Smiling and sad wrinkles: Age-related changes in the face and the perception of emotions and intentions

Heß, Ursula; Adams, Reginald B.; Simard, Annie; Stevenson, Michael; Kleck, Robert E.

doi:10.1016/j.jesp.2012.05.018

Cited by 90 publications

(84 citation statements)

References 14 publications

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“…Hess et al (2012) confirmed this finding with artificially created face stimuli displaying identical expressions for younger and older faces. As an exception, Ebner et al (2010) found no age difference for posed fear expressions, but for happiness, anger, sadness, disgust, and neutrality expressions.…”

Section: Influence Of the Faces' Agementioning

confidence: 79%

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Facial age affects emotional expression decoding

2014

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Facial expressions convey important information on emotional states of our interaction partners. However, in interactions between younger and older adults, there is evidence for a reduced ability to accurately decode emotional facial expressions. Previous studies have often followed up this phenomenon by examining the effect of the observers' age. However, decoding emotional faces is also likely to be influenced by stimulus features, and age-related changes in the face such as wrinkles and folds may render facial expressions of older adults harder to decode. In this paper, we review theoretical frameworks and empirical findings on age effects on decoding emotional expressions, with an emphasis on age-of-face effects. We conclude that the age of the face plays an important role for facial expression decoding. Lower expressivity, age-related changes in the face, less elaborated emotion schemas for older faces, negative attitudes toward older adults, and different visual scan patterns and neural processing of older than younger faces may lower decoding accuracy for older faces. Furthermore, age-related stereotypes and age-related changes in the face may bias the attribution of specific emotions such as sadness to older faces.

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Section: Influence Of the Faces' Agementioning

confidence: 79%

“…Age differences in expressivity are unlikely to be the sole underlying mechanism, as age-of-face effects have also been found when expressivity was controlled for (Hess et al, 2012). Further, previous research rather argues against visual scan patterns as an underlying mechanism.…”

Section: Discussionmentioning

confidence: 99%

Facial age affects emotional expression decoding

2014

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“…Given that emotions affect the physical configuration of facial appearance through the autonomic nervous system (Ekman 1978), facial appearance may come to communicate information to observers about an individual's chronic affective experience over time (Hess et al 2012;Malatesta et al 1987). That is, people with particular dispositions (e.g., anxiety) may come to develop static facial appearances that subtly reflect their frequent but fleeting emotional states (e.g., an anxious person may come to look permanently fearful).…”

Section: Study 2bmentioning

confidence: 99%

Identifying Mental Disorder from the Faces of Women with Borderline Personality Disorder

2016

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Although appearance-based cues can help to diagnose physical illness, visual manifestations of mental disorder may be more elusive. Here, we investigated whether individuals could distinguish women with a serious mental disorder (borderline personality disorder) from demographically-and IQ-matched non-psychiatric controls. Participants rated mentally ill targets as more likely to have a mental disorder from photos more accurately than chance, despite not believing that such judgments were possible. The configuration of facial cues played an important role in these judgments, as interfering with the spatial relationships between facial features reduced participants' accuracy to chance guessing. Further investigation showed similar results when participants rated the targets for specific mental disorders (borderline personality disorder, major depressive disorder) and rated the mentally ill targets as more depressed, angry, anxious, disgusted, emotionally unstable, distressed, and less happy. Moreover, the depression ratings significantly correlated with the targets' actual depressive symptoms. Thus, individuals may be able to infer aspects of mental disorder from minimal facial cues.

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“…For instance, the specific wrinkles are an indicator of middle and intense AU6 (intensity B or more); the intensity of these wrinkles is used to distinguish between AU6 and AU7 (see FACS manual [Ekman et al 2002]). Hess et al [2012] studied the role of age-related structural changes on the face, such as wrinkles and folds, in the nonverbal communication of sadness and happiness. According to their results, age-related wrinkles reduce the signal clarity; emotions were perceived less accurately when shown by an old face.…”

Section: Role Of Wrinkles Presentation Mode and Intensity In The Idmentioning

confidence: 99%

The Effect of Wrinkles, Presentation Mode, and Intensity on the Perception of Facial Actions and Full-Face Expressions of Laughter

Niewiadomski

Pélachaud

2015

ACM Trans. Appl. Percept.

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This article focuses on the identification and perception of facial action units displayed alone as well as the meaning decoding and perception of full-face synthesized expressions of laughter. We argue that the adequate representation of single action units is important in the decoding and perception of full-face expressions. In particular, we focus on three factors that may influence the identification and perception of single actions and full-face expressions: their presentation mode (static vs. dynamic), their intensity, and the presence of wrinkles.For the purpose of this study, we used a hybrid approach for animation synthesis that combines data-driven and procedural animations with synthesized wrinkles generated using a bump mapping method. Using such animation technique, we created animations of single action units and full-face movements of two virtual characters. Next, we conducted two studies to evaluate the role of presentation mode, intensity, and wrinkles in single actions and full-face context-free expressions. Our evaluation results show that intensity and presentation mode influence (1) the identification of single action units and (2) the perceived quality of the animation. At the same time, wrinkles (3) are useful in the identification of a single action unit and (4) influence the perceived meaning attached to the animation of full-face expressions. Thus, all factors are important for successful communication of expressions displayed by virtual characters. ACM Reference Format: Radoslaw Niewiadomski and Catherine Pelachaud. 2015. The effect of wrinkles, presentation mode, and intensity on the perception of facial actions and full-face expressions of laughter.

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Smiling and sad wrinkles: Age-related changes in the face and the perception of emotions and intentions

Cited by 90 publications

References 14 publications

Facial age affects emotional expression decoding

Facial age affects emotional expression decoding

Identifying Mental Disorder from the Faces of Women with Borderline Personality Disorder

The Effect of Wrinkles, Presentation Mode, and Intensity on the Perception of Facial Actions and Full-Face Expressions of Laughter

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