In all cases, the p values were acceptable. The results of four studies support the validity of the DEQ, a discrete emotions questionnaire S1 Appendix. Study 1 was intended to generate a starting base of emotion words that are used by laypeople to name the emotions they experience in various situations. In Study 2, these words, supplemented as needed with emotion words from Shaver et al.
Participants recalled emotional experiences, as in Study 1, and indicated their endorsement of these words. In Study 3, additional positive words were included to better capture discrete positive emotions, and participants rated their endorsement of these words after guided imagery of emotional scenarios. Results of Study 3 were conceptually replicated in Study 4 which revealed that the ratings on the DEQ subscales were influenced in predictable ways by a different manipulation of emotion states.
Moreover, the internal consistency of each subscale is more than adequate and experimental manipulations of emotion influence the emotion subscales in the predicted manner in all studies.
The results from the ANOVAs clearly indicate that the Discrete Emotions Questionnaire is sensitive at assessing discrete emotional responses to discrete emotional events. The exploratory factor analyses also supported the existence of discrete emotions, even though they were unable to take into account the experimental manipulation of emotions.
It is important to note that, in the validation studies for other affect scales such as the PANAS, emotions were not manipulated; rather participants were asked to endorse the items while in a neutral setting, presumably not experiencing strong emotion [ 7 ]. In contrast, participants in the current studies were assigned to experience one of 8 emotions, adding an important source of variance to their ratings.
Past research has revealed that inducing emotions changes the factor structure of affect scales[ 11 , 12 ]. Although we could have conducted a study similar to the validation studies for the PANAS, by asking participants to respond to the DEQ by indicating how they feel in a non-emotional context, we believe that such a study would be uninformative.
The DEQ is intended to measure state emotions, so in a non-emotional context, responses to the negative emotion subscales of the DEQ anger, disgust, fear, anxiety, and sadness should be near the floor for most individuals. The goal in creating the DEQ was to create an instrument that would be sensitive to state emotions.
In our view, whether the instrument has good factor structure in the absence of emotion-inducing situations is not informative. These studies showed that the DEQ is sensitive to several different manipulations of emotion: autobiographical recall, guided imagery, and pictorial stimuli. Scores on the target subscales were elevated significantly more than scores on the other subscales by these stimuli.
Fear manipulations elevated both Fear and Anxiety ratings, whereas anxiety manipulations elevated only Anxiety ratings, but not Fear ratings. Given that the Fear and Anxiety items behave differently in this way, we chose to retain both subscales in the DEQ. We based our design of the studies and inclusion of both anxiety and fear on existing theories and research that have revealed differences between anxiety and fear, as reviewed earlier. If both fear and anxiety were not included in the DEQ, then future research might miss these important differences in outcomes between fear and anxiety situations.
Thus, we included the two subscales in the DEQ. Although these are certainly not the only discrete emotions, they cover the broad categories considered basic and important by a number of emotion theorists [ 3 , 4 , 5 , 39 ]. We have developed a measure of emotion that is sensitive to discrete emotional states, and maintains a decent overall factor structure when capturing dynamic emotional states.
Yet, it may not be necessary to use the entire DEQ when measuring emotion. In some cases, the use of a few subscales or a single subscale of the DEQ might be sufficient.
Using the full scale may be beneficial when a researcher wants to examine whether participants are experiencing mixed emotions, or when a manipulation may cause one emotion for some participants and a different emotion for other participants.
In other cases, the participant fatigue brought on by completing many items might outweigh the benefits of measuring more emotions than those directly of interest. We note that although we have validated a coherent measure comprising eight subscales that are sensitive to situational changes in emotional states, we have not exhausted full possibilities of the DEQ.
Rather we view this as a work in progress that may be contributed to over time. For instance, researchers might find that they are interested in specific emotions not currently included in the DEQ.
We would suggest that in these cases the DEQ, as specified here, could be useful for assisting those researchers to construct measures of additional emotions by modelling the approach used in the DEQ , and to demonstrate the discriminant validity of those measures by showing they do not overlap with other measures in the DEQ. Overtime, additional emotions may be added to, and validated as part of, the DEQ.
In the end, we hope that the DEQ will be of use to researchers interested in measuring self-reported discrete emotions associated with emotional states.
