Danielle V. Dellarco

3.2k citations
8 papers · 428 · h-index 7

Impact in

Papers in

Danielle V. Dellarco

8 papers receiving 416 citations

Peers

Danielle V. Dellarco
Comparison fields: 5 of 67
  • Cognitive Neuroscience 197
  • Experimental and Cognitive Psychology 111
  • Behavioral Neuroscience 29
  • Applied Psychology 38
  • General Decision Sciences 15
Replace Sarah M. Tashjian with:
Sarah M. Tashjian United States
Kesong Hu United States
Konstantina Zougkou United Kingdom
Jiang Qiu China
Keanan J. Joyner United States
Verena Ly Netherlands
Mara van der Meulen Netherlands
Megan E. Speer United States
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Citations per field
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Citations per year

Countries citing papers authored by Danielle V. Dellarco

Since Specialization
Citations

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

Fields of papers citing papers by Danielle V. Dellarco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danielle V. Dellarco. 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 Danielle V. Dellarco. The network helps show where Danielle V. Dellarco may publish in the future.

Co-authors

The 25 scholars most cited alongside Danielle V. Dellarco, 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 Danielle V. Dellarco Line = papers co-authored together Danielle V. Dellarco links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 2016157
2 201892
3 201756
4 201837
5 201933
6 201632
7 201518
8 20173

About Danielle V. Dellarco

Danielle V. Dellarco is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology, Clinical Psychology, General Decision Sciences and Cellular and Molecular Neuroscience, having authored 8 papers that have together received 428 indexed citations. Recurring topics across this work include Neural and Behavioral Psychology Studies (4 papers), Functional Brain Connectivity Studies (4 papers), Child and Adolescent Psychosocial and Emotional Development (2 papers), Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (2 papers), Decision-Making and Behavioral Economics (2 papers), Behavioral Health and Interventions (1 paper), Face Recognition and Perception (1 paper) and Psychology of Moral and Emotional Judgment (1 paper). The work is most often cited by research in Cognitive Neuroscience (197 citations), Experimental and Cognitive Psychology (111 citations), Behavioral Neuroscience (29 citations), Applied Psychology (38 citations) and General Decision Sciences (15 citations). Danielle V. Dellarco has collaborated with scholars based in United States. Frequent co-authors include B.J. Casey, Alexandra O. Cohen, Jason Chein, Adriana Galván, Kaitlyn Breiner, Damien A. Fair, Marc D. Rudolph, Elizabeth S. Scott, Kim Taylor‐Thompson and Jennifer A. Richeson. Their work appears in journals such as Psychiatry Research, Psychological Science, PLoS ONE, Journal of Cognitive Neuroscience and Developmental Psychobiology.

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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