Emotion Elicitation Under Audiovisual Stimuli Reception: Should Artificial Intelligence Consider the Gender Perspective?
<p>Discrete emotions draft mapping in a 2-dimensional space for arousal and valence.</p> "> Figure 2
<p>Processing of audiovisual materials in the process of creation a balanced set of video clips for fear/no-fear detection system.</p> "> Figure 3
<p>Distribution of the reported emotions of the UC3M4Safety database videos in the AV space by target emotion and quadrant.</p> "> Figure 4
<p>Emotion labelling distribution (0.00–1.00) between the emotions reported by the volunteers with respect to the original target per video clip selected, gender and quadrant 1 (percentage computed considering individually women and men totals per stimuli).</p> "> Figure 5
<p>Emotion labelling distribution (0.00–1.00) between the emotions reported by the volunteers with respect to the original target per video clip selected, gender and quadrant 2 (percentage computed considering individually women and men totals per stimuli).</p> "> Figure 6
<p>Emotion labelling distribution (0.00–1.00) between the emotions reported by the volunteers with respect to the original target per video clip selected, gender and quadrant 3 (percentage computed considering individually women and men totals per stimuli).</p> "> Figure 7
<p>Emotion labelling distribution (0.00–1.00) between the emotions reported by the volunteers with respect to the original target per video clip selected, gender and quadrant 4 (percentage computed considering individually women and men totals per stimuli).</p> ">
Abstract
:1. Introduction
1.1. Human Emotions and Their Categorization
1.2. Triggering Emotions and Its Usefulness
1.3. Research Objectives
- (1)
- Are emotions best labelled in the PAD space or with the discrete values?
- (2)
- Is there a high level of agreement in the reported emotions?
- (3)
- Are emotional responses different for women and men?
- (4)
- Can we select a representative and small set of audiovisual stimuli to elicit emotions adequately?
2. Materials and Methods
2.1. Materials
2.2. Procedure
2.3. Sample
2.4. Data Analysis
3. Results
3.1. Reported and Target Emotions: Discrete Emotions and PAD Space (Numerical Values)
3.2. Gender Differences in Emotion Labelling onto Affective Space Quadrants
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Positive Emotions | Negative Emotions |
---|---|
Joy | Sadness |
Surprise, amusement | Contempt, indifference |
Hope, trust, pride, achievement | Fear |
Attraction, desire, interest, admiration | Disgust, aversion, revulsion |
Tenderness, gratitude, contentment, satisfaction | Anger, rage, fury |
Calm | Tedium, boredom |
Emotion | No. Surveyed Video Clips: 162 (No. Analysed for DB (80)) |
---|---|
Joy | 13 (5) |
Sadness | 12 (6) |
Surprise | 18 (4) |
Contempt | 10 (8) |
Hope | 8 (6) |
Fear | 40 (21) |
Attraction | 6 (6) |
Disgust | 8 (4) |
Tenderness | 12 (10) |
Anger | 12 (1) |
Calm | 10 (6) |
Tedium | 13 (3) |
Target Emotion | Quadrant in AV Space | N Videos |
---|---|---|
Joy | Q1 | 4 |
Sadness | Q3 | 3 |
Surprise | Q1 | 2 |
Contempt | Q3 | 0 |
Hope | Q1 | 1 |
Fear | Q2 | 19 |
Attraction | Q1 | 0 |
Disgust | Q3 | 3 |
Tenderness | Q4 | 2 |
Anger | Q2 | 3 |
Calm | Q4 | 4 |
Tedium | Q3 | 1 |
N Videos | Average Votes | SD Votes | Max–Min | Mean (Women) | SD (Women) | Max–Min (Women) | Mean (Men) | SD (Men) | Max–Min (Men) |
---|---|---|---|---|---|---|---|---|---|
42 | 83,881 | 22,106 | 129–52 | 46,309 | 14,195 | 75–25 | 37,429 | 11,236 | 62–27 |
Emotion Category | Gender | Valence Mean (SD) | Arousal Mean (SD) | Dominance Mean (SD) |
---|---|---|---|---|
Joy | General | 8.1 (1.1) | 4.4 (2.5) | 7.0 (2.0) |
