Kalif E. Vaughn
- Cognitive Neuroscience top 10%
- Developmental and Educational Psychology top 5%
- Artificial Intelligence top 10%
- Experimental and Cognitive Psychology top 10%
- Social Psychology
- Co-authors
- Katherine A. RawsonNate KornellJohn DunloskyShana K. CarpenterMary A. PycMatthew M. WalshLaura E. KnouseLaura Smalarz
- Topics
- Memory Processes and Influences (13 papers)Intelligent Tutoring Systems and Adaptive Learning (7 papers)Visual and Cognitive Learning Processes (4 papers)
- Cited by
- Developmental and Educational PsychologyCognitive NeuroscienceExperimental and Cognitive Psychology
- Journals
- Psychological ScienceJournal of Experimental Psychology Learning Memory and CognitionJournal of Memory and Language
- Partner nations
- United StatesAustralia
In The Last Decade
Kalif E. Vaughn
14 papers receiving 288 citations
Peers
Comparison fields: 5 of 47
- Cognitive Neuroscience 225
- Developmental and Educational Psychology 155
- Artificial Intelligence 120
- Experimental and Cognitive Psychology 90
- Social Psychology 37
Countries citing papers authored by Kalif E. Vaughn
This map shows the geographic impact of Kalif E. Vaughn'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 Kalif E. Vaughn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kalif E. Vaughn more than expected).
Fields of papers citing papers by Kalif E. Vaughn
This network shows the impact of papers produced by Kalif E. Vaughn. 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 Kalif E. Vaughn. The network helps show where Kalif E. Vaughn may publish in the future.
Co-authorship network of co-authors of Kalif E. Vaughn
This figure shows the co-authorship network connecting the top 25 collaborators of Kalif E. Vaughn. A scholar is included among the top collaborators of Kalif E. Vaughn 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 Kalif E. Vaughn. Kalif E. Vaughn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 0 | |
| 4 | 14 | |
| 5 | 9 | |
| 6 | 7 | |
| 7 | 21 | |
| 8 | 24 | |
| 9 | 15 | |
| 10 | 7 | |
| 11 | 11 | |
| 12 | 44 | |
| 13 | 13 | |
| 14 | 25 | |
| 15 | 53 | |
| 16 | 54 |
About Kalif E. Vaughn
Kalif E. Vaughn is a scholar working on Cognitive Neuroscience, Experimental and Cognitive Psychology and Artificial Intelligence, having authored 16 papers that have together received 303 indexed citations. Recurring topics across this work include Memory Processes and Influences (13 papers), Intelligent Tutoring Systems and Adaptive Learning (7 papers) and Visual and Cognitive Learning Processes (4 papers). The work is most often cited by research in Developmental and Educational Psychology (155 citations), Cognitive Neuroscience (225 citations) and Experimental and Cognitive Psychology (90 citations). Kalif E. Vaughn has collaborated with scholars based in United States and Australia. Frequent co-authors include Katherine A. Rawson, Nate Kornell, John Dunlosky, Shana K. Carpenter, Mary A. Pyc, Matthew M. Walsh, Laura E. Knouse, Laura Smalarz, Kathryn T. Wissman and Matthew A. Palmer. Their work appears in journals such as Psychological Science, Journal of Experimental Psychology Learning Memory and Cognition and Journal of Memory and Language.
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.