Manuel Suero

441 total citations
29 papers, 321 citations indexed

About

Manuel Suero is a scholar working on Cognitive Neuroscience, Management Science and Operations Research and Statistics, Probability and Uncertainty. According to data from OpenAlex, Manuel Suero has authored 29 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cognitive Neuroscience, 7 papers in Management Science and Operations Research and 7 papers in Statistics, Probability and Uncertainty. Recurrent topics in Manuel Suero's work include Visual perception and processing mechanisms (8 papers), Neural and Behavioral Psychology Studies (7 papers) and Meta-analysis and systematic reviews (5 papers). Manuel Suero is often cited by papers focused on Visual perception and processing mechanisms (8 papers), Neural and Behavioral Psychology Studies (7 papers) and Meta-analysis and systematic reviews (5 papers). Manuel Suero collaborates with scholars based in Spain, United States and United Kingdom. Manuel Suero's co-authors include Juan Botella, Andrew M. Derrington, Javier Revuelta, Carmen Ximénez, Jesús Privado, James F. Juola, Orfelio Gerardo León, Isabel Arend, Julio Sánchez‐Meca and Beatriz Gil-Gómez de Liaño and has published in prestigious journals such as Journal of Experimental Psychology Human Perception & Performance, Vision Research and Frontiers in Psychology.

In The Last Decade

Manuel Suero

28 papers receiving 314 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Manuel Suero Spain 9 172 63 39 36 34 29 321
Udo Boehm Netherlands 10 299 1.7× 121 1.9× 35 0.9× 7 0.2× 18 0.5× 19 530
Ana E. Van Gulick United States 11 212 1.2× 76 1.2× 28 0.7× 62 1.7× 10 0.3× 13 354
Andrei Teodorescu Israel 10 336 2.0× 85 1.3× 51 1.3× 10 0.3× 22 0.6× 12 442
Beth Baribault United States 6 72 0.4× 50 0.8× 23 0.6× 5 0.1× 22 0.6× 7 207
Tongran Liu China 13 198 1.2× 137 2.2× 55 1.4× 39 1.1× 32 0.9× 39 414
Richard B. May Canada 11 97 0.6× 63 1.0× 69 1.8× 4 0.1× 16 0.5× 39 346
Ariel Goldstein Israel 8 293 1.7× 121 1.9× 95 2.4× 12 0.3× 20 0.6× 25 413
Kate Nussenbaum United States 11 165 1.0× 109 1.7× 40 1.0× 7 0.2× 45 1.3× 18 343
Lee-Xieng Yang Taiwan 8 166 1.0× 123 2.0× 27 0.7× 10 0.3× 12 0.4× 12 330
Martin R. Vasilev United Kingdom 10 187 1.1× 121 1.9× 33 0.8× 5 0.1× 13 0.4× 20 335

Countries citing papers authored by Manuel Suero

Since Specialization
Citations

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

Fields of papers citing papers by Manuel Suero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Manuel Suero

This figure shows the co-authorship network connecting the top 25 collaborators of Manuel Suero. A scholar is included among the top collaborators of Manuel Suero 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 Manuel Suero. Manuel Suero 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.
Suero, Manuel, et al.. (2025). Reformulating the meta-analytical random effects model of the standardized mean difference as a mixture model. Behavior Research Methods. 57(2). 74–74.
2.
3.
Sánchez‐Meca, Julio, et al.. (2023). Heterogeneity estimation in meta-analysis of standardized mean differences when the distribution of random effects departs from normal: A Monte Carlo simulation study. BMC Medical Research Methodology. 23(1). 19–19. 12 indexed citations
4.
Suero, Manuel, et al.. (2021). Methods for estimating the sampling variance of the standardized mean difference.. Psychological Methods. 28(4). 895–904. 8 indexed citations
5.
Botella, Juan & Manuel Suero. (2019). Recovering the variance of d' from hit and false alarm statistics. Behavior Research Methods. 52(1). 1–22. 15 indexed citations
6.
Botella, Juan, Jesús Privado, Manuel Suero, Roberto Colom, & James F. Juola. (2019). Group analyses can hide heterogeneity effects when searching for a general model: Evidence based on a conflict monitoring task. Acta Psychologica. 193. 171–179. 5 indexed citations
7.
Botella, Juan, et al.. (2018). Assessing Individual Change Without Knowing the Test Properties: Item Bootstrapping. Frontiers in Psychology. 9. 223–223. 2 indexed citations
8.
Suero, Manuel, Jesús Privado, & Juan Botella. (2017). Methods to Estimate the Variance of Some Indices of the Signal Detection Theory: A Simulation Study.. Redalyc (Universidad Autónoma del Estado de México). 38(1). 149–175. 5 indexed citations
9.
Botella, Juan, et al.. (2017). The Debate on the Ego-Depletion Effect: Evidence from Meta-Analysis with the p-Uniform Method. Frontiers in Psychology. 8. 197–197. 28 indexed citations
10.
Botella, Juan, Huiling Huang, & Manuel Suero. (2014). Meta-analysis of the accuracy of tools used for binary classification when the primary studies employ different references.. Psychological Methods. 20(3). 331–341. 3 indexed citations
11.
Botella, Juan, et al.. (2014). Consequences of sequential sampling for meta-analysis. Behavior Research Methods. 46(4). 1167–1183. 2 indexed citations
12.
Botella, Juan, et al.. (2013). Multinomial tree models for assessing the status of the reference in studies of the accuracy of tools for binary classification. Frontiers in Psychology. 4. 694–694. 2 indexed citations
13.
Delicato, Louise, Ignacio Serrano‐Pedraza, Manuel Suero, & Andrew M. Derrington. (2012). Two-dimensional pattern motion analysis uses local features. Vision Research. 62. 84–92. 1 indexed citations
14.
Botella, Juan, et al.. (2010). Psychometric inferences from a meta-analysis of reliability and internal consistency coefficients.. Psychological Methods. 15(4). 386–397. 48 indexed citations
15.
Botella, Juan, et al.. (2007). Parallel processing of stimulus features during RSVP: Evidence from the second response. Perception & Psychophysics. 69(8). 1315–1323. 3 indexed citations
16.
Botella, Juan, Carmen Ximénez, Javier Revuelta, & Manuel Suero. (2006). Optimization of sample size in controlled experiments: The CLAST rule. Behavior Research Methods. 38(1). 65–76. 23 indexed citations
17.
Botella, Juan, Isabel Arend, & Manuel Suero. (2004). Illusory Conjunctions in the Time Domain and the Resulting Time-Course of the Attentional Blink. The Spanish Journal of Psychology. 7(1). 63–68. 9 indexed citations
18.
Botella, Juan, et al.. (2001). A model of the formation of illusory conjunctions in the time domain.. Journal of Experimental Psychology Human Perception & Performance. 27(6). 1452–1467. 45 indexed citations
19.
León, Orfelio Gerardo & Manuel Suero. (2000). Regression toward the mean associated with extreme groups and the evaluation of improvement. Psicothema. 12(1). 145–149. 5 indexed citations
20.
Derrington, Andrew M. & Manuel Suero. (1991). Motion of complex patterns is computed from the perceived motions of their components. Vision Research. 31(1). 139–149. 54 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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