This map shows the geographic impact of Mark A. Pitt'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 Mark A. Pitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark A. Pitt more than expected).
This network shows the impact of papers produced by Mark A. Pitt. 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 Mark A. Pitt. The network helps show where Mark A. Pitt may publish in the future.
Co-authorship network of co-authors of Mark A. Pitt
This figure shows the co-authorship network connecting the top 25 collaborators of Mark A. Pitt.
A scholar is included among the top collaborators of Mark A. Pitt 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 Mark A. Pitt. Mark A. Pitt is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dumay, Nicolas, et al.. (2020). Context variability promotes generalization in reading aloud: Insight from a neural network simulation.. Cognitive Science.1 indexed citations
7.
Myung, Jay I., et al.. (2020). The Scaled Target Learning Model: A Novel Computational Model of the Balloon Analogue Risk Task.. Cognitive Science.1 indexed citations
8.
Zhang, Byoung‐Tak, et al.. (2019). Modeling Delay Discounting using Gaussian Process with Active Learning.. Cognitive Science. 1479–1485.1 indexed citations
9.
Lee, Sang Ho, et al.. (2019). Active Learning for a Number-Line Task with Two Design Variables.. Cognitive Science. 638.1 indexed citations
Dilley, Laura C., et al.. (2015). Rate-dependent speech processing can be speech-specific: Evidence from the disappearance of words under changes in context speech rate.. ICPhS.1 indexed citations
12.
Tang, Yun, Christopher J. Young, Jay I. Myung, Mark A. Pitt, & John E. Opfer. (2010). Optimal Inference and Feedback for Representational Change. eScholarship (California Digital Library). 32(32).5 indexed citations
13.
Myung, Jay I. & Mark A. Pitt. (2010). Cognitive Modeling Repository. eScholarship (California Digital Library). 32(32).6 indexed citations
14.
Cavagnaro, Daniel R., Jay I. Myung, Mark A. Pitt, & Yun Tang. (2009). Better data with fewer participants and trials: improving experiment efficiency with adaptive design optimization. eScholarship (California Digital Library). 31(31).4 indexed citations
15.
Cavagnaro, Daniel R., Jay I. Myung, & Mark A. Pitt. (2009). Adaptive Design Optimization in Experiments with People. Neural Information Processing Systems. 22. 234–242.8 indexed citations
16.
Myung, In Jae, et al.. (2005). Advances in Minimum Description Length: Theory and Applications (Neural Information Processing). The MIT Press eBooks.18 indexed citations
17.
Grünwald, Peter, In Jae Myung, & Mark A. Pitt. (2005). Advances in Minimum Description Length: Theory and Applications. Data Archiving and Networked Services (DANS).276 indexed citations
18.
Kim, Woojae, Danielle Navarro, Mark A. Pitt, & In Jae Myung. (2003). An MCMC-Based Method of Comparing Connectionist Models in Cognitive Science. Neural Information Processing Systems. 16. 937–944.6 indexed citations
19.
Navarro, Danielle, In Jae Myung, Mark A. Pitt, & Woojae Kim. (2003). Global Model Analysis by Landscaping. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 25(25).6 indexed citations
20.
Myung, In Jae, Mark A. Pitt, Shaobo Zhang, & Vijay Balasubramanian. (2000). The Use of MDL to Select among Computational Models of Cognition. Neural Information Processing Systems. 13. 38–44.4 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.