Galit Yovel

10.8k total citations · 1 hit paper
112 papers, 7.7k citations indexed

About

Galit Yovel is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology. According to data from OpenAlex, Galit Yovel has authored 112 papers receiving a total of 7.7k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Cognitive Neuroscience, 53 papers in Computer Vision and Pattern Recognition and 38 papers in Experimental and Cognitive Psychology. Recurrent topics in Galit Yovel's work include Face Recognition and Perception (97 papers), Face recognition and analysis (40 papers) and Evolutionary Psychology and Human Behavior (32 papers). Galit Yovel is often cited by papers focused on Face Recognition and Perception (97 papers), Face recognition and analysis (40 papers) and Evolutionary Psychology and Human Behavior (32 papers). Galit Yovel collaborates with scholars based in Israel, United States and United Kingdom. Galit Yovel's co-authors include Nancy Kanwisher, Brad Duchaine, Bradley Duchaine, Elinor McKone, Vadim Axelrod, Talia Brandman, Vincent Walsh, Ken A. Paller, David Pitcher and Boaz Sadeh and has published in prestigious journals such as Journal of Personality and Social Psychology, Neuron and Journal of Neuroscience.

In The Last Decade

Galit Yovel

110 papers receiving 7.5k citations

Hit Papers

The fusiform face area: a cortical region specialized for... 2006 2026 2012 2019 2006 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Galit Yovel Israel 46 6.9k 2.7k 2.1k 840 358 112 7.7k
Bradley Duchaine United States 44 6.1k 0.9× 2.7k 1.0× 2.3k 1.1× 802 1.0× 545 1.5× 71 6.9k
Galia Avidan Israel 33 5.1k 0.7× 1.4k 0.5× 1.3k 0.6× 456 0.5× 346 1.0× 98 5.6k
Jason J.S. Barton Canada 49 6.9k 1.0× 2.1k 0.8× 1.4k 0.7× 461 0.5× 621 1.7× 272 8.1k
Elinor McKone Australia 43 4.5k 0.7× 2.2k 0.8× 1.7k 0.8× 709 0.8× 509 1.4× 101 5.2k
M. Ida Gobbini United States 29 10.1k 1.5× 3.6k 1.3× 1.7k 0.8× 2.0k 2.4× 548 1.5× 57 11.6k
Stefan R. Schweinberger Germany 55 7.7k 1.1× 3.8k 1.4× 1.9k 0.9× 997 1.2× 335 0.9× 195 8.4k
Catherine J. Mondloch Canada 38 5.8k 0.8× 3.9k 1.4× 1.8k 0.9× 785 0.9× 647 1.8× 117 7.0k
Kalanit Grill‐Spector United States 49 12.9k 1.9× 2.5k 0.9× 1.8k 0.9× 1.5k 1.7× 906 2.5× 128 14.2k
Daphne Maurer Canada 52 8.9k 1.3× 4.6k 1.7× 1.9k 0.9× 1.4k 1.6× 1.5k 4.2× 211 11.2k
Aina Puce United States 45 10.4k 1.5× 3.2k 1.2× 1.2k 0.6× 1.7k 2.1× 928 2.6× 105 12.0k

Countries citing papers authored by Galit Yovel

Since Specialization
Citations

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

Fields of papers citing papers by Galit Yovel

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Galit Yovel. 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 Galit Yovel. The network helps show where Galit Yovel may publish in the future.

Co-authorship network of co-authors of Galit Yovel

This figure shows the co-authorship network connecting the top 25 collaborators of Galit Yovel. A scholar is included among the top collaborators of Galit Yovel 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 Galit Yovel. Galit Yovel 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.
Goshen‐Gottstein, Yonatan, et al.. (2024). Group information enhances recognition of both learned and unlearned face appearances.. Journal of Personality and Social Psychology. 128(2). 262–280. 1 indexed citations
2.
Yovel, Galit, et al.. (2024). Distinct Yet Proximal Face- and Body-Selective Brain Regions Enable Clutter-Tolerant Representations of the Face, Body, and Whole Person. Journal of Neuroscience. 44(24). e1871232024–e1871232024. 1 indexed citations
3.
Yovel, Galit, et al.. (2022). Perceptual similarity modulates effects of learning from variability on face recognition. Vision Research. 201. 108128–108128. 4 indexed citations
5.
Yovel, Galit, et al.. (2020). Independent contributions of the face, body, and gait to the representation of the whole person. Attention Perception & Psychophysics. 83(1). 199–214. 3 indexed citations
6.
Yovel, Galit, et al.. (2018). Same critical features are used for identification of familiarized and unfamiliar faces. Vision Research. 157. 105–111. 10 indexed citations
7.
Tsourides, Kleovoulos, et al.. (2018). Recognizing Facial Slivers. Journal of Cognitive Neuroscience. 30(7). 951–962. 12 indexed citations
8.
Arizpe, Joseph, Vincent Walsh, Galit Yovel, & Chris I. Baker. (2016). The categories, frequencies, and stability of idiosyncratic eye-movement patterns to faces. Vision Research. 141. 191–203. 40 indexed citations
9.
Bronfman, Zohar Z., et al.. (2016). The Electrophysiological Signature of Remember–Know Is Confounded with Memory Strength and Cannot Be Interpreted as Evidence for Dual-process Theory of Recognition. Journal of Cognitive Neuroscience. 29(2). 322–336. 19 indexed citations
10.
Axelrod, Vadim, Moshe Bar, Geraint Rees, & Galit Yovel. (2014). Neural Correlates of Subliminal Language Processing. Cerebral Cortex. 25(8). 2160–2169. 43 indexed citations
11.
Tavor, Ido, et al.. (2013). Separate parts of occipito-temporal white matter fibers are associated with recognition of faces and places. NeuroImage. 86. 123–130. 66 indexed citations
12.
Yovel, Galit, et al.. (2012). A face inversion effect without a face. Journal of Vision. 12(9). 631–631. 26 indexed citations
13.
Amit, Elinor, et al.. (2012). Activation of ventral visual cortex supports distance representation. Brain and Cognition. 2 indexed citations
14.
Yovel, Galit, et al.. (2012). Can massive but passive exposure to faces contribute to face recognition abilities?. Journal of Experimental Psychology Human Perception & Performance. 38(2). 285–289. 28 indexed citations
15.
Sadeh, Boaz & Galit Yovel. (2010). Why is the N170 enhanced for inverted faces? An ERP competition experiment. NeuroImage. 53(2). 782–789. 85 indexed citations
16.
Yovel, Galit, et al.. (2010). It's all in your head: Why is the body inversion effect abolished for headless bodies?. Journal of Experimental Psychology Human Perception & Performance. 36(3). 759–767. 80 indexed citations
17.
Bar‐Haim, Yair, et al.. (2009). The Role of Skin Colour in Face Recognition. Perception. 38(1). 145–148. 34 indexed citations
18.
McKone, Elinor & Galit Yovel. (2009). Why does picture-plane inversion sometimes dissociate perception of features and spacing in faces, and sometimes not? Toward a new theory of holistic processing. Psychonomic Bulletin & Review. 16(5). 778–797. 230 indexed citations
19.
Levy, Jerre, et al.. (2003). Facilitation and disruption of lateralized syllable processing by unattended stimuli in the opposite visual field. Brain and Language. 85(3). 432–440. 1 indexed citations
20.
Mintz, Matti, et al.. (1998). Dissociation Between Startle and Prepulse Inhibition in Rats Exposed to γ Radiation at Day 15 of Embryogeny. Brain Research Bulletin. 45(3). 289–296. 12 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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