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Priming Effects on Behavior and Priming Behavioral Concepts: A Commentary on Sherman and Rivers (2020)
- Award ID(s):
- 2204924
- PAR ID:
- 10381369
- Date Published:
- Journal Name:
- Psychological Inquiry
- Volume:
- 32
- Issue:
- 1
- ISSN:
- 1047-840X
- Page Range / eLocation ID:
- 24 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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na (Ed.)In the field of affective computing, emotional annotations are highly important for both the recognition and synthesis of human emotions. Researchers must ensure that these emotional labels are adequate for modeling general human perception. An unavoidable part of obtaining such labels is that human annotators are exposed to known and unknown stimuli before and during the annotation process that can affect their perception. Emotional stimuli cause an affective priming effect, which is a pre-conscious phenomenon in which previous emotional stimuli affect the emotional perception of a current target stimulus. In this paper, we use sequences of emotional annotations during a perceptual evaluation to study the effect of affective priming on emotional ratings of speech. We observe that previous emotional sentences with extreme emotional content push annotations of current samples to the same extreme. We create a sentence-level bias metric to study the effect of affective priming on speech emotion recognition (SER) modeling. The metric is used to identify subsets in the database with more affective priming bias intentionally creating biased datasets. We train and test SER models using the full and biased datasets. Our results show that although the biased datasets have low inter-evaluator agreements, SER models for arousal and dominance trained with those datasets perform the best. For valence, the models trained with the less-biased datasets perform the best.more » « less
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Emotional annotation of data is important in affective computing for the analysis, recognition, and synthesis of emotions. As raters perceive emotion, they make relative comparisons with what they previously experienced, creating “anchors” that influence the annotations. This unconscious influence of the emotional content of previous stimuli in the perception of emotions is referred to as the affective priming effect. This phenomenon is also expected in annotations conducted with out-of-order segments, a common approach for annotating emotional databases. Can the affective priming effect introduce bias in the labels? If yes, how does this bias affect emotion recognition systems trained with these labels? This study presents a detailed analysis of the affective priming effect and its influence on speech emotion recognition (SER). The analysis shows that the affective priming effect affects emotional attributes and categorical emotion annotations. We observe that if annotators assign an extreme score to previous sentences for an emotional attribute (valence, arousal, or dominance), they will tend to annotate the next sentence closer to that extreme. We conduct SER experiments using the most biased sentences. We observe that models trained on the biased sentences perform the best and have the lowest prediction uncertainty.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/1047840X.2021.1889319
