@misc{YamadaXueLietal., author = {Yamada, Yuki and Xue, Jin and Li, Panpan and Ruiz-Fern{\´a}ndez, Susana and {\"O}zdoğru, Asil Ali and Sar{\i}, Şahsenem and Torres, Sergio C. and Hinojosa, Jos{\´e} A. and Montoro, Pedro R. and AlShebli, Bedoor and Bolatov, Aidos K. and McGeechan, Grant J. and Zloteanu, Mircea and Razpurker-Apfeld, Irene and Samekin, Adil and Tal-Or, Nurit and Tejada, Julian and Freitag, Raquel and Khatin-Zadeh, Omid and Banaruee, Hassan and Robin, Nicolas and Brise{\~n}o-Sanchez, Guillermo and Barrera-Causil, Carlos J. and Marmolejo-Ramos, Fernando}, title = {Where the 'bad' and the 'good' go : a multi-lab direct replication report of Casasanto (2009, Experiment 1)}, series = {Memory \& Cognition}, volume = {2024}, journal = {Memory \& Cognition}, publisher = {Springer Science and Business Media LLC}, issn = {0090-502X}, doi = {10.3758/s13421-024-01637-1}, pages = {1 -- 7}, abstract = {Casasanto (Journal of Experimental Psychology: General, 138, 351-367, 2009) conceptualised the body-specificity hypothesis by empirically finding that right-handed people tend to associate a positive valence with the right side and a negative valence with the left side, whilst left-handed people tend to associate a positive valence with the left side and negative valence with the right side. Thus, this was the first paper that showed a body-specific space-valence mapping. These highly influential findings led to a substantial body of research and follow-up studies, which could confirm the original findings on a conceptual level. However, direct replications of the original study are scarce. Against this backdrop and given the replication crisis in psychology, we conducted a direct replication of Casasanto's original study with 2,222 participants from 12 countries to examine the aforementioned effects in general and also in a cross-cultural comparison. Our results support Casasanto's findings that right-handed people associate the right side with positivity and the left side with negativity and vice versa for left-handers.}, language = {en} } @misc{KhatinZadehBanarueeRealietal., author = {Khatin-Zadeh, Omid and Banaruee, Hassan and Reali, Florencia and Tirado, Carlos and Ruiz-Fern{\´a}ndez, Susana and Yamada, Yuki and Wang, Ruiming and Nicolas, Robin and Khwaileh, Tariq and Szychowska, Malina and Vestlund, Johanna and Correa, Juan C. and Farsani, Danyal and Butcher, Natalie and Som, Bidisha and Volkonskii, Ivan and Plevoets, Koen and Marmolejo-Ramos, Fernando}, title = {Metaphors of time across cultures}, series = {Journal of Cultural Cognitive Science}, volume = {7}, journal = {Journal of Cultural Cognitive Science}, publisher = {Springer Science and Business Media LLC}, issn = {2520-100X}, doi = {10.1007/s41809-023-00125-3}, pages = {219 -- 231}, language = {en} } @misc{HeymanProniziusLewisetal., author = {Heyman, Tom and Pronizius, Ekaterina and Lewis, Savannah C. and Acar, Oguz A. and Adamkovič, Mat{\´u}š and Ambrosini, Ettore and Antfolk, Jan and Barzykowski, Krystian and Baskin, Ernest and Batres, Carlota and Boucher, Leanne and Boudesseul, Jordane and Brandst{\"a}tter, Eduard and Collins, W. Matthew and Filipović Ðurđević, Dušica and Egan, Ciara and Era, Vanessa and Ferreira, Paulo and Fini, Chiara and Garrido-V{\´a}squez, Patricia and Godbersen, Hendrik and Gomez, Pablo and Graton, Aurelien and Gurkan, Necdet and He, Zhiran and Johnson, Dave C. and Kačm{\´a}r, Pavol and Koch, Chris and Kowal, Marta and Kratochvil, Tomas and Marelli, Marco and Marmolejo-Ramos, Fernando and Mart{\´i}nez, Mart{\´i}n and Mattiassi, Alan and Maxwell, Nicholas P. and Montefinese, Maria and Morvinski, Coby and Neta, Maital and Nielsen, Yngwie A. and Ocklenburg, Sebastian and Onič, Jaš and Papadatou-Pastou, Marietta and Parker, Adam J. and Paruzel-Czachura, Mariola and Pavlov, Yuri G. and Perea, Manuel and Pfuhl, Gerit and Roembke, Tanja C. and R{\"o}er, Jan P. and Roettger, Timo B and Ruiz-Fernandez, Susana and Schmidt, Kathleen}, title = {Crowdsourcing multiverse analyses to explore the impact of different data-processing and analysis decisions : a tutorial}, series = {Psychological methods}, journal = {Psychological methods}, publisher = {American Psychological Association (APA)}, address = {Washington, DC}, issn = {1939-1463}, doi = {10.1037/met0000770}, abstract = {When processing and analyzing empirical data, researchers regularly face choices that may appear arbitrary (e.g., how to define and handle outliers). If one chooses to exclusively focus on a particular option and conduct a single analysis, its outcome might be of limited utility. That is, one remains agnostic regarding the generalizability of the results, because plausible alternative paths remain unexplored. A multiverse analysis offers a solution to this issue by exploring the various choices pertaining to data-processing and/or model building, and examining their impact on the conclusion of a study. However, even though multiverse analyses are arguably less susceptible to biases compared to the typical single-pathway approach, it is still possible to selectively add or omit pathways. To address this issue, we outline a novel, more principled approach to conducting multiverse analyses through crowdsourcing. The approach is detailed in a step-by-step tutorial to facilitate its implementation. We also provide a worked-out illustration featuring the Semantic Priming Across Many Languages project, thereby demonstrating its feasibility and its ability to increase objectivity and transparency. (PsycInfo Database Record (c) 2026 APA, all rights reserved).}, language = {en} }