Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of John E. Opfer'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 John E. Opfer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John E. Opfer more than expected).
This network shows the impact of papers produced by John E. Opfer. 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 John E. Opfer. The network helps show where John E. Opfer may publish in the future.
Co-authorship network of co-authors of John E. Opfer
This figure shows the co-authorship network connecting the top 25 collaborators of John E. Opfer.
A scholar is included among the top collaborators of John E. Opfer 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 John E. Opfer. John E. Opfer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yu, Shuyuan, et al.. (2020). From Integers to Fractions: Developing a Coherent Understanding of Proportional Magnitude.. Cognitive Science.3 indexed citations
Opfer, John E., et al.. (2015). Development of Numerosity Estimation: A Linear to Logarithmic Shift?. Cognitive Science.2 indexed citations
11.
Opfer, John E. & Clarissa A. Thompson. (2014). Numerical Estimation Under Supervision. Cognitive Science. 36(36).1 indexed citations
12.
Young, C., et al.. (2011). Linear Numerical Magnitude Representations Aid Memory for Single Numbers. Cognitive Science. 33(33).2 indexed citations
13.
Thompson, Clarissa A. & John E. Opfer. (2011). Learning Linear Spatial-Numeric Associations Improves Memory for Numbers. Cognitive Science. 33(33).1 indexed citations
14.
Opfer, John E., et al.. (2010). Electrophysiological Evidence for Multiple Representations of Number in the Human Brain. eScholarship (California Digital Library). 32(32).2 indexed citations
15.
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
16.
Opfer, John E., et al.. (2009). Why Children’s Number-line Estimates Follow Fechner’s Law. eScholarship (California Digital Library). 31(31).1 indexed citations
17.
Opfer, John E., et al.. (2007). Why children make better estimates of fractional magnitude than adults. eScholarship (California Digital Library). 29(29).2 indexed citations
18.
Opfer, John E. & Clarissa A. Thompson. (2006). Even Early Representations of Numerical Magnitude are Spatially Organized: Evidence for a Directional Magnitude Bias in Pre-Reading Preschoolers. eScholarship (California Digital Library). 28(28).18 indexed citations
19.
Opfer, John E. & Clarissa A. Thompson. (2005). Microgenetic Changes in Representations of Numerical Magnitude. eScholarship (California Digital Library). 27(27).3 indexed citations
20.
Opfer, John E.. (2000). Developing a biological understanding of goal -directed action.. Deep Blue (University of Michigan).1 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.