John E. Hummel
About
In The Last Decade
John E. Hummel
42 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 104
- Developmental and Educational Psychology 942
- Cognitive Neuroscience 712
- Artificial Intelligence 708
- Experimental and Cognitive Psychology 441
- Social Psychology 288
Countries citing papers authored by John E. Hummel
This map shows the geographic impact of John E. Hummel'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. Hummel 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. Hummel more than expected).
Fields of papers citing papers by John E. Hummel
This network shows the impact of papers produced by John E. Hummel. 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. Hummel. The network helps show where John E. Hummel may publish in the future.
Co-authorship network of co-authors of John E. Hummel
This figure shows the co-authorship network connecting the top 25 collaborators of John E. Hummel. A scholar is included among the top collaborators of John E. Hummel 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. Hummel. John E. Hummel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 13 | |
| 3 | 24 | |
| 4 | Progressive Alignment Facilitates Learning of Deterministic But Not Probabilistic Relational Categories. | 8 |
| 5 | Explanatory Reasoning for Inductive Confidence | 3 |
| 6 | Probabilistic relational categories are learnable as long as you don’t know you’re learning probabilistic relational categories | 2 |
| 7 | Toward a process model of explanation with implications for the type-token problem | 1 |
| 8 | 10 | |
| 9 | Using Ideal Observers in Higher-order Human Category Learning | 7 |
| 10 | A Theory of Reflexive Relational Generalization | 1 |
| 11 | Relating Category Coherence and Analogy: Simulating Category Use with a Model of Relational Reasoning | 1 |
| 12 | Ideals Aren't Always Typical: Dissociating Goodness-of-Exemplar From Typicality Judgments | 6 |
| 13 | Modeling Human Mental Representations: What Works, What doesn't and Why? | 8 |
| 14 | 113 | |
| 15 | Feature- vs. Relation-Defined Categories: Probab(alistic)ly Not the Same | 9 |
| 16 | Compositional Connectionism in Cognitive Science: Papers from the AAAI Fall Symposium | 6 |
| 17 | A Fundamental Limitation of Symbol-Argument-Argument Notation As a Model of Human Relational Representations | 2 |
| 18 | Structure Mapping and the Predication of Novel Higher-Order Relations | 1 |
| 19 | 13 | |
| 20 | 19 |
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.