Sarah Laszlo

2.1k total citations
34 papers, 1.4k citations indexed

About

Sarah Laszlo is a scholar working on Cognitive Neuroscience, Developmental and Educational Psychology and Artificial Intelligence. According to data from OpenAlex, Sarah Laszlo has authored 34 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Cognitive Neuroscience, 15 papers in Developmental and Educational Psychology and 5 papers in Artificial Intelligence. Recurrent topics in Sarah Laszlo's work include EEG and Brain-Computer Interfaces (16 papers), Reading and Literacy Development (15 papers) and Neurobiology of Language and Bilingualism (15 papers). Sarah Laszlo is often cited by papers focused on EEG and Brain-Computer Interfaces (16 papers), Reading and Literacy Development (15 papers) and Neurobiology of Language and Bilingualism (15 papers). Sarah Laszlo collaborates with scholars based in United States, Spain and Romania. Sarah Laszlo's co-authors include Kara D. Federmeier, Zhanpeng Jin, Maria V. Ruiz-Blondet, Blair C. Armstrong, David C. Plaut, Qiong Gui, Kenneth J. Kurtz, Mallory Stites, Matthew B. Pontifex and Vladimir Miskovic and has published in prestigious journals such as Brain Research, Psychological Science and Neuropsychologia.

In The Last Decade

Sarah Laszlo

33 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sarah Laszlo United States 18 1.1k 522 227 169 128 34 1.4k
Santosh Mathan United States 17 588 0.5× 144 0.3× 95 0.4× 31 0.2× 189 1.5× 59 1.0k
Joseph Doyle United Kingdom 13 880 0.8× 81 0.2× 127 0.6× 114 0.7× 118 0.9× 31 1.2k
Shanqing Cai United States 16 579 0.5× 147 0.3× 418 1.8× 49 0.3× 115 0.9× 34 888
Demetrios Karis United States 10 1.4k 1.3× 158 0.3× 187 0.8× 28 0.2× 94 0.7× 17 1.8k
Mitchell Valdés-Sosa Cuba 23 1.2k 1.1× 180 0.3× 342 1.5× 17 0.1× 62 0.5× 59 1.6k
Jessica D. Bayliss United States 16 536 0.5× 197 0.4× 40 0.2× 36 0.2× 52 0.4× 33 941
Cathryn Downing United Kingdom 13 1.2k 1.0× 79 0.2× 348 1.5× 99 0.6× 51 0.4× 18 1.6k
Brian Roark United States 31 678 0.6× 233 0.4× 179 0.8× 113 0.7× 2.6k 20.5× 127 3.4k
Jeannine Pinto United States 11 433 0.4× 194 0.4× 90 0.4× 382 2.3× 202 1.6× 22 1.1k
Sascha Tamm Germany 14 609 0.5× 285 0.5× 265 1.2× 76 0.4× 64 0.5× 34 946

Countries citing papers authored by Sarah Laszlo

Since Specialization
Citations

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

Fields of papers citing papers by Sarah Laszlo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarah Laszlo

This figure shows the co-authorship network connecting the top 25 collaborators of Sarah Laszlo. A scholar is included among the top collaborators of Sarah Laszlo 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 Sarah Laszlo. Sarah Laszlo 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.
Prabhakaran, Vinodkumar, Emily Denton, Sarah Laszlo, et al.. (2024). ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation. 12333–12347. 5 indexed citations
2.
Kurtz, Kenneth J., et al.. (2020). Similarity judgments predict N400 amplitude differences between taxonomic category members and thematic associates. Neuropsychologia. 141. 107388–107388. 7 indexed citations
3.
Laszlo, Sarah, et al.. (2018). An Event-Related Potential study of letter spacing during visual word recognition. Brain Research. 1684. 9–20. 4 indexed citations
4.
Laszlo, Sarah, et al.. (2016). An event-related potential study of the relationship between N170 lateralization and phonological awareness in developing readers. Neuropsychologia. 91. 415–425. 31 indexed citations
5.
Laszlo, Sarah, et al.. (2015). Individual differences in involvement of the visual object recognition system during visual word recognition. Brain and Language. 145-146. 42–52. 11 indexed citations
6.
Ruiz-Blondet, Maria V., et al.. (2014). Brainprint: Identifying Unique Features of Neural Activity with Machine Learning. Cognitive Science. 36(36). 11 indexed citations
7.
8.
Laszlo, Sarah, et al.. (2014). A direct comparison of active and passive amplification electrodes in the same amplifier system. Journal of Neuroscience Methods. 235. 298–307. 60 indexed citations
9.
Laszlo, Sarah & Kara D. Federmeier. (2014). Never seem to find the time: evaluating the physiological time course of visual word recognition with regression analysis of single-item event-related potentials. Language Cognition and Neuroscience. 29(5). 642–661. 67 indexed citations
10.
Laszlo, Sarah & Blair C. Armstrong. (2013). Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended ERP reading data. Cognitive Science. 35(35). 3 indexed citations
11.
Laszlo, Sarah & David C. Plaut. (2011). Simulating Event-Related Potential Reading Data in a Neurally Plausible Parallel Distributed Processing Model.. Cognitive Science. 33(33). 3 indexed citations
12.
Laszlo, Sarah & David C. Plaut. (2011). Simulating Event-Related Potential Reading Data in a Neurally Plausible Parallel Distributed Processing Model - eScholarship. Proceedings of the Annual Meeting of the Cognitive Science Society. 33(33). 1 indexed citations
13.
Laszlo, Sarah, Mallory Stites, & Kara D. Federmeier. (2011). Won't get fooled again: An event-related potential study of task and repetition effects on the semantic processing of items without semantics. Language and Cognitive Processes. 27(2). 257–274. 38 indexed citations
14.
Laszlo, Sarah & David C. Plaut. (2011). A neurally plausible Parallel Distributed Processing model of Event-Related Potential word reading data. Brain and Language. 120(3). 271–281. 63 indexed citations
15.
Laszlo, Sarah, Blair C. Armstrong, W. Joseph MacInnes, David C. Plaut, & Kara D. Federmeier. (2010). When dog is more wolf than bone: Computational and electrophysiological evidence for featural organization of semantic memory. eScholarship (California Digital Library). 32(32).
16.
Laszlo, Sarah & Kara D. Federmeier. (2010). The N400 as a snapshot of interactive processing: Evidence from regression analyses of orthographic neighbor and lexical associate effects. Psychophysiology. 48(2). 176–186. 154 indexed citations
17.
Laszlo, Sarah & Kara D. Federmeier. (2009). A beautiful day in the neighborhood: An event-related potential study of lexical relationships and prediction in context. Journal of Memory and Language. 61(3). 326–338. 186 indexed citations
18.
Laszlo, Sarah & Kara D. Federmeier. (2008). Minding the PS, queues, and PXQs: Uniformity of semantic processing across multiple stimulus types. Psychophysiology. 45(3). 458–466. 34 indexed citations
19.
Laszlo, Sarah & Kara D. Federmeier. (2007). The acronym superiority effect. Psychonomic Bulletin & Review. 14(6). 1158–1163. 29 indexed citations
20.
Szilágyi, László, et al.. (2005). Quick QRS Complex Detection for On-Line ECG and Holter Systems. PubMed. 39. 3906–3908. 3 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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