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All in My Head?: How Does Mood Affect How We Interpret Facial Expressions ?

All in My Head?: How Does Mood Affect How We Interpret Facial Expressions ?. Purdue University Lilli Ashmore Gregory Francis (Faculty sponsor). The Stink Eye. Ekman’s universal facial expressions Individual differences in emotion recognition in facial expressions

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All in My Head?: How Does Mood Affect How We Interpret Facial Expressions ?

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  1. All in My Head?: How Does Mood Affect How We Interpret Facial Expressions? Purdue University Lilli Ashmore Gregory Francis (Faculty sponsor)

  2. The Stink Eye • Ekman’s universal facial expressions • Individual differences in emotion recognition in facial expressions • How do we reconcile this?

  3. Methodology • Reverse correlation technique by embedding random noise pixels on a face with an ambiguous facial expression (a copy of the Mona Lisa) • Because of the random noise pattern, the resulting image sometimes looks happy and sometimes sad • Subjects classified the facial expressions (sad, slightly sad, slightly happy, happy) for 140 trials. 60 subjects in each experiment (30/cell) • The commonly categorized patterns were then averaged to identify which noise pixels influenced expression categorization. • In addition, mood was manipulated using sad or happy music (experiment 1) or core disposition based on SONA prescreen results (experiment 2).

  4. Example Trials Sad, press A. Slightly sad, press F. Slightly happy, press J. Happy, press ;.

  5. Mood Manipulation – Experiment 1 • Sad subjects listened to… • Adagio for Strings by Samuel Barber • Adagietto by Mahler • Barber Violin Concerto: 2nd movement • Happy subjects listened to… • EinekleineNacht by Mozart • The Nutcracker Suite & Swan Lake by Tchaikovsky • Minuet from the Surprise Symphony by Haydn • Polovtsian Dance by Borodin • Both playlists were approximately 30 minutes long, but all subjects were done after 12-15 minutes.

  6. Mood Manipulation Check • “On a scale from 1 to 9, 9 being happy and 1 being sad, what is your current mood right now?” • Happy condition = 6.44 +/- 1.48 • Sad condition = 6.00 +/- 1.31. • t(60)=1.18, p=0.24.

  7. Interaction: Mood x Response Type 2(3)=37.315, p<0.0001.

  8. Mood Manipulation – Experiment 2 • To what extent are you GENERALLY happy/sad? • In relation to the mean emotionality score in the SONA prescreen results… • 1 STD above = happy emotionality • 1 STD below = sad emotionality • 2(3)=8.511, p=0.037

  9. RESULTS – Experiment 1

  10. Happy Music

  11. Sad Music

  12. Happy – Sad: Clusters of Non-Random Noise

  13. Bayesian analysis

  14. T-test correlation analysis Plots of 21 x 21 pixels significantly different, using the correlation technique.

  15. RESULTS – Experiment 2

  16. Happy Music

  17. Sad Music

  18. Happy - Sad Happy Emotionality Sad Emotionality

  19. Bayesian Analysis

  20. Discussion • Our results combined between Experiment 1 and Experiment 2 suggest evidence for an objective processing of facial expressions, which emboldens Ekman’s theory of universal facial expressions. • These results suggest some people are more sensitive to facial markers than others, which is why observers may report varying results in response to a facial stimulus. • The conclusions from this study can help inform society what parts of the face we might not be attending to, which can be especially helpful for people who struggle to pick up on emotional facial cues. • Future research should work towards testing this experiment in the context of human faces, to see if the effects from processing Mona Lisa’s face is generalizable to the processes involved in interpreting human facial expressions.

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