Bradley C. Love

9.5k total citations · 1 hit paper
150 papers, 4.8k citations indexed

About

Bradley C. Love is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Developmental and Educational Psychology. According to data from OpenAlex, Bradley C. Love has authored 150 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 70 papers in Cognitive Neuroscience, 56 papers in Artificial Intelligence and 56 papers in Developmental and Educational Psychology. Recurrent topics in Bradley C. Love's work include Child and Animal Learning Development (53 papers), Decision-Making and Behavioral Economics (24 papers) and Neural dynamics and brain function (22 papers). Bradley C. Love is often cited by papers focused on Child and Animal Learning Development (53 papers), Decision-Making and Behavioral Economics (24 papers) and Neural dynamics and brain function (22 papers). Bradley C. Love collaborates with scholars based in United States, United Kingdom and Germany. Bradley C. Love's co-authors include Todd M. Gureckis, Alison R. Preston, Douglas L. Medin, Matt Jones, W. Todd Maddox, Michael L. Mack, Tyler Davis, Brian D. Glass, Yasuaki Sakamoto and Arthur B. Markman and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neuroscience.

In The Last Decade

Bradley C. Love

147 papers receiving 4.6k citations

Hit Papers

SUSTAIN: A Network Model ... 2004 2026 2011 2018 2004 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bradley C. Love United States 36 2.3k 1.8k 1.3k 955 521 150 4.8k
Thomas J. Palmeri United States 42 4.5k 1.9× 1.8k 1.0× 989 0.8× 1.9k 2.0× 652 1.3× 128 6.5k
Charles Kemp United States 24 1.1k 0.5× 1.2k 0.7× 1.9k 1.5× 611 0.6× 413 0.8× 90 4.3k
Arthur S. Reber United States 34 3.1k 1.4× 3.3k 1.8× 1.0k 0.8× 1.1k 1.2× 1.4k 2.7× 93 6.8k
W. Todd Maddox United States 48 4.3k 1.9× 3.3k 1.8× 939 0.7× 1.9k 2.0× 1.2k 2.2× 184 7.7k
Thomas T. Hills United Kingdom 32 1.2k 0.5× 836 0.5× 595 0.5× 651 0.7× 378 0.7× 102 3.6k
Edward Vul United States 29 3.7k 1.6× 1.5k 0.8× 1.0k 0.8× 1.4k 1.5× 760 1.5× 74 6.4k
Noah D. Goodman United States 46 1.8k 0.8× 2.6k 1.5× 3.4k 2.7× 1.1k 1.2× 892 1.7× 203 8.2k
Christian Lebière United States 24 1.8k 0.8× 745 0.4× 2.3k 1.8× 660 0.7× 934 1.8× 152 5.5k
Zoltán Dienes United Kingdom 46 6.0k 2.6× 2.8k 1.6× 1.0k 0.8× 2.0k 2.1× 1.9k 3.7× 225 10.3k
Niels Taatgen Netherlands 36 2.6k 1.1× 1.1k 0.6× 1.5k 1.2× 1.3k 1.3× 1.2k 2.4× 170 5.8k

Countries citing papers authored by Bradley C. Love

Since Specialization
Citations

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

Fields of papers citing papers by Bradley C. Love

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Bradley C. Love. 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 Bradley C. Love. The network helps show where Bradley C. Love may publish in the future.

Co-authorship network of co-authors of Bradley C. Love

This figure shows the co-authorship network connecting the top 25 collaborators of Bradley C. Love. A scholar is included among the top collaborators of Bradley C. Love 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 Bradley C. Love. Bradley C. Love 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.
Mok, Robert M., et al.. (2025). Coordinating multiple mental faculties during learning. Scientific Reports. 15(1). 5319–5319. 1 indexed citations
2.
Holt, Lori L., et al.. (2024). Demystifying unsupervised learning: how it helps and hurts. Trends in Cognitive Sciences. 28(11). 974–986. 11 indexed citations
3.
Dickerson, John P., et al.. (2023). Do Invariances in Deep Neural Networks Align with Human Perception?. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9277–9285. 2 indexed citations
4.
Barry, Daniel N. & Bradley C. Love. (2022). A neural network account of memory replay and knowledge consolidation. Cerebral Cortex. 33(1). 83–95. 15 indexed citations
5.
Mok, Robert M. & Bradley C. Love. (2020). Abstract Neural Representations of Category Membership beyond Information Coding Stimulus or Response. Journal of Cognitive Neuroscience. 34(10). 1719–1735. 10 indexed citations
6.
Love, Bradley C.. (2020). Levels of biological plausibility. Philosophical Transactions of the Royal Society B Biological Sciences. 376(1815). 20190632–20190632. 18 indexed citations
7.
Braunlich, Kurt & Bradley C. Love. (2018). Occipitotemporal representations reflect individual differences in conceptual knowledge.. Journal of Experimental Psychology General. 148(7). 1192–1203. 22 indexed citations
8.
Mack, Michael L., Bradley C. Love, & Alison R. Preston. (2016). Dynamic updating of hippocampal object representations reflects new conceptual knowledge. Proceedings of the National Academy of Sciences. 113(46). 13203–13208. 126 indexed citations
9.
Schulz, Eric, et al.. (2015). Active learning as a means to distinguish among prominent decision strategies. Cognitive Science. 1829–1834. 3 indexed citations
10.
Gureckis, Todd M. & Bradley C. Love. (2010). Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior. Cognitive Science. 34(1). 10–50. 23 indexed citations
11.
Gureckis, Todd M. & Bradley C. Love. (2009). Short-term gains, long-term pains: How cues about state aid learning in dynamic environments. Cognition. 113(3). 293–313. 66 indexed citations
12.
Love, Bradley C., et al.. (2008). Predicting Information Needs: Adaptive Display in Dynamic Environments. eScholarship (California Digital Library). 30(30). 2 indexed citations
13.
Maddox, W. Todd, Bradley C. Love, Brian D. Glass, & J. Vincent Filoteo. (2008). When more is less: Feedback effects in perceptual category learning. Cognition. 108(2). 578–589. 72 indexed citations
14.
Tomlinson, Marc & Bradley C. Love. (2007). Relation-Based Categories are Easier to Learn than Feature-Based Categories. eScholarship (California Digital Library). 29(29). 2 indexed citations
15.
Jones, Matt, Bradley C. Love, & W. Todd Maddox. (2006). The Role of Similarity in Generalization. eScholarship (California Digital Library). 28(28). 5 indexed citations
16.
Love, Bradley C., et al.. (2006). Learning Abstract Relations Through Analogy to Concrete Exemplars. eScholarship (California Digital Library). 28(28). 5 indexed citations
17.
Sakamoto, Yasuaki, Toshihiko Matsuka, & Bradley C. Love. (2004). Dimension-Wide vs. Exemplar-Specific Attention in Category Learning and Recognition.. 261–266. 13 indexed citations
18.
Jones, Matt & Bradley C. Love. (2004). Beyond common features: The role of roles in determining similarity. UCL Discovery (University College London). 26(26). 1 indexed citations
19.
Gureckis, Todd M. & Bradley C. Love. (2002). Modeling Unsupervised Learning with SUSTAIN. The Florida AI Research Society. 163–167. 3 indexed citations
20.
Love, Bradley C.. (2000). A Computational Level Theory of Similarity. eScholarship (California Digital Library). 22(22). 13 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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