Benoı̂t Lemaire

1.3k total citations
50 papers, 674 citations indexed

About

Benoı̂t Lemaire is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Experimental and Cognitive Psychology. According to data from OpenAlex, Benoı̂t Lemaire has authored 50 papers receiving a total of 674 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 20 papers in Cognitive Neuroscience and 17 papers in Experimental and Cognitive Psychology. Recurrent topics in Benoı̂t Lemaire's work include Neural and Behavioral Psychology Studies (14 papers), Natural Language Processing Techniques (12 papers) and Cognitive Functions and Memory (10 papers). Benoı̂t Lemaire is often cited by papers focused on Neural and Behavioral Psychology Studies (14 papers), Natural Language Processing Techniques (12 papers) and Cognitive Functions and Memory (10 papers). Benoı̂t Lemaire collaborates with scholars based in France, Switzerland and United States. Benoı̂t Lemaire's co-authors include Sophie Portrat, Guy Denhière, Philippe Dessus, Gaën Plancher, Anne Guérin-Dugué, Mirta B. Gordon, Annika Wærn, Thierry Baccino, Jussi Karlgren and Nils Dahlbäck and has published in prestigious journals such as Annals of the New York Academy of Sciences, Journal of Experimental Psychology General and Journal of Experimental Psychology Learning Memory and Cognition.

In The Last Decade

Benoı̂t Lemaire

42 papers receiving 615 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Benoı̂t Lemaire France 16 285 282 151 129 79 50 674
Ion Juvina United States 14 148 0.5× 253 0.9× 125 0.8× 92 0.7× 92 1.2× 49 644
Emily Prud’hommeaux United States 14 447 1.6× 195 0.7× 50 0.3× 139 1.1× 166 2.1× 55 779
Mathieu Koppen Netherlands 12 318 1.1× 166 0.6× 74 0.5× 109 0.8× 45 0.6× 27 627
Maya Sappelli Netherlands 9 147 0.5× 226 0.8× 135 0.9× 178 1.4× 80 1.0× 32 567
M. Martin Taylor France 12 171 0.6× 194 0.7× 116 0.8× 303 2.3× 43 0.5× 20 715
Craig A. Kaplan United States 5 143 0.5× 129 0.5× 232 1.5× 88 0.7× 46 0.6× 6 498
Peter Organisciak United States 13 145 0.5× 97 0.3× 311 2.1× 99 0.8× 112 1.4× 59 546
David Reitter United States 18 679 2.4× 305 1.1× 220 1.5× 246 1.9× 43 0.5× 78 1.2k
Alison Pease United Kingdom 14 379 1.3× 133 0.5× 209 1.4× 60 0.5× 28 0.4× 65 675

Countries citing papers authored by Benoı̂t Lemaire

Since Specialization
Citations

This map shows the geographic impact of Benoı̂t Lemaire'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 Benoı̂t Lemaire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benoı̂t Lemaire more than expected).

Fields of papers citing papers by Benoı̂t Lemaire

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Benoı̂t Lemaire. 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 Benoı̂t Lemaire. The network helps show where Benoı̂t Lemaire may publish in the future.

Co-authorship network of co-authors of Benoı̂t Lemaire

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

All Works

20 of 20 papers shown
1.
Lemaire, Benoı̂t, et al.. (2024). Does the extension of free time trigger spontaneous elaborative strategies in working memory?. Memory & Cognition. 52(8). 2022–2052.
2.
Lemaire, Benoı̂t, et al.. (2023). Similarity-Based Compression in Working Memory: Implications for Decay and Refreshing Models. Computational Brain & Behavior. 7(1). 163–180.
3.
Lemaire, Benoı̂t, et al.. (2023). A Hebbian Model to Account for Musical Expertise Differences in a Working Memory Task. Cognitive Computation. 15(5). 1620–1639.
4.
Lemaire, Benoı̂t, et al.. (2023). Learning basic arithmetic: A comparison between rote and procedural learning based on an artificial sequence.. Journal of Experimental Psychology Learning Memory and Cognition. 50(3). 418–434. 2 indexed citations
5.
Lemaire, Benoı̂t, et al.. (2022). Between-item similarity frees up working memory resources through compression: A domain-general property.. Journal of Experimental Psychology General. 151(11). 2641–2665. 10 indexed citations
6.
Lemaire, Benoı̂t, et al.. (2021). Can activated long-term memory maintain serial order information?. Psychonomic Bulletin & Review. 28(4). 1301–1312. 6 indexed citations
7.
Lemaire, Benoı̂t & Sophie Portrat. (2018). A Computational Model of Working Memory Integrating Time-Based Decay and Interference. Frontiers in Psychology. 9. 416–416. 22 indexed citations
8.
Frey, Aline, Benoı̂t Lemaire, Laurent Vercueil, & Anne Guérin-Dugué. (2018). An Eye Fixation-Related Potential Study in Two Reading Tasks: Reading to Memorize and Reading to Make a Decision. Brain Topography. 31(4). 640–660. 12 indexed citations
9.
Lemaire, Benoı̂t, et al.. (2017). What is the time course of working memory attentional refreshing?. Psychonomic Bulletin & Review. 25(1). 370–385. 40 indexed citations
10.
Lemaire, Benoı̂t, et al.. (2016). Reconciling Two Computational Models of Working Memory in Aging. Topics in Cognitive Science. 8(1). 264–278. 16 indexed citations
11.
Portrat, Sophie, et al.. (2015). Promoting the experimental dialogue between working memory and chunking: Behavioral data and simulation. Memory & Cognition. 44(3). 420–434. 28 indexed citations
12.
Reichle, Erik D., et al.. (2015). An analysis of reading skill development using E-Z Reader. Journal of Cognitive Psychology. 27(5). 657–676. 33 indexed citations
13.
Frey, Aline, et al.. (2013). Decision-making in information seeking on texts: an eye-fixation-related potentials investigation. Frontiers in Systems Neuroscience. 7. 39–39. 32 indexed citations
14.
Guérin-Dugué, Anne, et al.. (2012). Towards a Model of Information Seeking by Integrating Visual, Semantic and Memory Maps. HAL (Le Centre pour la Communication Scientifique Directe).
15.
Lemaire, Benoı̂t, et al.. (2011). MDLChunker: A MDL‐Based Cognitive Model of Inductive Learning. Cognitive Science. 35(7). 1352–1389. 32 indexed citations
16.
Lemaire, Benoı̂t, et al.. (2009). MDLChunker: a MDL-based Model of Word Segmentation. eScholarship (California Digital Library). 31(31). 4 indexed citations
17.
Lemaire, Benoı̂t, et al.. (2008). Testing the cognitive relevance of a geometric model on a word association task: A comparison of humans, ACOM, and LSA. Behavior Research Methods. 40(4). 926–934. 5 indexed citations
18.
Bisson, Gilles, et al.. (2007). Inducing High-Level Behaviors from Problem-Solving Traces Using Machine-Learning Tools. IEEE Intelligent Systems. 22(4). 22–30. 7 indexed citations
19.
Lemaire, Benoı̂t, et al.. (2006). A computational model for simulating text comprehension. Behavior Research Methods. 38(4). 628–637. 23 indexed citations
20.
Lemaire, Benoı̂t & Guy Denhière. (2004). Incremental Construction of an Associative Network from a Corpus. CogPrints (University of Southampton). 26(26). 29 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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