David M. Riefer

2.5k total citations
26 papers, 1.9k citations indexed

About

David M. Riefer is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Developmental and Educational Psychology. According to data from OpenAlex, David M. Riefer has authored 26 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Cognitive Neuroscience, 8 papers in Artificial Intelligence and 7 papers in Developmental and Educational Psychology. Recurrent topics in David M. Riefer's work include Memory Processes and Influences (8 papers), Child and Animal Learning Development (5 papers) and Visual and Cognitive Learning Processes (3 papers). David M. Riefer is often cited by papers focused on Memory Processes and Influences (8 papers), Child and Animal Learning Development (5 papers) and Visual and Cognitive Learning Processes (3 papers). David M. Riefer collaborates with scholars based in United States. David M. Riefer's co-authors include William H. Batchelder, Xiangen Hu, Jeffrey N. Rouder, Donald Bamber, Victor Manifold, Diane F. Halpern, Carol D. Hansen, George J. Andersen, Myron L. Braunstein and Yu-Chin Chien and has published in prestigious journals such as Psychological Review, Journal of Educational Psychology and Journal of Experimental Psychology Learning Memory and Cognition.

In The Last Decade

David M. Riefer

25 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David M. Riefer United States 18 1.2k 696 414 401 397 26 1.9k
Géry d’Ydewalle Belgium 29 1.3k 1.0× 435 0.6× 263 0.6× 713 1.8× 676 1.7× 135 2.7k
Heinz‐Martin Süß Germany 16 1.0k 0.8× 317 0.5× 299 0.7× 1.1k 2.8× 476 1.2× 35 2.0k
Chris Donkin Australia 28 1.6k 1.3× 327 0.5× 235 0.6× 597 1.5× 292 0.7× 79 2.4k
Koen Lamberts United Kingdom 23 917 0.7× 243 0.3× 348 0.8× 325 0.8× 499 1.3× 65 2.0k
Peter A. White United Kingdom 24 772 0.6× 359 0.5× 583 1.4× 376 0.9× 714 1.8× 100 1.8k
Moreno I. Coco United Kingdom 17 873 0.7× 409 0.6× 354 0.9× 569 1.4× 752 1.9× 52 2.0k
Jeffrey J. Starns United States 22 1.6k 1.3× 388 0.6× 436 1.1× 283 0.7× 251 0.6× 60 1.8k
Roger L. Dominowski United States 18 551 0.4× 323 0.5× 212 0.5× 593 1.5× 613 1.5× 41 1.6k
Adam N. Sanborn United Kingdom 18 638 0.5× 457 0.7× 151 0.4× 399 1.0× 298 0.8× 56 1.6k
John Paul Minda Canada 23 684 0.6× 456 0.7× 313 0.8× 539 1.3× 1.2k 3.0× 49 2.0k

Countries citing papers authored by David M. Riefer

Since Specialization
Citations

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

Fields of papers citing papers by David M. Riefer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David M. Riefer. 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 David M. Riefer. The network helps show where David M. Riefer may publish in the future.

Co-authorship network of co-authors of David M. Riefer

This figure shows the co-authorship network connecting the top 25 collaborators of David M. Riefer. A scholar is included among the top collaborators of David M. Riefer 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 David M. Riefer. David M. Riefer 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.
Riefer, David M., et al.. (2006). Positive and Negative Generation Effects in Source Monitoring. Quarterly Journal of Experimental Psychology. 60(10). 1389–1405. 24 indexed citations
2.
Riefer, David M., et al.. (2002). Cognitive psychometrics: Assessing storage and retrieval deficits in special populations with multinomial processing tree models.. Psychological Assessment. 14(2). 184–201. 72 indexed citations
3.
Riefer, David M., et al.. (2002). Cognitive psychometrics: Assessing storage and retrieval deficits in special populations with multinomial processing tree models.. Psychological Assessment. 14(2). 184–201. 57 indexed citations
4.
Batchelder, William H. & David M. Riefer. (1999). Theoretical and empirical review of multinomial process tree modeling. Psychonomic Bulletin & Review. 6(1). 57–86. 484 indexed citations
5.
Riefer, David M., et al.. (1998). Memory for common and bizarre stimuli: A storage-retrieval analysis. Psychonomic Bulletin & Review. 5(2). 312–317. 19 indexed citations
6.
Riefer, David M. & William H. Batchelder. (1995). A multinomial modeling analysis of the recognition-failure paradigm. Memory & Cognition. 23(5). 611–630. 28 indexed citations
7.
Batchelder, William H., David M. Riefer, & Xiangen Hu. (1994). Measuring memory factors in source monitoring: Reply to Kinchla.. Psychological Review. 101(1). 172–176. 41 indexed citations
8.
Batchelder, William H., David M. Riefer, & Xiangen Hu. (1994). Measuring memory factors in source monitoring: Reply to Kinchla.. Psychological Review. 101(1). 172–176. 28 indexed citations
9.
Riefer, David M., Xiangen Hu, & William H. Batchelder. (1994). Response strategies in source monitoring.. Journal of Experimental Psychology Learning Memory and Cognition. 20(3). 680–693. 8 indexed citations
10.
Riefer, David M., Xiangen Hu, & William H. Batchelder. (1994). Response strategies in source monitoring.. Journal of Experimental Psychology Learning Memory and Cognition. 20(3). 680–693. 80 indexed citations
11.
Riefer, David M. & Jeffrey N. Rouder. (1992). A multinomial modeling analysis of the mnemonic benefits of bizarre imagery. Memory & Cognition. 20(6). 601–611. 52 indexed citations
12.
Riefer, David M.. (1991). Behavior Engineering Proposals: 4. Is ‘Backwards Reading’ an Effective Proofreading Strategy?. Perceptual and Motor Skills. 73(3). 767–777. 2 indexed citations
13.
Riefer, David M. & William H. Batchelder. (1991). Age differences in storage and retrieval: A multinomial modeling analysis. Bulletin of the Psychonomic Society. 29(5). 415–418. 23 indexed citations
14.
Halpern, Diane F., Carol D. Hansen, & David M. Riefer. (1990). Analogies as an aid to understanding and memory.. Journal of Educational Psychology. 82(2). 298–305. 5 indexed citations
15.
Batchelder, William H. & David M. Riefer. (1990). Multinomial processing models of source monitoring.. Psychological Review. 97(4). 548–564. 332 indexed citations
16.
Batchelder, William H. & David M. Riefer. (1990). Multinomial processing models of source monitoring.. Psychological Review. 97(4). 548–564. 16 indexed citations
17.
Riefer, David M. & William H. Batchelder. (1988). Multinomial modeling and the measurement of cognitive processes.. Psychological Review. 95(3). 318–339. 21 indexed citations
18.
Riefer, David M. & William H. Batchelder. (1988). Multinomial modeling and the measurement of cognitive processes.. Psychological Review. 95(3). 318–339. 332 indexed citations
19.
Riefer, David M. & William H. Batchelder. (1987). Further tests of a model for measuring storage and retrieval. 2 indexed citations
20.
Riefer, David M.. (1982). The advantages of mathematical modeling over traditional methods in the analysis of category clustering. Journal of Mathematical Psychology. 26(2). 97–123. 7 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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