Browsing by Subject "computer animation"
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Item Categorization-based stranger avoidance does not explain the uncanny valley effect(Elsevier, 2017-04) MacDorman, Karl F.; Chattopadhyay, Debaleena; BioHealth Informatics, School of Informatics and ComputingThe uncanny valley hypothesis predicts that an entity appearing almost human risks eliciting cold, eerie feelings in viewers. Categorization-based stranger avoidance theory identifies the cause of this feeling as categorizing the entity into a novel category. This explanation is doubtful because stranger is not a novel category in adults; infants do not avoid strangers while the category stranger remains novel; infants old enough to fear strangers prefer photographs of strangers to those more closely resembling a familiar person; and the uncanny valley’s characteristic eeriness is seldom felt when meeting strangers. We repeated our original experiment with a more realistic 3D computer model and found no support for categorization-based stranger avoidance theory. By contrast, realism inconsistency theory explains cold, eerie feelings elicited by transitions between instances of two different, mutually exclusive categories, given that at least one category is anthropomorphic: Cold, eerie feelings are caused by prediction error from perceiving some features as features of the first category and other features as features of the second category. In principle, realism inconsistency theory can explain not only negative evaluations of transitions between real and computer modeled humans but also between different vertebrate species.Item In the uncanny valley, transportation predicts narrative enjoyment more than empathy, but only for the tragic hero(Elsevier, 2019-05) MacDorman, Karl F.; Human-Centered Computing, School of Informatics and ComputingThe uncanny valley is a term used to describe the phenomenon that human simulations that are nearly but not quite realistic often give viewers an uneasy, eerie feeling. Given the prevalence of computer-animated human characters and a narrative framework in videogames, serious games, and health-related scenarios, it is important to examine how the uncanny valley influences narrative empathy and enjoyment. In a 2 × 2 × 2 between-groups posttest-only experiment, 738 participants took the role of a patient in a virtual consultation with a doctor; the consultation varied in the doctor's character (hero or villain), its subplot ending (happy or tragic), and its depiction (computer animated or real). The participants' posttest results showed greater emotional empathy and enjoyment in the hero condition and no significant difference in emotional empathy for the computer animation but greater narrative enjoyment and persuasion. Just endings (hero rewarded, villain punished) elicited much greater pleasure than unjust endings. In comparing computer animation with recorded video, emotional empathy was a significantly stronger predictor of narrative enjoyment than transportation only for the real hero with a tragic ending. The enjoyment and persuasiveness of the computer-animated doctor–patient consultation bodes well for the use of animation in interactive visual narratives.Item A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables(ACM, 2022-03) Diel, Alexander; Weigelt, Sarah; MacDorman, Karl F.; Human-Centered Computing, School of Informatics and ComputingThe 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.Item The persuasiveness of humanlike computer interfaces varies more through narrative characterization than through the uncanny valley(2015) Patel, Himalaya; Pfaff, Mark S.; Ashburn-Nardo, Leslie; MacDorman, Karl F.; Šabanović, SelmaJust as physical appearance affects persuasion and compliance in human communication, it may also bias the processing of information from avatars, computer-animated characters, and other computer interfaces with faces. Although the most persuasive of these interfaces are often the most humanlike, they incur the greatest risk of falling into the uncanny valley, the loss of empathy associated with eerily human characters. The uncanny valley could delay the acceptance of humanlike interfaces in everyday roles. To determine the extent to which the uncanny valley affects persuasion, two experiments were conducted online with undergraduates from Indiana University. The first experiment (N = 426) presented an ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (recorded or animated), motion quality (smooth or jerky), and recommendation (disclose or refrain from disclosing sensitive information). Of these, only the recommendation changed opinion about the dilemma, even though the animated depiction was eerier than the human depiction. These results indicate that compliance with an authority persists even when using a realistic computer-animated double. The second experiment (N = 311) assigned one of two different dilemmas in professional ethics involving the fate of a humanlike character. In addition to the dilemma, there were three manipulations of the character’s human realism: depiction (animated human or humanoid robot), voice (recorded or synthesized), and motion quality (smooth or jerky). In one dilemma, decreasing depiction realism or increasing voice realism increased eeriness. In the other dilemma, increasing depiction realism decreased perceived competence. However, in both dilemmas realism had no significant effect on whether to punish the character. Instead, the willingness to punish was predicted in both dilemmas by narratively characterized trustworthiness. Together, the experiments demonstrate both direct and indirect effects of narratives on responses to humanlike interfaces. The effects of human realism are inconsistent across different interactions, and the effects of the uncanny valley may be suppressed through narrative characterization.Item Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not(Elsevier, 2016-01) MacDorman, Karl F.; Chattopadhyay, Debaleena; Human-Centered Computing, School of Informatics and ComputingHuman replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica’s realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes–eyelashes–mouth and skin–nose–eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations—those eliciting the greatest category uncertainty—were neither the eeriest nor the coldest.