Psycholinguistic studies have shown that there are many variables implicated in language comprehension and production. At the lexical level, subjective age of acquisition (AoA), the estimate of the age at which a word is acquired, is key for stimuli selection in psycholinguistic studies. AoA databases in English are often used when testing a variety of phenomena in second language (L2) speakers of English. However, these have limitations, as the norms are not provided by the target population (L2 speakers of English) but by native English speakers. In this study, we asked native Spanish L2 speakers of English to provide subjective AoA ratings for 1604 English words, and investigated whether factors related to 14 lexico-semantic and afective variables, both in Spanish and English, and to the speakers’ profle (i.e., sociolinguistic variables and L2 profciency), were related to the L2 AoA ratings. We used boosted regression trees, an advanced form of regression analysis based on machine learning and boosting algorithms, to analyse the data. Our results showed that the model accounted for a relevant proportion of deviance (58.56%), with the English AoA provided by native English speakers being the strongest predictor for L2 AoA. Additionally, L2 AoA correlated with L2 reaction times. Our database is a useful tool for the research community running psycholinguistic studies in L2 speakers of English. It adds knowledge about which factors—linked to the characteristics of both the linguistic stimuli and the speakers—afect L2 subjective AoA. The database and the data can be downloaded from: https://osf.io/gr8xd/?view_only=73b01dccbedb4d7897c8d104d3d68c46
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was supported by the “Proyectos de I+D de Jóvenes Doctores” funded by the Autonomous University of Madrid and the Community of Madrid (Title: "The acquisition and development of afective vocabulary in a second language from childhood to adolescence”; reference SI3/PJI/2021-00249) awarded to Sara Rodriguez-Cuadrado; by the Ministerio de Ciencia e Innovación under Grants PGC2018-098558-B-I00 to José A. Hinojosa, and PID2019-107206GB-I00 and RED2018-102615-T to Pilar Ferré, PID2019-108092GA-I00/AEI/10.13039/501100011033 to Carlos Romero-Rivas, and PID2019-106868GB-I00 to Paz Suárez-Coalla; by Comunidad de Madrid under Grant H2019/HUM-5705 to José A. Hinojosa, and Universitat Rovira i Virgili under Grant 2019PFR-URVB2-32 to Pilar Ferré. Lucía Sabater was hired by the aforementioned grant PGC2018-098558-B-I00 awarded to José A. Hinojosa