The psycholinguistic and affective structure of words conveying pain

Abstract

Despite the flourishing research on the relationships between affect and language, the characteristics of pain-related words, a specific type of negative words, have never been systematically investigated from a psycholinguistic and emotional perspective, despite their psychological relevance. This study offers psycholinguistic, affective, and pain-related norms for words expressing physical and social pain. This may provide a useful tool for the selection of stimulus materials in future studies on negative emotions and/or pain. We explored the relationships between psycholinguistic, affective, and pain-related properties of 512 Italian words (nouns, adjectives, and verbs) conveying physical and social pain by asking 1020 Italian participants to provide ratings of Familiarity, Age of Acquisition, Imageability, Concreteness, Context Availability, Valence, Arousal, Pain-Relatedness, Intensity, and Unpleasantness. We also collected data concerning Length, Written Frequency (Subtlex-IT), N-Size, Orthographic Levenshtein Distance 20, Neighbor Mean Frequency, and Neighbor Maximum Frequency of each word. Interestingly, the words expressing social pain were rated as more negative, arousing, pain-related, and conveying more intense and unpleasant experiences than the words conveying physical pain.

Fig 1. Demographic characteristics of participants.

Age distribution across the 20 questionnaires, each represented by a different line. The grey scale for each line represents the gender proportion in the specific sample of participants.

Fig 2. Distribution of valid data points.

Distribution of the final number of valid data points (243,824) across the 10 variables of interest.

Fig 3. Measures reliability.

Distribution Boxplots of the overall half-split reliability distributions over 1,000 random replicates, run separately for each questionnaire and for each variable.

Fig 4. Variables distribution.

Distribution of the variables in the final dataset.

Fig 5. Hierarchical Clustering Analysis dendrogram.

Dendrogram resulting from the Hierarchical Clustering Analysis of the 16 variables.

Fig 6. Partial correlations among all the variables.

The dot color indicates the direction of the correlation (blue for direct, red for inverse) and the size and transparency its strength.

Fig 7. Partial scatterplot.

Partial scatterplot of mean values in the Valence and Arousal dimensions, along with the quadratic regression line (R² = .33).