Danielle Navarro
About
In The Last Decade
Danielle Navarro
124 papers receiving 2.6k citations
Peers
Comparison fields: 5 of 154
- Artificial Intelligence 1.1k
- Cognitive Neuroscience 780
- Developmental and Educational Psychology 698
- Experimental and Cognitive Psychology 472
- General Decision Sciences 362
Countries citing papers authored by Danielle Navarro
This map shows the geographic impact of Danielle Navarro'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 Danielle Navarro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle Navarro more than expected).
Fields of papers citing papers by Danielle Navarro
This network shows the impact of papers produced by Danielle Navarro. 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 Danielle Navarro. The network helps show where Danielle Navarro may publish in the future.
Co-authorship network of co-authors of Danielle Navarro
This figure shows the co-authorship network connecting the top 25 collaborators of Danielle Navarro. A scholar is included among the top collaborators of Danielle Navarro 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 Danielle Navarro. Danielle Navarro is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 37 | |
| 3 | 9 | |
| 4 | 4 | |
| 5 | 4 | |
| 6 | When extremists win: On the behavior of iterated learning chains when priors are heterogeneous. | 3 |
| 7 | Sampling frames, Bayesian inference and inductive reasoning. | 3 |
| 8 | 1 | |
| 9 | 15 | |
| 10 | Quantifying the time course of similarity. | 2 |
| 11 | 35 | |
| 12 | The relevance of labels in semi-supervised learning depends on category structure. | 1 |
| 13 | Graded structure in adjective categories | 4 |
| 14 | Does anchoring cause overconfidence only in experts | 5 |
| 15 | Why are some word orders more common than others? A uniform information density account | 24 |
| 16 | Learning the context of a category | 5 |
| 17 | Learning Time-Varying Categories | 3 |
| 18 | Using sequential structure to improve visuomotor control | 2 |
| 19 | Joint acquisition of word order and word reference | 19 |
| 20 | Modeling Individual Differences with Dirichlet Processes | 2 |
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.