Standardizing norms for 180 coloured Snodgrass and Vanderwart pictures in Kannada language

Naming in a multilingual context: Norms for the ICMR-Manipal colour picture corpus in Kannada from the Indian context

There have been many published picture corpora. However, more than half of the world's population speaks more than one language and, as language and culture are intertwined, some of the items from a picture corpus designed for a given language in a particular culture may not fit another culture (with the same or different language). There is also an awareness that language research can gain from the study of bi-/multilingual individuals who are immersed in multilingual contexts that foster inter-language interactions. Consequently, we developed a relatively large corpus of pictures (663 nouns, 96 verbs) and collected normative data from multilingual speakers of Kannada (a southern Indian language) on two picture-related measures (name agreement, image agreement) and three word-related measures (familiarity, subjective frequency, age of acquisition), and report objective visual complexity and syllable count of the words. Naming labels were classified into words from the target language (i.e., Kannada), cognates (borrowed from/shared with another language), translation equivalents, and elaborations. The picture corpus had > 85% mean concept agreement with multiple acceptable names (1-7 naming labels) for each concept. The mean percentage name agreement for the modal name was > 70%, with H-statistics of 0.89 for nouns and 0.52 for verbs. We also analyse the variability of responses highlighting the influence of bi-/multilingualism on (picture) naming. The picture corpus is freely accessible to researchers and clinicians. It may be used for future standardization with other languages of similar cultural contexts, and relevant items can be used in languages from different cultures, following suitable standardization.

Standardizing norms for 1286 colored pictures in Cantonese

This study established psycholinguistic norms in Cantonese for a set of 1286 colored pictures sourced from several picture databases, including 750 colored line drawings from MultiPic (Duñabeitia et al., 2018) and 536 photographs selected for McRae et al. (2005) concepts. The pictures underwent rigorous normalization processes. We provided picture characteristics including name and concept agreement, familiarity, visual complexity, and frequency of modal responses. Through correlational analyses, we observed strong interrelationships among these variables. We also compared the current Cantonese norming to other languages and demonstrated similarity and variations among different languages. Additionally, we embraced the multilingual diversity within the current sample, and found that higher Cantonese proficiency but lower non-native language proficiency were associated with better spoken picture naming. Last but not least, we validated the predictive power of normed variables calculated from typed responses to spoken picture naming, and the consistency between typed and spoken responses. The present norming provides a timely and valuable alternative for researchers in the field of psycholinguistics, especially those studying Cantonese production and lexical retrieval. All raw data, analysis scripts, and final norming results are available online as psycholinguistic norms for Cantonese in the following link at the Open Science Framework: https://osf.io/dz9j6/?view_only=a452d8a56c92430b9dedf21ac26b1bc1 .

Polish norms for a set of colored drawings of 168 objects and 146 actions with predictors of naming performance

In this study, we present the first database of pictures and their corresponding psycholinguistic norms for Polish: the CLT database. In this norming study, we used the pictures from Cross-Linguistic Lexical Tasks (CLT): a set of colored drawings of 168 object and 146 actions. The CLT pictures were carefully created to provide a valid tool for multicultural comparisons. The pictures are accompanied by norms for Naming latencies, Name agreement, Goodness of depiction, Image agreement, Concept familiarity, Age of acquisition, Imageability, Lexical frequency, and Word complexity. We also report analyses of predictors of Naming latencies for pictures of objects and actions. Our results show that Name agreement, Concept familiarity, and Lexical frequency are significant predictors of Naming latencies for pictures of both objects and actions. Additionally, Age of acquisition significantly predicts Naming latencies of pictures of objects. The CLT database is freely available at osf.io/gp9qd. The full set of CLT pictures, including additional variants of pictures, is available on request at osf.io/y2cwr.