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.
Neural reuse: A fundamental organizational principle of the brain
Citations per year, relative to Michael L. Anderson Michael L. Anderson (= 1×)
peers
James A. Dixon
Countries citing papers authored by Michael L. Anderson
Since
Specialization
Citations
This map shows the geographic impact of Michael L. Anderson'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 Michael L. Anderson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael L. Anderson more than expected).
Fields of papers citing papers by Michael L. Anderson
This network shows the impact of papers produced by Michael L. Anderson. 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 Michael L. Anderson. The network helps show where Michael L. Anderson may publish in the future.
Co-authorship network of co-authors of Michael L. Anderson
This figure shows the co-authorship network connecting the top 25 collaborators of Michael L. Anderson.
A scholar is included among the top collaborators of Michael L. Anderson 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 Michael L. Anderson. Michael L. Anderson 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.
Raja, Vicente, Edward Baggs, Anthony Chemero, & Michael L. Anderson. (2023). Trick or treat. Physics of Life Reviews. 44. 153–159.2 indexed citations
Penner‐Wilger, Marcie & Michael L. Anderson. (2011). The Relation between Finger Gnosis and Mathematical Ability: Can we Attribute Function to Cortical Structure with Cross-Domain Modeling?. Cognitive Science. 33(33).8 indexed citations
5.
Havasi, Catherine, et al.. (2011). Comparing Matrix Decomposition Methods for Meta-Analysis and Reconstruction of Cognitive Neuroscience Results. The Florida AI Research Society.6 indexed citations
6.
Anderson, Michael L. & Luiz Pessoa. (2011). Quantifying the diversity of neural activations in individual brain regions. Cognitive Science. 33(33).10 indexed citations
7.
Dinalankara, Wikum, Shomir Wilson, Donald Perlis, et al.. (2010). The Metacognitive Loop: An Architecture for Building Robust Intelligent Systems. National Conference on Artificial Intelligence. 33–39.6 indexed citations
8.
Anderson, Michael L. & Tim Oates. (2010). A critique of multi-voxel pattern analysis. eScholarship (California Digital Library). 32(32).26 indexed citations
9.
Oates, Tim, et al.. (2008). The role of metacognition in robust AI systems. National Conference on Artificial Intelligence.5 indexed citations
Anderson, Michael L. & Marcie Penner‐Wilger. (2007). Do Redeployed Finger Representations Underlie Math Ability. eScholarship (California Digital Library). 29(29).6 indexed citations
13.
Anderson, Michael L., et al.. (2007). Toward Domain-Neutral Human-Level Metacognition.. National Conference on Artificial Intelligence. 1–6.4 indexed citations
Anderson, Michael L.. (2006). Strike while the iron is. Artificial Intelligence. 170(18). 1213–1217.1 indexed citations
16.
Anderson, Michael L., et al.. (2004). A brief introduction to the guidance theory of representation. CogPrints (University of Southampton). 26(26).7 indexed citations
17.
Josyula, Darsana, et al.. (2003). Towards domain-independent, task-oriented, conversational adequacy. International Joint Conference on Artificial Intelligence. 1637–1638.4 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.