Women | 8.2 (1.1) | 4.4 (2.5) | 7.1 (2.1) | |
Men | 7.9 (1.1) | 4.3 (2.4) | 6.8 (2.0) | |
Sadness | General | 3.1 (1.7) | 5.6 (2.0) | 5.5 (2.2) |
Women | 3.0 (1.8) | 5.8 (2.0) | 5.3 (2.3) | |
Men | 3.2 (1.7) | 5.4 (2.0) | 5.8 (2.0) | |
Surprise | General | 6.3 (1.7) | 4.8 (2.0) | 6.6 (1.8) |
Women | 6.3 (1.7) | 4.8 (2.0) | 6.7 (1.9) | |
Men | 6.2 (1.6) | 4.8 (2.0) | 6.6 (1.8) | |
Contempt | General | 4.5 (1.4) | 4.0 (2.1) | 7.0 (1.9) |
Women | 4.3 (1.4) | 4.4 (2.2) | 6.9 (1.9) | |
Men | 4.7 (1.4) | 3.7 (2.0) | 7.1 (1.9) | |
Hope | General | 7.5 (1.6) | 4.2 (2.3) | 7.1 (1.8) |
Women | 7.6 (1.6) | 3.9 (2.3) | 7.4 (1.8) | |
Men | 7.4 (1.6) | 4.6 (2.2) | 6.8 (1.8) | |
Fear | General | 2.6 (1.6) | 7.2 (1.5) | 4.1 (2.2) |
Women | 2.4 (1.6) | 7.4 (1.5) | 4.0 (2.3) | |
Men | 2.9 (1.5) | 6.9 (1.5) | 4.4 (2.1) | |
Attraction | General | 6.3 (1.7) | 4.5 (2.2) | 7.1 (1.8) |
Women | 6.5 (1.7) | 4.3 (2.3) | 7.3 (1.7) | |
Men | 6.2 (1.7) | 4.8 (2.0) | 6.8 (1.8) | |
Disgust | General | 3.1 (1.8) | 6.0 (2.0) | 5.3 (2.4) |
Women | 3.2 (1.8) | 6.0 (2.0) | 5.2 (2.5) | |
Men | 3.1 (1.8) | 6.0 (1.9) | 5.4 (2.3) | |
Tenderness | General | 7.9 (1.3) | 3.5 (2.3) | 6.9 (2.3) |
Women | 8.0 (1.2) | 3.6 (2.3) | 6.8 (2.4) | |
Men | 7.7 (1.4) | 3.4 (2.3) | 7.0 (2.1) | |
Anger | General | 2.4 (1.6) | 6.6 (1.7) | 5.1 (2.2) |
Women | 2.4 (1.6) | 6.7 (1.7) | 5.1 (2.3) | |
Men | 2.5 (1.5) | 6.6 (1.6) | 5.2 (2.1) | |
Calm | General | 7.1 (1.6) | 2.4 (1.8) | 7.5 (1.7) |
Women | 7.4 (1.6) | 2.2 (1.6) | 7.4 (1.9) | |
Men | 6.9 (1.6) | 2.6 (1.9) | 7.7 (1.6) | |
Tedium | General | 4.7 (1.3) | 3.5 (2.1) | 6.8 (2.1) |
Women | 4.8 (1.5) | 3.7 (2.2) | 6.5 (2.1) | |
Men | 4.7 (1.0) | 3.3 (1.9) | 7.1 (2.0) |
Emotion | Arousal | Valence | Dominance |
---|---|---|---|
Joy | 0.834 | 0.00027 *** | 0.0123 * |
Sadness | 0.0604 | 0.236 | 0.00621 ** |
Surprise | 0.786 | 0.669 | 0.743 |
Contempt | 0.00047 *** | 0.00256 ** | 0.427 |
Hope | 0.00694 ** | 0.169 | 0.00523 ** |
Fear | 4.25 × 10−9 *** | 1.42 × 10−7 *** | 0.000956 *** |
Attraction | 0.0314 * | 0.0946 | 0.00463 ** |
Disgust | 0.722 | 0.922 | 0.471 |
Tenderness | 0.494 | 0.0824 | 0.374 |
Anger | 0.449 | 0.28 | 0.708 |
Calm | 0.0088 ** | 0.000137 *** | 0.0684 |
Tedium | 0.0314 * | 0.782 | 0.0113 * |
Joy | Sadness | Surprise | Contempt | Hope | Fear | |
Women | −1.5559 | −0.4584 | −1.7373 | −5.2307 ** | −0.7867 | 6.0483 ** |
Men | 1.5559 | 0.4584 | 1.7373 | 5.2307 ** | 0.7867 | −6.0483 ** |
Attraction | Disgust | Tenderness | Anger | Calm | Tedium | |
Women | −1.2545 | 1.8722 | −0.1990 | 1.1330 | −0.9483 | −0.6388 |
Men | 1.2545 | −1.8722 | 0.1990 | −1.1330 | 0.9483 | 0.6388 |
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Blanco-Ruiz, M.; Sainz-de-Baranda, C.; Gutiérrez-Martín, L.; Romero-Perales, E.; López-Ongil, C. Emotion Elicitation Under Audiovisual Stimuli Reception: Should Artificial Intelligence Consider the Gender Perspective? Int. J. Environ. Res. Public Health 2020, 17, 8534. https://doi.org/10.3390/ijerph17228534
Blanco-Ruiz M, Sainz-de-Baranda C, Gutiérrez-Martín L, Romero-Perales E, López-Ongil C. Emotion Elicitation Under Audiovisual Stimuli Reception: Should Artificial Intelligence Consider the Gender Perspective? International Journal of Environmental Research and Public Health. 2020; 17(22):8534. https://doi.org/10.3390/ijerph17228534
Chicago/Turabian StyleBlanco-Ruiz, Marian, Clara Sainz-de-Baranda, Laura Gutiérrez-Martín, Elena Romero-Perales, and Celia López-Ongil. 2020. "Emotion Elicitation Under Audiovisual Stimuli Reception: Should Artificial Intelligence Consider the Gender Perspective?" International Journal of Environmental Research and Public Health 17, no. 22: 8534. https://doi.org/10.3390/ijerph17228534