Abstract
Emotional picture databases are commonly used in emotion research. The databases were first based on ratings of emotional dimensions, and the interest in studying discrete emotions led to the categorization of subsets from these databases to emotional categories. However, to-date, studies that categorized affective pictures used confidence intervals in their analysis, a method that provides important data but also results in a high percentage of blended or undifferentiated categorization of images. The current study used 526 affective pictures from four databases and categorized the pictures to discrete emotions in two steps (Pre-testing phase & Experiment 1). First, clinical psychologists were asked to generate emotional labels for each picture, according to the emotion the picture evoked in them. This resulted in the creation of 10 emotional categories. These labels were presented to students who were asked to choose the emotional category that matched the emotion a presented picture evoked in them. Agreement levels on the emotional categories were calculated for each picture, and pictures were categorized according to the most dominant emotion they evoked. The analysis of agreement levels rather than confidence intervals enabled us to provide both dominance of emotional category and agreement in the population regarding the dominance. In Experiment 2, we asked participants to provide ratings of emotional intensity and arousal, in order to provide more detailed information regarding the database. This is the first study to provide agreement levels on the categorization of affective pictures, and may be useful in various studies which aim at generating specific emotions.
Highlights
Emotional experience has an important, evolutionary role in our lives, since it helps us evaluate the environment and guides our reaction to different situations (Frijda & Mesquita, 1994)
The growing interest in studying discrete emotions and the combination between emotional dimensions and discrete emotions led to the addition of emotional categories to the existing emotional picture databases (e.g., IAPS, NAPS)
In order to create the emotional categories, the first author grouped the emotional labels to categories using dictionary definitions
Summary
Emotional experience has an important, evolutionary role in our lives, since it helps us evaluate the environment and guides our reaction to different situations (Frijda & Mesquita, 1994). Classification of the emotional stimuli is according to valence and arousal (for all three databases), dominance (IAPS), approach-avoidance (NAPS), and internal-external norms (GAPED) These primary dimensional classifications lack information regarding discrete emotions. In the NAPS, for example, Riegel et al (2016) asked people to rate the intensity of six emotions, which are known as “basic emotions” (Ekman & Keltner, 1970) – happiness, anger, fear, disgust, sadness, and surprise They found that out of 510 pictures, 72% had a distinct emotional category, 8% were blended and 19% were undifferentiated. Mikels et al (2005) categorized the IAPS based on labels that were generated in a pilot study according to the emotions the pictures evoked They found that out of 203 negative pictures, 42% had a distinct emotional category (disgust, fear or sadness), 24% were blended and 34% were undifferentiated. No comparison between psychologists and the general population was made, but it is reasonable to assume that psychologists have the same, if not better, emotional intelligence than the general population
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