The Croatian psycholinguistic database: Estimates for 6000 nouns, verbs, adjectives and adverbs

CROWD-5e: A Croatian psycholinguistic database of affective norms for five discrete emotions

The present study introduces affective norms for a set of 3022 Croatian words on five discrete emotions: happiness, anger, sadness, fear, and disgust. The words were rated by 1239 Croatian native speakers. Each participant rated 251 or 252 words for one discrete emotion on a five-point Likert scale. The analyses revealed a significant relationship between discrete emotions, emotional dimensions (valence and arousal), and other psycholinguistic properties of words. In addition, small sex differences in discrete emotion ratings were found. Finally, the analysis of the distribution of words among discrete emotions allowed a distinction between "pure" words (i.e., those mostly related to a single emotion) and "mixed" words (i.e., those related to more than one emotion). The new database extends the existing Croatian affective norms collected from a dimensional conception of emotions, providing the necessary resource for future experimental investigation in Croatian within the theoretical framework of discrete emotions.

Specificity ratings for Italian data

Abstraction enables us to categorize experience, learn new information, and form judgments. Language arguably plays a crucial role in abstraction, providing us with words that vary in specificity (e.g., highly generic: tool vs. highly specific: muffler). Yet, human-generated ratings of word specificity are virtually absent. We hereby present a dataset of specificity ratings collected from Italian native speakers on a set of around 1K Italian words, using the Best-Worst Scaling method. Through a series of correlation studies, we show that human-generated specificity ratings have low correlation coefficients with specificity metrics extracted automatically from WordNet, suggesting that WordNet does not reflect the hierarchical relations of category inclusion present in the speakers' minds. Moreover, our ratings show low correlations with concreteness ratings, suggesting that the variables Specificity and Concreteness capture two separate aspects involved in abstraction and that specificity may need to be controlled for when investigating conceptual concreteness. Finally, through a series of regression studies we show that specificity explains a unique amount of variance in decision latencies (lexical decision task), suggesting that this variable has theoretical value. The results are discussed in relation to the concept and investigation of abstraction.

Is "earth" an animate thing? Cross-language and inter-age analyses of animacy word ratings in European Portuguese and British English young and older adults

Animacy plays an important role in cognition (e.g., memory and language). Across languages, a processing advantage for animate words (representing living beings), comparatively to inanimate words (i.e., non-living things), has been found mostly in young adults. Evidence in older adults, though, is still unclear, possibly due to the use of stimuli not properly characterised for this age group. Indeed, whereas several animacy word-rating studies already exist for young adults, these are non-existent for older adults. This work provides animacy ratings for 500 British English and 224 European Portuguese words, rated by young and older adults from the corresponding countries. The comparisons across languages and ages revealed a high interrater agreement. Nonetheless, the Portuguese samples provided higher mean ratings of animacy than the British samples. Also, the older adults assigned, on average, higher animacy ratings than the young adults. The Age X Language interaction was non-significant. These results suggest an inter-age and inter-language consistency in whether a word represents an animate or an inanimate thing, although with some differences, emphasising the need for age- and language-specific word rating data. The animacy ratings are available via OSF: https://osf.io/6xjyv/.

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.

Familiarity ratings for 24,325 simplified Chinese words

The present work collected familiarity norms for 20,275 two-character, 1231 three-character, and 2819 four-character simplified Chinese words from 1300 native speakers of Mandarin Chinese. The familiarity of each word was rated on a 7-point scale by at least 21 participants. The results supported the reliability and validity of the present familiarity ratings, which is the first large familiarity database for Chinese in the field. These familiarity norms can be downloaded from the supplemental materials. Furthermore, the contribution of familiarity to Chinese lexical processing was investigated using the present familiarity ratings and previous data (lexical features and visual lexical decision), mainly from two major Chinese lexicon projects, MELD-SCH and CLP. Regression analysis suggests that familiarity explained a substantial percentage of the variance in lexical processing of both simplified and traditional Chinese words, over and above the effects of word frequency and other lexical features, including age of acquisition (AoA). Further analysis identified a significantly greater familiarity effect for lower-frequency words than that for higher-frequency words. Together, among the first, our findings support the important contribution of familiarity with Chinese words to lexical processing, especially for low-frequency words.

How do word valence and classes influence lexical processing? Evidence from virtual reality emotional contexts

The current study examines the influence of word class (i.e., noun vs. adjective) and valence (i.e., positive vs. negative vs. neutral) on the processing of emotional words under different virtual reality (VR) emotional contexts. To this end, 115 participants performed a modified affect labeling task after experiencing different VR scenarios. Their galvanic skin responses were also examined to further gauge the different effects of VR contexts. The results demonstrated significant main effect for word valence, indicating more processing of positive words relative to neutral words which are processed more than negative words. The results also demonstrated significant main effect for word class, indicating more processing of nouns in contrast to adjectives. Additionally, the results indicated that both positive and negative VR contexts could stimulate participants to select more positive words though negatively valenced words were processed more under negative VR context relative to positive VR context. However, the amplitude of galvanic skin responses in positive VR was lower than that in negative VR. The results were interpreted in line with the situation-consistency effects, the mood-consistency effects, the specific nature of VR context, and the different features of different word classes in terms of concreteness, imageability, arousal, and valence.

Extrapolation of Human Estimates of the Concreteness/ Abstractness of Words by Neural Networks of Various Architectures

In a great deal of theoretical and applied cognitive and neurophysiological research, it is essential to have more vocabularies with concreteness/abstractness ratings. Since creating such dictionaries by interviewing informants is labor-intensive, considerable effort has been made to machine-extrapolate human rankings. The purpose of the article is to study the possibility of the fast construction of high-quality machine dictionaries. In this paper, state-of-the-art deep learning neural networks are involved for the first time to solve this problem. For the English language, the BERT model has achieved a record result for the quality of a machine-generated dictionary. It is known that the use of multilingual models makes it possible to transfer ratings from one language to another. However, this approach is understudied so far and the results achieved so far are rather weak. Microsoft’s Multilingual-MiniLM-L12-H384 model also obtained the best result to date in transferring ratings from one language to another. Thus, the article demonstrates the advantages of transformer-type neural networks in this task. Their use will allow the generation of good-quality dictionaries in low-resource languages. Additionally, we study the dependence of the result on the amount of initial data and the number of languages in the multilingual case. The possibilities of transferring into a certain language from one language and from several languages together are compared. The influence of the volume of training and test data has been studied. It has been found that an increase in the amount of training data in a multilingual case does not improve the result.