Alberto Testolin

1.2k total citations
51 papers, 688 citations indexed

About

Alberto Testolin is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Statistics and Probability. According to data from OpenAlex, Alberto Testolin has authored 51 papers receiving a total of 688 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 14 papers in Cognitive Neuroscience and 11 papers in Statistics and Probability. Recurrent topics in Alberto Testolin's work include Cognitive and developmental aspects of mathematical skills (11 papers), Underwater Acoustics Research (8 papers) and Mathematics Education and Teaching Techniques (7 papers). Alberto Testolin is often cited by papers focused on Cognitive and developmental aspects of mathematical skills (11 papers), Underwater Acoustics Research (8 papers) and Mathematics Education and Teaching Techniques (7 papers). Alberto Testolin collaborates with scholars based in Italy, Israel and France. Alberto Testolin's co-authors include Marco Zorzi, Michele De Filippo De Grazia, Roee Diamant, Ivilin Peev Stoianov, Michele Zorzi, Andréa Zanella, James L. McClelland, Will Y. Zou, Alessandro Sperduti and Filippo Campagnaro and has published in prestigious journals such as PLoS ONE, Scientific Reports and Philosophical Transactions of the Royal Society B Biological Sciences.

In The Last Decade

Alberto Testolin

49 papers receiving 675 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alberto Testolin Italy 15 170 157 150 108 101 51 688
Michele De Filippo De Grazia Italy 12 220 1.3× 83 0.5× 10 0.1× 99 0.9× 88 0.9× 19 569
Chen Yi China 10 39 0.2× 59 0.4× 32 0.2× 26 0.2× 40 0.4× 50 478
J. Michael Herrmann United Kingdom 15 735 4.3× 248 1.6× 10 0.1× 119 1.1× 183 1.8× 68 1.2k
Julien Diard France 13 197 1.2× 181 1.2× 36 0.2× 17 0.2× 7 0.1× 45 560
Dazhi Cheng China 14 165 1.0× 82 0.5× 182 1.2× 3 0.0× 19 0.2× 42 770
Ron Chrisley United Kingdom 13 289 1.7× 522 3.3× 8 0.1× 22 0.2× 68 0.7× 31 1.1k
Barry Van Veen United States 14 161 0.9× 25 0.2× 6 0.0× 74 0.7× 70 0.7× 37 530
Yu Meng United States 14 268 1.6× 116 0.7× 11 0.1× 116 1.1× 117 1.2× 42 710
Alan H. Kawamoto United States 12 607 3.6× 406 2.6× 15 0.1× 23 0.2× 40 0.4× 35 1.2k
Kenneth D. Morton United States 18 350 2.1× 191 1.2× 7 0.0× 11 0.1× 82 0.8× 93 1.3k

Countries citing papers authored by Alberto Testolin

Since Specialization
Citations

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

Fields of papers citing papers by Alberto Testolin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alberto Testolin

This figure shows the co-authorship network connecting the top 25 collaborators of Alberto Testolin. A scholar is included among the top collaborators of Alberto Testolin 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 Alberto Testolin. Alberto Testolin 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.
Grazia, Michele De Filippo De, et al.. (2024). Weaker number sense accounts for impaired numerosity perception in dyscalculia: Behavioral and computational evidence. Developmental Science. 27(6). e13538–e13538. 3 indexed citations
2.
Testolin, Alberto. (2024). Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models. Applied Sciences. 14(2). 744–744. 9 indexed citations
3.
Duma, Gian Marco, et al.. (2024). Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting. Sensors. 24(9). 2863–2863. 5 indexed citations
4.
Diamant, Roee, et al.. (2024). Observational study on the non-linear response of dolphins to the presence of vessels. Scientific Reports. 14(1). 6062–6062. 2 indexed citations
5.
Testolin, Alberto, et al.. (2024). Measuring temporal bias in sequential numerosity comparison. Behavior Research Methods. 56(7). 7561–7573. 1 indexed citations
6.
Testolin, Alberto, et al.. (2024). A Comparison of Recurrent and Convolutional Deep Learning Architectures for EEG Seizure Forecasting. Research Padua Archive (University of Padua). 583–590. 2 indexed citations
7.
8.
Testolin, Alberto, et al.. (2023). FORTE: Few Samples for Recognizing Hand Gestures with a Smartphone-attached Radar. Proceedings of the ACM on Human-Computer Interaction. 7(EICS). 1–25. 3 indexed citations
9.
Chen, Ling, et al.. (2023). Learning to solve arithmetic problems with a virtual abacus. arXiv (Cornell University). 4. 1 indexed citations
10.
Testolin, Alberto, et al.. (2023). Learning Constraints From Human Stop-Feedback in Reinforcement Learning. 2328–2330.
11.
Testolin, Alberto, et al.. (2020). Visual sense of number vs. sense of magnitude in humans and machines. Scientific Reports. 10(1). 10045–10045. 34 indexed citations
12.
Gatto, Elia, Alberto Testolin, Angelo Bisazza, Marco Zorzi, & Tyrone Lucon‐Xiccato. (2020). Poor numerical performance of guppies tested in a Skinner box. Scientific Reports. 10(1). 16724–16724. 5 indexed citations
13.
Testolin, Alberto & James L. McClelland. (2020). Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies. Psychonomic Bulletin & Review. 28(1). 158–168. 10 indexed citations
14.
Testolin, Alberto, Ivilin Peev Stoianov, & Marco Zorzi. (2017). Letter perception emerges from unsupervised deep learning and recycling of natural image features. Nature Human Behaviour. 1(9). 657–664. 32 indexed citations
15.
Testolin, Alberto, Michele De Filippo De Grazia, & Marco Zorzi. (2017). The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding. Frontiers in Computational Neuroscience. 11. 13–13. 5 indexed citations
16.
Testolin, Alberto & Marco Zorzi. (2016). Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions. Frontiers in Computational Neuroscience. 10. 73–73. 30 indexed citations
17.
Testolin, Alberto, et al.. (2015). Neural Networks for Sequential Data: a Pre‐training Approach based on Hidden Markov Models. Neurocomputing. 169. 323–333. 9 indexed citations
18.
Testolin, Alberto, et al.. (2014). A HMM-based pre-training approach for sequential data.. Research Padua Archive (University of Padua). 467–472. 1 indexed citations
19.
Testolin, Alberto, Ivilin Peev Stoianov, Michele De Filippo De Grazia, & Marco Zorzi. (2013). Deep Unsupervised Learning on a Desktop PC: A Primer for Cognitive Scientists. Frontiers in Psychology. 4. 251–251. 22 indexed citations
20.
Testolin, Alberto, Alessandro Sperduti, Ivilin Peev Stoianov, & Marco Zorzi. (2012). Assessment of Sequential Boltzmann Machines on a Lexical Processing Task. The European Symposium on Artificial Neural Networks. 275–280. 2 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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