FANCat: French affective norms for ten emotional categories

Emotion processing difficulties in ADHD: a Bayesian meta-analysis study

We investigated whether there is an emotional processing deficit in ADHD and whether this only applies to specific emotional categories. In this PRISMA-compliant systematic review based on a pre-registered protocol ( https://osf.io/egp7d ), we searched MEDLINE, PsycINFO, ERIC, Scopus and Web of Science databases until 3rd December 2023, to identify empirical studies comparing emotional processing in individuals meeting DSM (version III to 5-TR) or ICD (version 9 or 10) criteria for Attention Deficit/Hyperactivity Disorder (ADHD) and in a non-psychiatric control group. Study quality was assessed with the Appraisal tool for Cross-Sectional Studies (AXIS). Eighty studies were included and meta-analysed (encompassing 6191 participants and 465 observations). Bayesian meta-analyses were conducted to compare individuals with ADHD and non-psychiatric controls on overall emotional processing measures (meta-analysis 1) and across emotional categories (meta-analysis 2). The type of stimulus employed, outcome measurement reported, age, sex, and medication status were analysed as moderators. We found poorer performance in both overall emotion processing (g =  - 0.65) and across emotional categories (anger g =  - 0.37; disgust g =  - 0.24; fear g =  - 0.37; sadness g =  - 0.34; surprise g =  - 0.26; happiness/positive g =  - 0.31; negative g =  - 0.20; neutral g =  - 0.25) for individuals with ADHD compared to non-psychiatric controls. Scales items and accuracy outcome being the most effective moderators in detecting such differences. No effects of age, sex, or medication status were found. Overall, these results show that impaired emotional processing is a relevant feature of ADHD and suggest that it should be systematically assessed in clinical practice.

Affective Norms for German as a Second Language (ANGL2)

The present study introduces affective norms for a set of 880 German words rated by learners of German as a second language (L2), i.e., the Affective Norms for German as a Second Language (ANGL2). The database provides ratings across affective and subjective psycholinguistic dimensions. Besides valence and arousal ratings, ANGL2 features data on emotional prototypicality, which helps to identify emotion-label words and emotion-laden words. Moreover, the database includes two additional semantic variables: concreteness and familiarity. We observed similarities with previous studies, and the ratings provided by L2 speakers demonstrate characteristics that should be noted in studies involving bilinguals, including more moderate valence ratings, and a stronger correlation between valence and arousal, specifically for positive words. ANGL2 is the first set of affective norms that has been rated by L2 speakers for a language other than English. The set of norms is aimed to function as a resource for psycholinguistic experimental studies on the intersection between emotion and language among L2 speakers.

Emotion topology: extracting fundamental components of emotions from text using word embeddings

This exploratory study examined the potential of word embeddings, an automated numerical representation of written text, as a novel method for emotion decomposition analysis. Drawing from a substantial dataset scraped from a Social Media site, we constructed emotion vectors to extract the dimensions of emotions, as annotated by the readers of the texts, directly from human language. Our findings demonstrated that word embeddings yield emotional components akin to those found in previous literature, offering an alternative perspective not bounded by theoretical presuppositions, as well as showing that the dimensional structure of emotions is reflected in the semantic structure of their text-based expressions. Our study highlights word embeddings as a promising tool for uncovering the nuances of human emotions and comments on the potential of this approach for other psychological domains, providing a basis for future studies. The exploratory nature of this research paves the way for further development and refinement of this method, promising to enrich our understanding of emotional constructs and psychological phenomena in a more ecologically valid and data-driven manner.

The bright side of words: Norms for 9000 Spanish words in seven discrete positive emotions

In recent years, assumptions about the existence of a single construct of happiness that accounts for all positive emotions have been questioned. Instead, several discrete positive emotions with their own neurobiological and psychological mechanisms have been proposed. Of note, the effects of positive emotions on language processing are not yet properly understood. Here we provide a database for a large set of 9000 Spanish words scored by 3437 participants in the positive emotions of awe, contentment, amusement, excitement, serenity, relief, and pleasure. We also report significant correlations between discrete positive emotions and several affective (e.g., valence, arousal, happiness, negative discrete emotions) and lexico-semantic (e.g., frequency of use, familiarity, concreteness, age of acquisition) characteristics of words. Finally, we analyze differences between words conveying a single emotion ("pure" emotion words) and those denoting more than one emotion ("mixed" emotion words). This study will provide researchers a rich source of information to do research that contributes to expanding the current knowledge on the role of positive emotions in language. The norms are available at https://doi.org/10.6084/m9.figshare.21533571.v2.

