Donald Bamber

2.4k total citations · 1 hit paper
30 papers, 1.8k citations indexed

About

Donald Bamber is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Computational Theory and Mathematics. According to data from OpenAlex, Donald Bamber has authored 30 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 11 papers in Cognitive Neuroscience and 5 papers in Computational Theory and Mathematics. Recurrent topics in Donald Bamber's work include Bayesian Modeling and Causal Inference (15 papers), Logic, Reasoning, and Knowledge (10 papers) and Semantic Web and Ontologies (5 papers). Donald Bamber is often cited by papers focused on Bayesian Modeling and Causal Inference (15 papers), Logic, Reasoning, and Knowledge (10 papers) and Semantic Web and Ontologies (5 papers). Donald Bamber collaborates with scholars based in United States and Germany. Donald Bamber's co-authors include Jan P. H. van Santen, Victor Manifold, David M. Riefer, William H. Batchelder, Geoffrey R. Loftus, I. R. Goodman, Hung T. Nguyen, Kimberly A. Jameson, F. Gregory Ashby and Hung T. Nguyen and has published in prestigious journals such as Journal of Experimental Psychology Learning Memory and Cognition, Information Sciences and Psychological Assessment.

In The Last Decade

Donald Bamber

25 papers receiving 1.7k citations

Hit Papers

The area above the ordinal dominance graph and the area b... 1975 2026 1992 2009 1975 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Donald Bamber United States 11 548 472 372 223 185 30 1.8k
David J. Getty United States 20 534 1.0× 257 0.5× 116 0.3× 177 0.8× 69 0.4× 33 1.5k
Ronald M. Pickett United States 15 227 0.4× 416 0.9× 144 0.4× 108 0.5× 177 1.0× 26 1.9k
R. Clifford Blair United States 23 1.3k 2.4× 134 0.3× 623 1.7× 224 1.0× 119 0.6× 60 3.3k
Don van Ravenzwaaij Netherlands 23 868 1.6× 205 0.4× 210 0.6× 496 2.2× 197 1.1× 70 2.2k
Willem J. Heiser Netherlands 31 176 0.3× 355 0.8× 345 0.9× 426 1.9× 36 0.2× 86 2.6k
Nancy R. Mendell United States 33 432 0.8× 197 0.4× 235 0.6× 125 0.6× 30 0.2× 104 3.1k
Torrin M. Liddell United States 8 349 0.6× 163 0.3× 164 0.4× 247 1.1× 96 0.5× 12 1.6k
Leo Katz United States 10 224 0.4× 100 0.2× 197 0.5× 155 0.7× 92 0.5× 43 1.5k
Brandon M. Turner United States 27 1.3k 2.4× 387 0.8× 210 0.6× 405 1.8× 61 0.3× 77 2.4k
Edward F. Alf United States 13 176 0.3× 195 0.4× 287 0.8× 105 0.5× 215 1.2× 38 1.3k

Countries citing papers authored by Donald Bamber

Since Specialization
Citations

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

Fields of papers citing papers by Donald Bamber

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Donald Bamber

This figure shows the co-authorship network connecting the top 25 collaborators of Donald Bamber. A scholar is included among the top collaborators of Donald Bamber 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 Donald Bamber. Donald Bamber 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.
Ashby, F. Gregory & Donald Bamber. (2022). State trace analysis: What it can and cannot do. Journal of Mathematical Psychology. 108. 102655–102655. 4 indexed citations
2.
Bamber, Donald. (2013). Entailment in Probability of Thresholded Generalizations. arXiv (Cornell University). 57–64.
3.
Bamber, Donald, I. R. Goodman, Arjun K. Gupta, & Hung T. Nguyen. (2010). Use of the global implicit function theorem to induce singular conditional distributions on surfaces in n dimensions: Part I. Random Operators and Stochastic Equations. 18(4). 355–389.
4.
Bamber, Donald, I. R. Goodman, & Hung T. Nguyen. (2004). Deduction from conditional knowledge. Soft Computing. 8(4). 247–255. 5 indexed citations
5.
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
6.
Goodman, I. R., et al.. (2002). New applications of relational event algebra to fuzzy quantification and probabilistic reasoning. Information Sciences. 148(1-4). 87–96. 3 indexed citations
7.
Bamber, Donald & I. R. Goodman. (2001). Reasoning with Assertions of High Conditional Probability: Entailment with Universal Near Surety.. 17–26. 2 indexed citations
8.
Bamber, Donald, et al.. (2001). <title>Complexity reducing algorithm for near optimal fusion (CRANOF) with application to tracking and information fusion</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4380. 269–280. 4 indexed citations
9.
Jameson, Kimberly A., et al.. (2001). Color coding information: Assessing alternative coding systems using independent brightness and hue dimensions.. Journal of Experimental Psychology Applied. 7(2). 112–128. 5 indexed citations
10.
Bamber, Donald. (2000). Entailment with Near Surety of Scaled Assertions of High Conditional Probability. Journal of Philosophical Logic. 29(1). 1–74. 14 indexed citations
11.
Bamber, Donald. (1994). Probabilistic entailment of conditionals by conditionals. IEEE Transactions on Systems Man and Cybernetics. 24(12). 1714–1723. 7 indexed citations
12.
Loftus, Geoffrey R. & Donald Bamber. (1990). Learning-forgetting independence, unidimensional memory models, and feature models: Comment on Bogartz (1990).. Journal of Experimental Psychology Learning Memory and Cognition. 16(5). 916–926. 24 indexed citations
13.
Santen, Jan P. H. van & Donald Bamber. (1981). Finite and infinite state confusion models. Journal of Mathematical Psychology. 24(2). 101–111. 5 indexed citations
14.
Bamber, Donald. (1979). State-trace analysis: A method of testing simple theories of causation. Journal of Mathematical Psychology. 19(2). 137–181. 108 indexed citations
15.
Bamber, Donald & Victor Manifold. (1978). Evaluation of a method for studying forgetting: Is data from split-half recognition tests contaminated by test interference?. Bulletin of the Psychonomic Society. 11(2). 126–128. 2 indexed citations
16.
Bamber, Donald. (1975). The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology. 12(4). 387–415. 1060 indexed citations breakdown →
17.
Bamber, Donald, et al.. (1975). Reaction times in a task analogous to “same”-“different” judgment. Perception & Psychophysics. 18(5). 321–327. 14 indexed citations
18.
Bamber, Donald. (1974). A method for finding the maximum of a function of several variables suitable for use on a programmable desk calculator. Computers and Biomedical Research. 7(2). 183–187. 2 indexed citations
19.
Bamber, Donald. (1972). Reaction times and error rates for judging nominal identity of letter strings. Perception & Psychophysics. 12(4). 321–326. 32 indexed citations
20.
Bamber, Donald. (1969). Reaction times and error rates for “same”-“different” judgments of multidimensional stimull. Perception & Psychophysics. 6(3). 169–174. 249 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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