Charles Chubb

4.0k total citations · 1 hit paper
86 papers, 2.8k citations indexed

About

Charles Chubb is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Charles Chubb has authored 86 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Cognitive Neuroscience, 20 papers in Cellular and Molecular Neuroscience and 19 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Charles Chubb's work include Visual perception and processing mechanisms (53 papers), Neural dynamics and brain function (21 papers) and Neurobiology and Insect Physiology Research (20 papers). Charles Chubb is often cited by papers focused on Visual perception and processing mechanisms (53 papers), Neural dynamics and brain function (21 papers) and Neurobiology and Insect Physiology Research (20 papers). Charles Chubb collaborates with scholars based in United States, Taiwan and United Kingdom. Charles Chubb's co-authors include George Sperling, Joshua A. Solomon, Roger T. Hanlon, Chuan‐Chin Chiao, Kendra C. Buresch, Li Zhao, Lydia M. Mäthger, Peter Werkhoven, Alexandra Barbosa and Jong-Ho Nam and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Psychological Review and Philosophical Transactions of the Royal Society B Biological Sciences.

In The Last Decade

Charles Chubb

80 papers receiving 2.7k citations

Hit Papers

Drift-balanced random stimuli: a general basis for studyi... 1988 2026 2000 2013 1988 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charles Chubb United States 25 2.0k 655 583 473 431 86 2.8k
Tom Trościanko United Kingdom 26 1.4k 0.7× 159 0.2× 207 0.4× 211 0.4× 714 1.7× 75 2.8k
Jenny C. A. Read United Kingdom 29 1.4k 0.7× 455 0.7× 355 0.6× 127 0.3× 149 0.3× 128 2.3k
Nicholas E. Scott‐Samuel United Kingdom 27 643 0.3× 339 0.5× 1.1k 1.8× 85 0.2× 362 0.8× 78 2.3k
Colin W. G. Clifford Australia 45 5.7k 2.9× 892 1.4× 160 0.3× 357 0.8× 918 2.1× 205 6.4k
Bruce G. Cumming United States 43 5.0k 2.5× 1.4k 2.1× 206 0.4× 360 0.8× 337 0.8× 155 6.2k
David Ingle United States 25 4.0k 2.0× 692 1.1× 379 0.7× 106 0.2× 947 2.2× 57 5.3k
Jochen Braun Germany 33 3.4k 1.7× 494 0.8× 58 0.1× 209 0.4× 314 0.7× 87 4.3k
Françoise Viénot France 17 595 0.3× 161 0.2× 150 0.3× 458 1.0× 517 1.2× 57 1.8k
George Mather United Kingdom 28 3.0k 1.5× 559 0.9× 47 0.1× 309 0.7× 599 1.4× 103 3.7k
Roland Baddeley United Kingdom 28 738 0.4× 296 0.5× 602 1.0× 80 0.2× 256 0.6× 59 2.1k

Countries citing papers authored by Charles Chubb

Since Specialization
Citations

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

Fields of papers citing papers by Charles Chubb

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charles Chubb

This figure shows the co-authorship network connecting the top 25 collaborators of Charles Chubb. A scholar is included among the top collaborators of Charles Chubb 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 Charles Chubb. Charles Chubb 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.
Chubb, Charles, et al.. (2021). The density effect in centroid estimation is blind to contrast polarity. Vision Research. 186. 41–51. 1 indexed citations
2.
Chubb, Charles, et al.. (2019). The features that control discrimination of an isodipole texture pair. Vision Research. 158. 208–220. 1 indexed citations
3.
Chubb, Charles, et al.. (2018). Dark scene elements strongly influence cuttlefish camouflage responses in visually cluttered environments. Vision Research. 149. 86–101. 3 indexed citations
4.
Chubb, Charles, et al.. (2015). The centroid paradigm: Quantifying feature-based attention in terms of attention filters. Attention Perception & Psychophysics. 78(2). 474–515. 21 indexed citations
5.
Chubb, Charles, et al.. (2014). The 3-dimensional, 4-channel model of human visual sensitivity to grayscale scrambles. Vision Research. 101. 94–107. 9 indexed citations
6.
Buresch, Kendra C., Lydia M. Mäthger, Justine J. Allen, et al.. (2011). The use of background matching vs. masquerade for camouflage in cuttlefish Sepia officinalis. Vision Research. 51(23-24). 2362–2368. 33 indexed citations
7.
Chiao, Chuan‐Chin, et al.. (2009). The scaling effects of substrate texture on camouflage patterning in cuttlefish. Vision Research. 49(13). 1647–1656. 68 indexed citations
8.
Barbosa, Alexandra, Lydia M. Mäthger, Kendra C. Buresch, et al.. (2008). Cuttlefish camouflage: The effects of substrate contrast and size in evoking uniform, mottle or disruptive body patterns. Vision Research. 48(10). 1242–1253. 133 indexed citations
9.
Chubb, Charles, et al.. (2007). The three dimensions of human visual sensitivity to first-order contrast statistics. Vision Research. 47(17). 2237–2248. 19 indexed citations
10.
Chiao, Chuan‐Chin, Charles Chubb, & Roger T. Hanlon. (2007). Interactive effects of size, contrast, intensity and configuration of background objects in evoking disruptive camouflage in cuttlefish. Vision Research. 47(16). 2223–2235. 46 indexed citations
11.
Solomon, Joshua A., et al.. (2005). Stimulus contrast and the Reichardt detector. Vision Research. 45(16). 2109–2117. 5 indexed citations
12.
Chubb, Charles, et al.. (2004). A visual mechanism tuned to black. Vision Research. 44(27). 3223–3232. 59 indexed citations
13.
Chubb, Charles, et al.. (2003). Brightness assimilation in bullseye displays. Vision Research. 44(3). 309–319. 23 indexed citations
14.
Chubb, Charles, et al.. (2002). Attentional control of texture orientation judgments. Vision Research. 42(3). 311–330. 4 indexed citations
15.
Zhao, Li & Charles Chubb. (2001). The size-tuning of the face-distortion after-effect. Vision Research. 41(23). 2979–2994. 151 indexed citations
16.
Chubb, Charles, Lynn A. Olzak, & Andrew M. Derrington. (2001). Second-order processes in vision: introduction. Journal of the Optical Society of America A. 18(9). 2175–2175. 19 indexed citations
17.
Chubb, Charles & John I. Yellott. (2000). Every discrete, finite image is uniquely determined by its dipole histogram. Vision Research. 40(5). 485–492. 20 indexed citations
18.
Nam, Jong-Ho & Charles Chubb. (2000). Texture luminance judgments are approximately veridical. Vision Research. 40(13). 1695–1709. 19 indexed citations
19.
Chubb, Charles, Zhong‐Lin Lu, & George Sperling. (1997). Structure detection: a statistically certified unsupervised learning procedure. Vision Research. 37(23). 3343–3365. 4 indexed citations
20.
Werkhoven, Peter, George Sperling, & Charles Chubb. (1992). Energy computations in motion and texture. Optical Society of America Annual Meeting. FOO4–FOO4. 2 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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