Extrapolation of affective norms using transformer-based neural networks and its application to experimental stimuli selection

Data on the emotionality of words is important for the selection of experimental stimuli and sentiment analysis on large bodies of text. While norms for valence and arousal have been thoroughly collected in English, most languages do not have access to such large datasets. Moreover, theoretical developments lead to new dimensions being proposed, the norms for which are only partially available. In this paper, we propose a transformer-based neural network architecture for semantic and emotional norms extrapolation that predicts a whole ensemble of norms at once while achieving state-of-the-art correlations with human judgements on each. We improve on the previous approaches with regards to the correlations with human judgments by Δr = 0.1 on average. We precisely discuss the limitations of norm extrapolation as a whole, with a special focus on the introduced model. Further, we propose a unique practical application of our model by proposing a method of stimuli selection which performs unsupervised control by picking words that match in their semantic content. As the proposed model can easily be applied to different languages, we provide norm extrapolations for English, Polish, Dutch, German, French, and Spanish. To aid researchers, we also provide access to the extrapolation networks through an accessible web application.

Ratings of valence, arousal, happiness, anger, fear, sadness, disgust, and surprise for 24,000 Dutch words

Emotion is a fundamental aspect of human life and therefore is critically encoded in language. To facilitate research into the encoding of emotion in language and how emotion associations affect language processing, we present a new set of emotion norms for over 24,000 Dutch words. The emotion norms include ratings of two key dimensions of emotion: valence and arousal, as well as ratings on discrete emotion categories: happiness, anger, fear, sadness, disgust, and surprise. We show that emotional information can predict word processing, such that responses to positive words are facilitated in contrast to neutral and negative words. We also demonstrate how the ratings of emotion are related to personality characteristics. The data are available via the Open Science Framework ( https://osf.io/9htuv/ ) and serve as a valuable resource for research into emotion as well as in applied settings such as healthcare and digital communication.

A shared structure for emotion experiences from narratives, videos, and everyday life

Our knowledge of the diversity and psychological organization of emotion experiences is based primarily on studies that used a single type of stimulus with an often limited set of rating scales and analyses. Here we take a comprehensive data-driven approach. We surveyed 1,000+ participants on a diverse set of ratings of emotion experiences to a validated set of ca. 150 text narratives, a validated set of ca. 1,000 videos, and over 10,000 personal experiences sampled longitudinally in everyday life, permitting a unique comparison. All three types of emotion experiences were characterized by similar dimensional spaces that included valence and arousal, as well as dimensions related to generalizability. Emotion experiences were distributed along continuous gradients, with no clear clusters even for the so-called basic emotions. Individual differences in personality traits were associated with differences in everyday emotion experiences but not with emotions evoked by narratives or videos.

Sound symbolic associations in Spanish emotional words: affective dimensions and discrete emotions

Sound symbolism refers to non-arbitrary associations between word forms and meaning, such as those observed for some properties of sounds and size or shape. Recent evidence suggests that these connections extend to emotional concepts. Here we investigated two types of non-arbitrary relationships. Study 1 examined whether iconicity scores (i.e. resemblance-based mapping between aspects of a word's form and its meaning) for words can be predicted from ratings in the affective dimensions of valence and arousal and/or the discrete emotions of happiness, anger, fear, disgust and sadness. Words denoting negative concepts were more likely to have more iconic word forms. Study 2 explored whether statistical regularities in single phonemes (i.e. systematicity) predicted ratings in affective dimensions and/or discrete emotions. Voiceless (/p/, /t/) and voiced plosives (/b/, /d/, /g/) were related to high arousing words, whereas high arousing negative words tended to include fricatives (/s/, /z/). Hissing consonants were also more likely to occur in words denoting all negative discrete emotions. Additionally, words conveying certain discrete emotions included specific phonemes. Overall, our data suggest that emotional features might explain variations in iconicity and provide new insight about phonemic patterns showing sound symbolic associations with the affective properties of words.

Construction of a Chinese traditional instrumental music dataset: A validated set of naturalistic affective music excerpts

Music is omnipresent among human cultures and moves us both physically and emotionally. The perception of emotions in music is influenced by both psychophysical and cultural factors. Chinese traditional instrumental music differs significantly from Western music in cultural origin and music elements. However, previous studies on music emotion perception are based almost exclusively on Western music. Therefore, the construction of a dataset of Chinese traditional instrumental music is important for exploring the perception of music emotions in the context of Chinese culture. The present dataset included 273 10-second naturalistic music excerpts. We provided rating data for each excerpt on ten variables: familiarity, dimensional emotions (valence and arousal), and discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, and transcendence). The excerpts were rated by a total of 168 participants on a seven-point Likert scale for the ten variables. Three labels for the excerpts were obtained: familiarity, discrete emotion, and cluster. Our dataset demonstrates good reliability, and we believe it could contribute to cross-cultural studies on emotional responses to music.

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.