Marcel Binz
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- General Decision Sciences top 10%
- Decision-Making and Behavioral Economics
Papers in ⓘ
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- Machine Learning and Data Classification 2
- Topic Modeling 2
- Explainable Artificial Intelligence (XAI) 2
- Data Stream Mining Techniques 1
- Artificial Intelligence in Games 1
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- Clinical Reasoning and Diagnostic Skills 2
- Co-authors
- Eric Schulz (6 shared papers)Dirk U. Wulff (2 shared papers)Dominik Endres (2 shared papers)Rui Mata (1 shared paper)Matthew Botvinick (3 shared papers)Samuel J. Gershman (2 shared papers)Ishita Dasgupta (2 shared papers)Jane X. Wang (2 shared papers)
- Journals
- Proceedings of the National Academy of Sciences (2 papers)Behavioral and Brain Sciences (2 papers)Behavior Research Methods (1 paper)Cognitive Science (1 paper)Psychological Review (1 paper)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Marcel Binz
8 papers receiving 317 citations
Hit Papers
Peers
Comparison fields: 5 of 70
- Health Informatics 32
- General Decision Sciences 21
- General Social Sciences 19
- Artificial Intelligence 167
- Experimental and Cognitive Psychology 39
Countries citing papers authored by Marcel Binz
This map shows the geographic impact of Marcel Binz'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 Marcel Binz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcel Binz more than expected).
Fields of papers citing papers by Marcel Binz
This network shows the impact of papers produced by Marcel Binz. 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 Marcel Binz. The network helps show where Marcel Binz may publish in the future.
Co-authors
The 20 scholars most cited alongside Marcel Binz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Using cognitive psychology to understand GPT-3 Hit paper breakdown → | 2023 | 253 |
| 2 | 2024 | 21 | |
| 3 | 2023 | 17 | |
| 4 | 2025 | 14 | |
| 5 | 2022 | 10 | |
| 6 | Where Do Heuristics Come From | 2019 | 4 |
| 7 | 2024 | 2 | |
| 8 | 2022 | 1 |
About Marcel Binz
Marcel Binz is a scholar working on Artificial Intelligence, Family Practice, General Social Sciences, Social Psychology and Management Science and Operations Research, having authored 8 papers that have together received 322 indexed citations. Recurring topics across this work include Clinical Reasoning and Diagnostic Skills (2 papers), Machine Learning and Data Classification (2 papers), Computational and Text Analysis Methods (2 papers), Topic Modeling (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Data Stream Mining Techniques (1 paper), Decision-Making and Behavioral Economics (1 paper) and Artificial Intelligence in Games (1 paper). The work is most often cited by research in Health Informatics (32 citations), General Decision Sciences (21 citations), General Social Sciences (19 citations), Artificial Intelligence (167 citations) and Experimental and Cognitive Psychology (39 citations). Marcel Binz has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Eric Schulz, Dirk U. Wulff, Dominik Endres, Rui Mata, Matthew Botvinick, Samuel J. Gershman, Ishita Dasgupta, Jane X. Wang, Vencislav Popov and Richard M. Shiffrin. Their work appears in journals such as Proceedings of the National Academy of Sciences, Behavioral and Brain Sciences, Behavior Research Methods, Cognitive Science and Psychological Review.
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.