This map shows the geographic impact of Bob Rehder'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 Bob Rehder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bob Rehder more than expected).
This network shows the impact of papers produced by Bob Rehder. 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 Bob Rehder. The network helps show where Bob Rehder may publish in the future.
Co-authorship network of co-authors of Bob Rehder
This figure shows the co-authorship network connecting the top 25 collaborators of Bob Rehder.
A scholar is included among the top collaborators of Bob Rehder 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 Bob Rehder. Bob Rehder is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rehder, Bob, et al.. (2021). Testing a Process Model of Causal Reasoning With Inhibitory Causal Links. eScholarship (California Digital Library). 43(43).1 indexed citations
4.
Bramley, Neil R, et al.. (2018). A causal model approach to dynamic control. Cognitive Science. 281–286.
5.
Bramley, Neil R, et al.. (2018). Causal structure learning with continuous variables in continuous time. Cognitive Science. 287–292.1 indexed citations
6.
Rehder, Bob, et al.. (2017). The Causal Sampler: A Sampling Approach to Causal Representation, Reasoning, and Learning.. Cognitive Science.5 indexed citations
7.
Rehder, Bob, et al.. (2016). Evaluating Causal Hypotheses: The Curious Case of Correlated Cues.. Cognitive Science.3 indexed citations
8.
Coenen, Anna, Bob Rehder, & Todd M. Gureckis. (2014). Decisions to intervene on causal systems are adaptively selected. Cognitive Science. 36(36).4 indexed citations
9.
Rehder, Bob & Jay B. Martin. (2011). A Generative Model of Causal Cycles. Cognitive Science. 33(33).7 indexed citations
10.
Williams, Joseph Jay, Tania Lombrozo, & Bob Rehder. (2011). Explaining drives the discovery of real and illusory patterns. Cognitive Science. 33(33).4 indexed citations
Rehder, Bob, et al.. (2009). A new theory of classification and feature inference learning: An exemplar fragment model. Proceedings of the Annual Meeting of the Cognitive Science Society. 31(31). 371–376.3 indexed citations
Rehder, Bob, et al.. (2007). Bias Toward Sufficiency and Completeness in Causal Explanations. eScholarship (California Digital Library). 29(29).2 indexed citations
15.
Rehder, Bob, et al.. (2007). Causal status, coherence, and essentialized categories. eScholarship (California Digital Library). 29(29).1 indexed citations
16.
Rehder, Bob. (2006). Human Deviations from Normative Causal Reasoning. eScholarship (California Digital Library). 28(28).2 indexed citations
17.
Harris, Harlan D. & Bob Rehder. (2006). Modeling Category Learning with Exemplars and Prior Knowledge. eScholarship (California Digital Library). 28(28). 1440–1445.4 indexed citations
18.
Rehder, Bob, et al.. (2006). Classifying with Essentialized Categories. eScholarship (California Digital Library). 28(28).1 indexed citations
19.
Rehder, Bob & Aaron B. Hoffman. (2003). Eyetracking and Selective Attention in Category Learning. eScholarship (California Digital Library). 25(25).2 indexed citations
20.
Rehder, Bob, Michael L. Littman, Susan Dumais, & Thomas K. Landauer. (1997). Automatic 3-language cross-language information retrieval with latent semantic indexing. Text REtrieval Conference. 233–239.22 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.