We believe our understanding of psychological and behavioral processes will be better understood by using a measure such as the DEQ that more accurately captures emotional responses to events. Indeed, we have already reported the results of three experiments that revealed the DEQ to be more sensitive than the PANAS at detecting self-reported emotions to discrete events [ 71 ]. Emotions are vital psychological constructs that need to be measured as well as possible so that research can fully understand their impact on psychological processes.
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Abstract Several discrete emotions have broad theoretical and empirical importance, as shown by converging evidence from diverse areas of psychology, including facial displays, developmental behaviors, and neuroscience. Introduction Beginning with the theories of Darwin [ 1 ] and James [ 2 ], several theories of discrete emotions have been proposed [ 3 , 4 , 5 , 6 ]. On Accurately Assessing Self-Reported Emotions Psychologists prefer to use established, published measures of constructs.
Theories of Basic Emotions In designing our Discrete Emotions Questionnaire DEQ , we aimed to include emotions that are considered "basic" by prominent emotion theories. Study 1 In Study 1, we aimed to generate a list of words that individuals use to name emotions. Method Materials. Download: PPT. Manipulation checks. Results We performed a count of the words generated for each condition.
Table 3. Study 2 For Study 2, participants recalled an emotional experience similar to Study 1 , but instead of generating emotion words, they rated the extent to which they had experienced emotions during the experience, using the preliminary emotion scale developed in Study 1. Method Participants. Table 5. Study 3 For Study 3, we wanted to examine how well the scale discriminated among discrete emotions using a different manipulation of emotion.
Results The data S3 Data were subjected to the same factor analysis parameters as used in Study 2. Table 8. Study 4 In Study 4, we wanted to assess whether our emotion instrument was sensitive to a different manipulation of emotion. Results To examine whether these subscales were sensitive to our emotional picture manipulation, one-way analyses of variance ANOVA were conducted with the picture sets as independent variables and the average ratings on each emotion subscale as the dependent measures Table 9.
Table 9. Supporting Information. S1 Appendix. The Discrete Emotions Questionnaire. DEQ with instructions. S1 Data. Data file for Study 1. Generate words following emotion-evoking prompts. S2 Data. Data file for Study 2. Rate words following emotion-evoking prompts. S3 Data. Data file for Study 3. Rate words following emotion-evoking scenarios. S4 Data. Data file for Study 4. Rate words following emotion-evoking photographs. References 1. Darwin C. The descent of man and selection in relation to sex.
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Toward an understanding of the determinants of anger. Anger is an approach-related affect: Evidence and implications. Psychol Bull. What is Approach Motivation? Emot Rev. Emotions are physical and instinctive, instantly prompting bodily reactions to threat, reward, and everything in between. Top of Page. While emotions are associated with bodily reactions that are activated through neurotransmitters and hormones released by the brain, feelings are the conscious experience of emotional reactions.
Originating in the neocortical regions of the brain, feelings are sparked by emotions and shaped by personal experiences, beliefs, memories, and thoughts linked to that particular emotion. Strictly speaking, a feeling is the side product of your brain perceiving an emotion and assigning a certain meaning to it [7]. Interestingly, this process works both ways: While the actual encounter with a spider stimulus might freak you out, just thinking of it can activate the same emotional response.
The conscious nature of feelings makes it quite easy to measure them using self-reporting tools such as interviews, surveys , and questionnaires including rating scales and self-assessment procedures. In commercial and academic human behavior research , collecting data about emotional responses and feelings is central for obtaining valuable insights into processes associated with observable actions, thoughts, and memories of the respondent group of your interest.
Brendan Murray and Dr. Cognitive neuroscience of emotional memory. Nature Reviews Neuroscience, 7 1 , Emotion, Decision Making and the Orbitofrontal Cortex.
Cerebral Cortex , 10 3 , Foundations of human reasoning in the prefrontal cortex. Science , , Encoding of emotional memories depends on amygdala and hippocampus and their interactions. Using AI to measure emotions As part of the research, they collected EEG data from 40 students while they watched videos, in this case video clips from the Pixar movie Up. Each of the clips was intended to trigger a specific emotion: happiness, sadness, fear and disgust, and participants viewed five short clips for each of the emotions.
Based on the frequency and topography of the EEG signal, the machine learning was able to successfully predict which of the emotions the clip being watched corresponded with. Researchers also found that it could be applied on a moment-by-moment basis so could be used to track changes in emotion over time. Due to their subjective nature, finding a way to accurately and unobtrusively measure emotions has proven difficult.
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