1.2× 1.2k 0.7× 234 0.2× 1.0k 1.6× 32 0.1× 119 6.6k
Gordon E. Legge 8.4k 2.3× 1.3k 0.8× 1.5k 1.6× 1.2k 1.9× 31 0.1× 236 11.8k
Karla K. Evans 1.1k 0.3× 392 0.2× 434 0.4× 548 0.9× 109 0.2× 47 2.0k

Countries citing papers authored by Miguel P. Eckstein

Since Specialization
Citations

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

Fields of papers citing papers by Miguel P. Eckstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Miguel P. Eckstein. 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 Miguel P. Eckstein. The network helps show where Miguel P. Eckstein may publish in the future.

Co-authorship network of co-authors of Miguel P. Eckstein

This figure shows the co-authorship network connecting the top 25 collaborators of Miguel P. Eckstein. A scholar is included among the top collaborators of Miguel P. Eckstein 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 Miguel P. Eckstein. Miguel P. Eckstein 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.
Wang, William Yang, et al.. (2025). Emergent neuronal mechanisms mediating covert attention in convolutional neural networks. Proceedings of the National Academy of Sciences. 122(46). e2411909122–e2411909122.
2.
Eckstein, Miguel P., et al.. (2024). How experts and novices judge other people’s knowledgeability from language use. Psychonomic Bulletin & Review. 31(4). 1627–1637. 2 indexed citations
3.
Eckstein, Miguel P., et al.. (2024). Eye Movements during Free Viewing to Maximize Scene Understanding. Journal of Vision. 24(10). 1189–1189. 1 indexed citations
4.
Zhu, Wanrong, Xin Wang, An Yan, Miguel P. Eckstein, & William Yang Wang. (2023). ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation. 93–105. 2 indexed citations
5.
Zhu, Wanrong, Xinyi Wang, Yujie Lu, et al.. (2023). Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation. 11113–11122. 2 indexed citations
6.
Giesbrecht, Barry, et al.. (2021). The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships. Communications Biology. 4(1). 768–768. 7 indexed citations
7.
Fu, Tsu-Jui, Xin Wang, Scott T. Grafton, Miguel P. Eckstein, & William Yang Wang. (2020). SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning. 4413–4422. 15 indexed citations
8.
Eckstein, Miguel P., et al.. (2017). Towards Metamerism via Foveated Style Transfer. arXiv (Cornell University). 2 indexed citations
9.
Eckstein, Miguel P., et al.. (2016). Can Peripheral Representations Improve Clutter Metrics on Complex Scenes. arXiv (Cornell University). 29. 2847–2855. 4 indexed citations
10.
Eckstein, Miguel P., et al.. (2015). Scene Inversion Slows the Rejection of False Positives through Saccade Exploration During Search.. Cognitive Science. 3 indexed citations
11.
Peterson, Matthew, Calvin Kalun Or, J. C. Elliott, Barry Giesbrecht, & Miguel P. Eckstein. (2014). Early and late neural correlates of individual differences in fixation-specific face recognition performance. Journal of Vision. 14(10). 1441–1441.
12.
Das, Koel, et al.. (2013). Neural Representations of Contextual Guidance in Visual Search of Real-World Scenes. Journal of Neuroscience. 33(18). 7846–7855. 34 indexed citations
13.
Peterson, Matthew, et al.. (2013). Asian and Caucasian observers' initial eye movements during face identification are similar and optimal. Journal of Vision. 13(9). 1273–1273. 1 indexed citations
14.
Peterson, Matthew & Miguel P. Eckstein. (2013). Learning optimal eye movements to unusual faces. Vision Research. 99. 57–68. 16 indexed citations
15.
Eckstein, Miguel P., et al.. (2010). Optimizing eye movements in search for rewards. Journal of Vision. 10(7). 33–33. 4 indexed citations
16.
Peterson, Matthew, Craig K. Abbey, & Miguel P. Eckstein. (2008). The surprisingly high human efficiency at learning to recognize faces. Vision Research. 49(3). 301–314. 6 indexed citations
17.
Eckstein, Miguel P., Binh T. Pham, & Steven S. Shimozaki. (2004). The footprints of visual attention during search with 100% valid and 100% invalid cues. Vision Research. 44(12). 1193–1207. 42 indexed citations
18.
Eckstein, Miguel P. & Steven S. Shimozaki. (2002). Classification images for saccadic targeting and perceptual decisions during search. Perception. 31. 0–0. 1 indexed citations
19.
Carrasco, M., et al.. (2000). Spatial covert attention increases contrast sensitivity across the CSF: support for signal enhancement. Vision Research. 40(10-12). 1203–1215. 378 indexed citations
20.
Eckstein, Miguel P., et al.. (1998). The Effect of Set Size on the Relation Between Saccadic and Perceptual Decisions During Search. NASA Technical Reports Server (NASA). 6(1). 88–92.

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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