In the present study, we introduce affective norms for a new set of Spanish words, the Madrid Affective Database for Spanish (MADS), that were scored on two emotional dimensions (valence and arousal) and on five discrete emotional categories (happiness, anger, sadness, fear, and disgust), as well as on concreteness, by 660 Spanish native speakers. Measures of several objective psycholinguistic variables-grammatical class, word frequency, number of letters, and number of syllables-for the words are also included. We observed high split-half reliabilities for every emotional variable and a strong quadratic relationship between valence and arousal. Additional analyses revealed several associations between the affective dimensions and discrete emotions, as well as with some psycholinguistic variables. This new corpus complements and extends prior databases in Spanish and allows for designing new experiments investigating the influence of affective content in language processing under both dimensional and discrete theoretical conceptions of emotion. These norms can be downloaded as supplemental materials for this article from www.dropbox.com/s/o6dpw3irk6utfhy/ Hinojosa%20et%20al_Supplementary%20materials.xlsx?dl=0.

The current study presents ratings by 540 Spanish native speakers for dominance, familiarity, subjective age of acquisition (AoA), and sensory experience (SER) for the 875 Spanish words included in the Madrid Affective Database for Spanish (MADS). The norms can be downloaded as supplementary materials for this manuscript from https://figshare.com/s/8e7b445b729527262c88 These ratings may be of potential relevance to researches who are interested in characterizing the interplay between language and emotion. Additionally, with the aim of investigating how the affective features interact with the lexicosemantic properties of words, we performed correlational analyses between norms for familiarity, subjective AoA and SER, and scores for those affective variables which are currently included in the MADs. A distinct pattern of significant correlations with affective features was found for different lexicosemantic variables. These results show that familiarity, subjective AoA and SERs may have independent effects on the processing of emotional words. They also suggest that these psycholinguistic variables should be fully considered when formulating theoretical approaches to the processing of affective language.

The two main theoretical accounts of the human affective space are the dimensional perspective and the discrete-emotion approach. In recent years, several affective norms have been developed from a dimensional perspective, including ratings for valence and arousal. In contrast, the number of published datasets relying on the discrete-emotion approach is much lower. There is a need to fill this gap, considering that discrete emotions have an effect on word processing above and beyond those of valence and arousal. In the present study, we present ratings from 1,380 participants for a set of 2,266 Spanish words in five discrete emotion categories: happiness, anger, fear, disgust, and sadness. This will be the largest dataset published to date containing ratings for discrete emotions. We also present, for the first time, a fine-grained analysis of the distribution of words into the five emotion categories. This analysis reveals that happiness words are the most consistently related to a single, discrete emotion category. In contrast, there is a tendency for many negative words to belong to more than one discrete emotion. The only exception is disgust words, which overlap least with the other negative emotions. Normative valence and arousal data already exist for all of the words included in this corpus. Thus, the present database will allow researchers to design studies to contrast the predictions of the two most influential theoretical perspectives in this field. These studies will undoubtedly contribute to a deeper understanding of the effects of emotion on word processing.

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