Micha Heilbron
- Cognitive Neuroscience top 2%
- Experimental and Cognitive Psychology top 5%
- Social Psychology top 10%
- Developmental and Educational Psychology top 10%
- Artificial Intelligence
- Co-authors
- Floris P. de LangePeter KokMaria ChaitPeter HagoortJan‐Mathijs SchoffelenKristijan ArmeniFlorent MeynielDavid Richter
- Topics
- Neural dynamics and brain function (6 papers)Visual perception and processing mechanisms (6 papers)Neuroscience and Music Perception (3 papers)
- Journals
- Proceedings of the National Academy of SciencesNature CommunicationsTrends in Cognitive Sciences
- Partner nations
- NetherlandsFranceUnited Kingdom
In The Last Decade
Micha Heilbron
11 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Cognitive Neuroscience 852
- Experimental and Cognitive Psychology 221
- Social Psychology 132
- Developmental and Educational Psychology 86
- Artificial Intelligence 78
Countries citing papers authored by Micha Heilbron
This map shows the geographic impact of Micha Heilbron'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 Micha Heilbron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Micha Heilbron more than expected).
Fields of papers citing papers by Micha Heilbron
This network shows the impact of papers produced by Micha Heilbron. 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 Micha Heilbron. The network helps show where Micha Heilbron may publish in the future.
Co-authorship network of co-authors of Micha Heilbron
This figure shows the co-authorship network connecting the top 25 collaborators of Micha Heilbron. A scholar is included among the top collaborators of Micha Heilbron 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 Micha Heilbron. Micha Heilbron is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 5 | |
| 3 | 14 | |
| 4 | 10 | |
| 5 | A hierarchy of linguistic predictions during natural language comprehensionbreakdown → | 153 |
| 6 | 30 | |
| 7 | 27 | |
| 8 | How Do Expectations Shape Perception?breakdown → | 522 |
| 9 | 6 | |
| 10 | 230 | |
| 11 | 25 |
About Micha Heilbron
Micha Heilbron is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Artificial Intelligence, having authored 11 papers that have together received 1.0k indexed citations. Recurring topics across this work include Neural dynamics and brain function (6 papers), Visual perception and processing mechanisms (6 papers) and Neuroscience and Music Perception (3 papers). The work is most often cited by research in Cognitive Neuroscience (852 citations), Experimental and Cognitive Psychology (221 citations) and Sensory Systems (38 citations). Micha Heilbron has collaborated with scholars based in Netherlands, France and United Kingdom. Frequent co-authors include Floris P. de Lange, Peter Kok, Maria Chait, Peter Hagoort, Jan‐Mathijs Schoffelen, Kristijan Armeni, Florent Meyniel, David Richter, Stefan Van der Stigchel and Surya Gayet. Their work appears in journals such as Proceedings of the National Academy of Sciences, Nature Communications and Trends in Cognitive Sciences.
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.