Younger and older users׳ recognition of virtual agent facial expressions

Abstract

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.

With or against Each Other? The Influence of a Virtual Agent’s (Non)cooperative Behavior on User’s Cooperation Behavior in the Prisoners’ Dilemma

“…It has been shown, that elderly people are more nervous during an interaction with a virtual agent [10] and, thus, it is especially important to calm them down and find a subtle way to gain their willingness to cooperate. One potential factor to evoke cooperation behavior might be nonverbal cues, but prior findings suggest that seniors showed a higher tendency to misinterpret emotional displays [11]. Hence, it is possible that seniors might also show a higher tendency to misinterpret nonverbal cooperation indicators.…”

A Study on Removing Cloud Drift of Sky-Sea Infrared Image Based on Agent

“…EAMCORE can fulfill coordination between Agents based on team planning and group belief. A BDI structure of the individual Agent expands content and context in order to describe group concept [16]. Regulatory organization takes organization as a group of Agent and a rule set, which can define organization's control and restriction to the action and interaction of the Agent based on laws from sociology.…”

Utilizing ICT to prevent loneliness and social isolation of the elderly. A literature review

“…Evidence from the data showed that the usage of ICT can significantly improve and enhance the quality of life among older adults (Fokkema & Knipscheer, 2007;Bobillier Chaumon, Michel, Tarpin Bernard & Croisile, 2013;Heart & Kalderon, 2013;Niehaves & Plattfaut, 2013;De Schutter, Brown & Abeele, 2014;Gustafson et al, 2015;Sokoler & Svensson, 2007;Beer, Smarr, Fisk & Rogers, 2015;Tsai, Shillair, Cotten,Winstead & Yost, 2015;Wu, Damnée, Kerhervé, Ware & Rigaud,, 2015) and that digital games can even lead to «successful aging» (Allaire, McLaughlin, Trujillo, Whitlock, LaPorte & Gandy, 2013). Firstly, the assistant function of ICT has been shown to offer older adults the opportunities and resources for independent living as long as they want (Cresci & Jarosz, 2010;Niehaves & Plattfaut, 2013;Sokoler & Svensson, 2007).…”

“…The articles included in the data have shown that the emotional needs of older adults have built up around them a significant focus on innovations, research and services for the elderly (Fokkema & Knipscheer, 2007;Allaire, McLaughlin, Trujillo, Whitlock, LaPorte & Gandy, 2013;Beer, Smarr, Fisk & Rogers, 2015;Hill, Betts & Gardner, 2015). As the out-migration of young people has become more common in several countries, the older generation sometimes have been left alone without enough emotional support due to social isolation, which leads to loneliness (Yamada & Teerawichitchainan, 2015).…”