Daniel D. Dilks

3.9k citations
64 papers · 2.5k · h-index 27

Impact in

    • Face Recognition and Perception
    • Visual perception and processing mechanisms
    • Neural dynamics and brain function
    • Memory and Neural Mechanisms
    • Functional Brain Connectivity Studies

Papers in

Daniel D. Dilks

62 papers receiving 2.5k citations

Peers

Daniel D. Dilks
Comparison fields: 5 of 119
  • Cognitive Neuroscience 2.0k
  • Sensory Systems 155
  • Experimental and Cognitive Psychology 396
  • Computer Vision and Pattern Recognition 439
  • Ophthalmology 152
Replace Dwight J. Kravitz with:
Dwight J. Kravitz United States
Mark E. McCourt United States
Erik Blaser United States
Stephan A. Brandt Germany
Uri Polat Israel
Timothy J. Andrews United Kingdom
Carlo A. Marzi Italy
Marko Nardini United Kingdom
Scott D. Slotnick United States
Duje Tadin United States
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Citations per year

Countries citing papers authored by Daniel D. Dilks

Since Specialization
Citations

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

Fields of papers citing papers by Daniel D. Dilks

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel D. Dilks, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel D. Dilks Line = papers co-authored together Daniel D. Dilks links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011325
2 2013233
3 2017207
4 2012162
5 2011125
6 2011111
7 2008109
8 200997
9 201686
10 201382
11 200775
12 201773
13 201563
14 202053
15 201650
16 201443
17 201643
18 202041
19 200036
20 201936

About Daniel D. Dilks

Daniel D. Dilks is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition, Molecular Biology, Automotive Engineering and Sensory Systems, having authored 64 papers that have together received 2.5k indexed citations. Recurring topics across this work include Face Recognition and Perception (31 papers), Visual perception and processing mechanisms (30 papers), Neural dynamics and brain function (16 papers), Memory and Neural Mechanisms (15 papers), Visual Attention and Saliency Detection (9 papers), Olfactory and Sensory Function Studies (5 papers), Spatial Cognition and Navigation (5 papers) and Retinal Development and Disorders (5 papers). The work is most often cited by research in Cognitive Neuroscience (2.0k citations), Sensory Systems (155 citations), Experimental and Cognitive Psychology (396 citations), Computer Vision and Pattern Recognition (439 citations) and Ophthalmology (152 citations). Daniel D. Dilks has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Nancy Kanwisher, Joshua B. Julian, Rebecca Saxe, Andrew S. Persichetti, Frederik S. Kamps, Christina Triantafyllou, David Pitcher, Eli Peli, Alexander Paunov and Chris I. Baker. Their work appears in journals such as Journal of Vision, Journal of Neuroscience, NeuroImage, Cerebral Cortex and Cortex.

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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