Changes in Posed Facial Expression of Emotion Across the Adult Life Span

Borod, Joan C.; Yecker, Sandra; Brickman, Adam M.; Moreno, Caridad R.; Sliwinski, Martin J.; Foldi, Nancy S.; Alpert, Murray; Welkowitz, Joan

doi:10.1080/03610730490484399

Cited by 43 publications

(41 citation statements)

References 88 publications

(102 reference statements)

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“…As a possible explanation, an own-age effect on decoding accuracy was only found when expressions were difficult to decode. Thus, previous studies with comparatively low decoding accuracy have shown an own-age effect (Fölster et al, 2015;Malatesta et al, 1987), whereas those with higher decoding accuracy did not (Borod et al, 2004;Ebner & Johnson, 2009;Ebner et al, 2012;Ebner et al, 2013;Hühnel et al, 2014). Also in line with this assumption, in the present study, we only found an own-age effect on decoding accuracy for sad expressions of low intensity, and only for older observers.…”

Section: Discussionsupporting

confidence: 90%

ERP evidence for own-age effects on late stages of processing sad faces

Fölster

Werheid

2016

Cogn Affect Behav Neurosci

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Faces convey important information on interaction partners, such as their emotional state and age. Faces of the same age are, according to recent research, preferentially processed. The aim of the present study was to investigate whether the neural processes underlying this own-age effect are influenced by the emotional expression of the face, and to explore possible explanations such as the frequency or quality of contact to own-age versus other-age groups. Event-related potentials were recorded while 19 younger (18-30 years) and 19 older (64-86 years) observers watched younger and older sad and happy faces. Sad but not happy faces elicited higher late positive potential amplitudes for own-age than for other-age faces. This own-age effect was significant for older, but not for younger, observers, and correlated with the quality of contact with the own-age versus the other-age group. This pattern suggests that sad own-age faces are motivationally more relevant.Keywords Own-age effect . Facial expression . Event-related potentials . Late positive potential . Emotion Faces convey crucial information for social interactions: They signal the emotional state of our interaction partners, and also provide important social information on their age, sex, and race. Recent research has revealed preferential processing of faces belonging to one's same age group (own-age faces).

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Section: Discussionsupporting

confidence: 90%

ERP evidence for own-age effects on late stages of processing sad faces

Fölster

Werheid

2016

Cogn Affect Behav Neurosci

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“…However, opposite results were also reported, showing no difference in emotional expressiveness between older and younger adults [16,21]. Results from Borod et al [3] might provide a nice hypothesis to account for such discrepancy as well as for the aforementioned theoretical assumptions. These authors instructed young, middle-aged and old women to produce negative, positive emotions, and neutral facial expressions.…”

Section: Related Workmentioning

confidence: 89%

“…Physiological reactions to emotional experience also decline with age [18]. An alternate theory suggests that these phenomena may be due to a greater emotional control rather than a decline [3,14,21], but the consequences on emotional expression are the same. Indeed, several experimental data show that elderly people's facial expressions are harder to decode, be they voluntarily elicited [18,22] or produced by mood-induction procedures [23].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the neutral poses of older subjects were rated as more intense than those of younger people, which can be due to age-related morphological changes in the face, i.e. permanent wrinkles and folds [3]. Furthermore, these permanent wrinkles remaining visible on neutral expressions can convey personality information [22], e.g.…”

Section: Related Workmentioning

confidence: 99%

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Impact of Expressive Wrinkles on Perception of a Virtual Character’s Facial Expressions of Emotions

Courgeon

Buisine

Martin

2009

Intelligent Virtual Agents

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. Facial animation has reached a high level of photorealism. Skin is rendered with grain and translucency, wrinkles are accurate and dynamic. These recent visual improvements are not fully tested for their contribution to the perceived expressiveness of virtual characters. This paper presents a perceptual study assessing the impact of different rendering modes of expressive wrinkles on users' perception of facial expressions of basic and complex emotions. Our results suggest that realistic wrinkles increase agent's expressivity and user's preference, but not the recognition of emotion categories. This study was conducted using our real time facial animation platform that is designed for perceptive evaluations of affective interaction.

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“…Lastly, the characters used in this study were either age-less or pictures of younger adult humans. The few studies that have investigated older adults’ own-age bias has yielded mixed results (e.g., Borod et al 2004; Ebner & Johnson, 2009; Ebner et al 2010; Malatesta et al 1987), and further investigation on the potential of own-age bias of on-screen characters needs to be further tested.…”

Section: Discussionmentioning

confidence: 99%

Younger and older users׳ recognition of virtual agent facial expressions

Beer

Smarr

Fisk

et al. 2015

International Journal of Human-Computer Studies

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As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent’s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell, Sullivan, Prevost, & Churchill, 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck & Reichenbach, 2005; Courgeon et al. 2009; 2011; Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent’s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition.

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Changes in Posed Facial Expression of Emotion Across the Adult Life Span

Cited by 43 publications

References 88 publications

ERP evidence for own-age effects on late stages of processing sad faces

ERP evidence for own-age effects on late stages of processing sad faces

Impact of Expressive Wrinkles on Perception of a Virtual Character’s Facial Expressions of Emotions

Younger and older users׳ recognition of virtual agent facial expressions

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