The Multilingual Picture Database
Duñabeitia, Jon Andoni; Baciero, Ana; Antoniou, Kyriakos; Antoniou, Mark; Ataman, Esra; Baus, Cristina; Ben-Shachar, Michal; Çağlar, Ozan Can; Chromý, Jan; Comesaña, Montserrat; Filip, Maroš; Đurđević, Dušica Filipović; Dowens, Margaret Gillon; Hatzidaki, Anna; Januška, Jiří; Jusoh, Zuraini; Kanj, Rama; Kim, Say Young; Kırkıcı, Bilal; Leminen, Alina; Lohndal, Terje; Yap, Ngee Thai; Renvall, Hanna; Rothman, Jason; Royle, Phaedra; Santesteban, Mikel; Sevilla, Yamila; Slioussar, Natalia; Vaughan-Evans, Awel; Wodniecka, Zofia; Wulff, Stefanie; Pliatsikas, Christos (2022)
Nature publishing group
2022
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022100561216
https://urn.fi/URN:NBN:fi-fe2022100561216
Tiivistelmä
The growing interdisciplinary research field of psycholinguistics is in constant need of new
and up-to-date tools which will allow researchers to answer complex questions, but also
expand on languages other than English, which dominates the field. One type of such tools
are picture datasets which provide naming norms for everyday objects. However, existing
databases tend to be small in terms of the number of items they include, and have also
been normed in a limited number of languages, despite the recent boom in multilingualism
research. In this paper we present the Multilingual Picture (Multipic) database, containing
naming norms and familiarity scores for 500 coloured pictures, in thirty-two languages or
language varieties from around the world. The data was validated with standard methods
that have been used for existing picture datasets. This is the first dataset to provide naming
norms, and translation equivalents, for such a variety of languages; as such, it will be of
particular value to psycholinguists and other interested researchers. The dataset has been
made freely available.
and up-to-date tools which will allow researchers to answer complex questions, but also
expand on languages other than English, which dominates the field. One type of such tools
are picture datasets which provide naming norms for everyday objects. However, existing
databases tend to be small in terms of the number of items they include, and have also
been normed in a limited number of languages, despite the recent boom in multilingualism
research. In this paper we present the Multilingual Picture (Multipic) database, containing
naming norms and familiarity scores for 500 coloured pictures, in thirty-two languages or
language varieties from around the world. The data was validated with standard methods
that have been used for existing picture datasets. This is the first dataset to provide naming
norms, and translation equivalents, for such a variety of languages; as such, it will be of
particular value to psycholinguists and other interested researchers. The dataset has been
made freely available.