Effect of emotion and articulation of speech on the Uncanny Valley in virtual characters

This paper presents a study of how exaggerated facial expression in the lower face region affects perception of emotion and the Uncanny Valley phenomenon in realistic, human-like, virtual characters. Characters communicated the six basic emotions, anger, disgust, fear, sadness and surprise with normal and exaggerated mouth movements. Measures were taken for perceived familiarity and human-likeness. The results showed that: an increased intensity of articulation significantly reduced the uncanny for anger; yet increased perception of the uncanny for characters expressing happiness with an exaggeration of mouth movement. The practical implications of these findings are considered when controlling the uncanny in virtual characters

Factors of Emotion and Affect in Designing Interactive Virtual Characters

The Arts: 1st Place (The Ohio State University Edward F. Hayes Graduate Research Forum)This paper represents a review of literature concerning factors of affective interactive virtual character design. Affect and it's related concepts are defined followed by a detail of work being conducted in relevant areas such as design, animation, robotics. The intent of this review as to inform the author on overlapping concepts in fields related to affective design in order to apply these concepts interactive character development.A three-year embargo was granted for this item

Uncanny valley effect: A qualitative synthesis of empirical research to assess the suitability of using virtual faces in psychological research

Recently, virtual faces are often used as stimuli to replace traditional photographs in human face perception studies. However, despite being increasingly human-like and realistic, they still present flaws in their aspects that might elicit eerie feelings in the observers, known as the Uncanny Valley (UV) effect. The current systematic review offers a qualitative synthesis of empirical studies investigating observers' subjective experience with virtual compared to real faces to discuss the possible challenges that the UV effect poses when virtual faces are used as stimuli to study face perception. Results: revealed that virtual faces are judged eerier than real faces. Perception of uncanniness represents a challenge in face perception research as it has been associated with negative emotions and avoidance behaviors that might influence observers' responses to these stimuli. Also, observers perceive virtual faces as more deviating from familiar patterns than real faces. Lower perceptual familiarity might have several implications in face perception research, as virtual faces might be considered as a category of stimuli distinct from real faces and therefore processed less efficiently. In conclusion, our findings suggest that researchers should be cautious in using these stimuli to study face perception

Rethinking the uncanny valley as a moderated linear function: Perceptual specialization increases the uncanniness of facial distortions

The relationship between artificial entities’ human likeness and aesthetic preference is thought to be best modelled by an N-shaped cubic “uncanny valley” function, which however suffers from conceptual criticisms and lack of parsimony. Here it is argued that uncanniness effects may instead be modelled by a linear function of deviation moderated by perceptual specialization. The two models are compared in an experiment with five incrementally distorted face types (cartoon, CG, drawing, real, robot). Recognition performance for upright and inverted faces were used as a specialization measure. Specialization significantly moderated the linear effect of distortion on uncanniness, and could explain the data better than a conventional uncanny valley. The uncanny valley may thus be better understood as a moderated linear function of specialization sensitizing the uncanniness of deviating stimuli. This simpler yet more accurate model is compatible with neurocognitive theories and can explain uncanniness effects beyond the conventional uncanny valley

Too real for comfort? Uncanny responses to computer generated faces

As virtual humans approach photorealistic perfection, they risk making real humans uncomfortable. This intriguing phenomenon, known as the uncanny valley, is well known but not well understood. In an effort to demystify the causes of the uncanny valley, this paper proposes several perceptual, cognitive, and social mechanisms that have already helped address riddles like empathy, mate selection, threat avoidance, cognitive dissonance, and psychological defenses. In the four studies described herein, a computer generated human character’s facial proportions, skin texture, and level of detail were varied to examine their effect on perceived eeriness, human likeness, and attractiveness. In Study I, texture photorealism and polygon count increased human likeness. In Study II, texture photorealism heightened the accuracy of human judgments of ideal facial proportions. In Study III, atypical facial proportions were shown to be more disturbing on photorealistic faces than on other faces. In Study IV, a mismatch in the size and texture of the eyes and face was especially prone to make a character eerie. These results contest the depiction of the uncanny valley as a simple relation between comfort level and human likeness. This paper concludes by introducing a set of design principles for bridging the uncanny valley

The audio/visual mismatch and the uncanny valley: an investigation using a mismatch in the human realism of facial and vocal aspects of stimuli

Indiana University-Purdue University Indianapolis (IUPUI)Empirical research on the uncanny valley has primarily been concerned with visual elements. The current study is intended to show how manipulating auditory variables of the stimuli affect participant’s ratings. The focus of research is to investigate whether an uncanny valley effect occurs when humans are exposed to stimuli that have an incongruity between auditory and visual aspects. Participants were exposed to sets of stimuli which are both congruent and incongruent in their levels of audio/visual humanness. Explicit measures were used to explore if a mismatch in the human realism of facial and vocal aspects produces an uncanny valley effect and attempt to explain a possible cause of this effect. Results indicate that an uncanny valley effect occurs when humans are exposed to stimuli that have an incongruity between auditory and visual aspects

The Surface of Acceptability in Virtual Faces

This paper explores the surface properties of skin and eyes and their importance in the acceptance and success of a digital human face, specifically in relation to the uncanny valley. The uncanny valley hypothesis states that as a human representation approaches photo-realism, subtle differences from reality become unsettling. Recent studies suggest that the uncanny valley could exist over a far greater range, affecting abstract human representations as well. These competing findings are explored by analyzing how changes to the surface of a digital character affect its level of acceptance. A female facial model is used as a base to compare a spectrum of different simulated real-world materials. The variations range from materials that are nearly identical to human skin, to those that are completely divergent from it, thus unnatural. After studying this catalogue of materials, it is concluded that given the right conditions, the uncanny valley can occur when facial representations are very near realism, as well as when human-likeness is quite distant from reality

A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity, and eeriness, and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance, and viewing duration. This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research

A meta-analysis of the uncanny valley's independent and dependent variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity, and eeriness, and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance, and viewing duration. This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research