Marcel Binz

789 total citations · 1 hit paper
8 papers, 322 citations indexed

About

Marcel Binz is a scholar working on Artificial Intelligence, Family Practice and General Social Sciences. According to data from OpenAlex, Marcel Binz has authored 8 papers receiving a total of 322 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Family Practice and 2 papers in General Social Sciences. Recurrent topics in Marcel Binz's work include Computational and Text Analysis Methods (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Topic Modeling (2 papers). Marcel Binz is often cited by papers focused on Computational and Text Analysis Methods (2 papers), Explainable Artificial Intelligence (XAI) (2 papers) and Topic Modeling (2 papers). Marcel Binz collaborates with scholars based in Germany, United Kingdom and United States. Marcel Binz's co-authors include Eric Schulz, Dirk U. Wulff, Rui Mata, Dominik Endres, Matthew Botvinick, Ishita Dasgupta, Samuel J. Gershman, Jane X. Wang, Richard M. Shiffrin and Carl T. Bergstrom and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Psychological Review and Behavioral and Brain Sciences.

In The Last Decade

Marcel Binz

8 papers receiving 317 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Using cognitive psychology to understand GPT-3

2023 253 citations Marcel Binz, Eric Schulz Proceedings of the National Academy of Sciences profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marcel Binz Germany	5	167	55	53	39	32	8	322
Yuling Gu United States	4	93 0.6×	25 0.5×	41 0.8×	17 0.4×	18 0.6×	12	215
James Brand United Kingdom	7	89 0.5×	67 1.2×	28 0.5×	162 4.2×	12 0.4×	13	335
Dorottya Demszky United States	8	153 0.9×	19 0.3×	56 1.1×	24 0.6×	29 0.9×	22	333
Erica J. Yoon United States	6	52 0.3×	28 0.5×	33 0.6×	31 0.8×	7 0.2×	10	310
Maurice Jakesch United States	7	178 1.1×	34 0.6×	140 2.6×	11 0.3×	54 1.7×	10	371
Alan Jern United States	8	168 1.0×	67 1.2×	96 1.8×	22 0.6×		19	365
Suzanne Tolmeijer Switzerland	6	130 0.8×	52 0.9×	54 1.0×	12 0.3×	14 0.4×	7	297
Caleb Ziems United States	8	333 2.0×	13 0.2×	100 1.9×	14 0.4×	18 0.6×	14	467
Ulrik Lyngs United Kingdom	9	95 0.6×	29 0.5×	142 2.7×	10 0.3×	16 0.5×	21	306
Kathryn S. McCarthy United States	13	173 1.0×	20 0.4×	28 0.5×	52 1.3×	12 0.4×	46	508

Countries citing papers authored by Marcel Binz

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Marcel Binz

This figure shows the co-authorship network connecting the top 25 collaborators of Marcel Binz. A scholar is included among the top collaborators of Marcel Binz 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 Marcel Binz. Marcel Binz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Binz, Marcel, Adina L. Roskies, Balázs Aczél, et al.. (2025). How should the advancement of large language models affect the practice of science?. Proceedings of the National Academy of Sciences. 122(5). e2401227121–e2401227121. 14 indexed citations

2.

Binz, Marcel, et al.. (2024). Meta-learning: Data, architecture, and both. Behavioral and Brain Sciences. 47. e170–e170. 2 indexed citations

3.

Binz, Marcel, et al.. (2024). A tutorial on open-source large language models for behavioral science. Behavior Research Methods. 56(8). 8214–8237. 21 indexed citations

4.

Binz, Marcel & Eric Schulz. (2023). Using cognitive psychology to understand GPT-3. Proceedings of the National Academy of Sciences. 120(6). e2218523120–e2218523120. 253 indexed citations breakdown →

5.

Binz, Marcel, et al.. (2023). Meta-learned models of cognition. Behavioral and Brain Sciences. 47. e147–e147. 17 indexed citations

6.

Binz, Marcel, Samuel J. Gershman, Eric Schulz, & Dominik Endres. (2022). Heuristics from bounded meta-learned inference.. Psychological Review. 129(5). 1042–1077. 10 indexed citations

7.

Binz, Marcel & Eric Schulz. (2022). Reconstructing the Einstellung Effect. Computational Brain & Behavior. 6(3). 526–542. 1 indexed citations

8.

Binz, Marcel & Dominik Endres. (2019). Where Do Heuristics Come From. Cognitive Science. 1402–1408. 4 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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