Richard A. Chechile

1.2k total citations
67 papers, 895 citations indexed

About

Richard A. Chechile is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Statistics and Probability. According to data from OpenAlex, Richard A. Chechile has authored 67 papers receiving a total of 895 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Cognitive Neuroscience, 17 papers in Artificial Intelligence and 17 papers in Statistics and Probability. Recurrent topics in Richard A. Chechile's work include Memory Processes and Influences (22 papers), Visual and Cognitive Learning Processes (9 papers) and Statistical Methods and Bayesian Inference (7 papers). Richard A. Chechile is often cited by papers focused on Memory Processes and Influences (22 papers), Visual and Cognitive Learning Processes (9 papers) and Statistical Methods and Bayesian Inference (7 papers). Richard A. Chechile collaborates with scholars based in United States. Richard A. Chechile's co-authors include Sal A. Soraci, Charles L. Richman, Donald L. Meyer, Jeffery J. Franks, Michael T. Carlin, Alan Cooke, Frank T.‐C. Tsai, Robert F. Belli, John D. Bransford and George Smith and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Applied Psychology and Psychological Review.

In The Last Decade

Richard A. Chechile

64 papers receiving 810 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richard A. Chechile United States 20 506 270 191 149 138 67 895
Stephen W. Link Canada 11 437 0.9× 89 0.3× 116 0.6× 176 1.2× 67 0.5× 22 727
K. I. Manktelow United Kingdom 11 156 0.3× 387 1.4× 255 1.3× 150 1.0× 77 0.6× 26 917
Matthew J. Dry Australia 16 149 0.3× 178 0.7× 131 0.7× 134 0.9× 93 0.7× 34 774
Janet L. Lachman United States 8 320 0.6× 100 0.4× 223 1.2× 179 1.2× 85 0.6× 11 755
Marsha C. Lovett United States 15 296 0.6× 243 0.9× 279 1.5× 241 1.6× 78 0.6× 48 971
Joseph W. Houpt United States 14 462 0.9× 87 0.3× 70 0.4× 224 1.5× 176 1.3× 64 709
Chris R. Sims United States 13 429 0.8× 123 0.5× 94 0.5× 144 1.0× 130 0.9× 41 733
Rani Moran United Kingdom 19 899 1.8× 131 0.5× 98 0.5× 220 1.5× 140 1.0× 49 1.2k
Detlef Rhenius Germany 4 214 0.4× 114 0.4× 76 0.4× 331 2.2× 62 0.4× 6 522
Maxwell J. Roberts United Kingdom 18 147 0.3× 200 0.7× 251 1.3× 190 1.3× 88 0.6× 44 800

Countries citing papers authored by Richard A. Chechile

Since Specialization
Citations

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

Fields of papers citing papers by Richard A. Chechile

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard A. Chechile. 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 Richard A. Chechile. The network helps show where Richard A. Chechile may publish in the future.

Co-authorship network of co-authors of Richard A. Chechile

This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Chechile. A scholar is included among the top collaborators of Richard A. Chechile 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 Richard A. Chechile. Richard A. Chechile 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.
Chechile, Richard A.. (2023). Bertrand’s Paradox Resolution and Its Implications for the Bing–Fisher Problem. Mathematics. 11(15). 3282–3282. 1 indexed citations
2.
Chechile, Richard A.. (2014). Using a multinomial tree model for detecting mixtures in perceptual detection. Frontiers in Psychology. 5. 641–641.
3.
Chechile, Richard A., et al.. (2013). Using logarithmic derivative functions for assessing the risky weighting function for binary gambles. Journal of Mathematical Psychology. 57(1-2). 15–28. 9 indexed citations
4.
Chechile, Richard A., et al.. (2013). Reformulating Markovian processes for learning and memory from a hazard function framework. Journal of Mathematical Psychology. 59. 65–81. 5 indexed citations
5.
Chechile, Richard A.. (2011). Properties of reverse hazard functions. Journal of Mathematical Psychology. 55(3). 203–222. 36 indexed citations
6.
Chechile, Richard A.. (2006). Memory hazard functions: A vehicle for theory development and test.. Psychological Review. 113(1). 31–56. 30 indexed citations
7.
Chechile, Richard A.. (2004). New multinomial models for the Chechile–Meyer task. Journal of Mathematical Psychology. 48(6). 364–384. 18 indexed citations
8.
Soraci, Sal A., Michael T. Carlin, Michael P. Toglia, Richard A. Chechile, & Jeffrey S. Neuschatz. (2003). Generative processing and false memories: When there is no cost.. Journal of Experimental Psychology Learning Memory and Cognition. 29(4). 511–523. 18 indexed citations
9.
Chechile, Richard A.. (2001). BAYESIAN ANALYSIS OF GUMBEL DISTRIBUTED DATA. Communication in Statistics- Theory and Methods. 30(3). 485–496. 5 indexed citations
10.
Soraci, Sal A., et al.. (2000). “Aha” effects in the generation of pictures. Memory & Cognition. 28(6). 939–948. 34 indexed citations
11.
Chechile, Richard A.. (1999). A vector-based goodness-of-fit metric for interval-scaled data. Communication in Statistics- Theory and Methods. 28(2). 277–296. 2 indexed citations
12.
Soraci, Sal A., et al.. (1999). Encoding Variability and Cuing in Generative Processing. Journal of Memory and Language. 41(4). 541–559. 28 indexed citations
13.
Chechile, Richard A. & Sal A. Soraci. (1999). Evidence for a Multiple-process Account of the Generation Effect. Memory. 7(4). 483–508. 23 indexed citations
14.
Smith, George, et al.. (1996). Identifying Impediments to Learning Probability and Statistics From an Assessment of Instructional Software. Journal of Educational and Behavioral Statistics. 21(1). 35–54. 22 indexed citations
15.
Chechile, Richard A., et al.. (1996). A syntactic complexity effect with visual patterns: Evidence for the syntactic nature of the memory representation.. Journal of Experimental Psychology Learning Memory and Cognition. 22(3). 654–669. 17 indexed citations
16.
Chechile, Richard A., et al.. (1996). Overview of ConStatS and the ConStatS assessment. 2 indexed citations
17.
Soraci, Sal A., Jeffery J. Franks, John D. Bransford, et al.. (1994). Incongruous item generation effects: A multiple-cue perspective.. Journal of Experimental Psychology Learning Memory and Cognition. 20(1). 67–78. 18 indexed citations
18.
Eggleston, Robert G., et al.. (1986). Modeling the Cognitive Complexity of Visual Displays. Proceedings of the Human Factors Society Annual Meeting. 30(7). 675–678. 4 indexed citations
19.
Chechile, Richard A., et al.. (1983). Long-Term Storage Losses: A Dilemma for Multistore Models. The Journal of General Psychology. 109(1). 15–30. 10 indexed citations
20.
Chechile, Richard A., et al.. (1976). Storage-Retrieval Analysis of Paired-Associate Acquisition.. 4 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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