Creepy cats and strange high houses: Support for configural processing in testing predictions of nine uncanny valley theories.

In 1970, Masahiro Mori proposed the uncanny valley (UV), a region in a human-likeness continuum where an entity risks eliciting a cold, eerie, repellent feeling. Recent studies have shown that this feeling can be elicited by entities modeled not only on humans but also nonhuman animals. The perceptual and cognitive mechanisms underlying the UV effect are not well understood, although many theories have been proposed to explain them. To test the predictions of nine classes of theories, a within-subjects experiment was conducted with 136 participants. The theories' predictions were compared with ratings of 10 classes of stimuli on eeriness and coldness indices. One type of theory, configural processing, predicted eight out of nine significant effects. Atypicality, in its extended form, in which the uncanny valley effect is amplified by the stimulus appearing more human, also predicted eight. Threat avoidance predicted seven; atypicality, perceptual mismatch, and mismatch+ predicted six; category+, novelty avoidance, mate selection, and psychopathy avoidance predicted five; and category uncertainty predicted three. Empathy's main prediction was not supported. Given that the number of significant effects predicted depends partly on our choice of hypotheses, a detailed consideration of each result is advised. We do, however, note the methodological value of examining many competing theories in the same experiment.

Familiar faces rendered strange: Why inconsistent realism drives characters into the uncanny valley.

Computer-modeled characters resembling real people sometimes elicit cold, eerie feelings. This effect, called the uncanny valley, has been attributed to uncertainty about whether the character is human or living or real. Uncertainty, however, neither explains why anthropomorphic characters lie in the uncanny valley nor their characteristic eeriness. We propose that realism inconsistency causes anthropomorphic characters to appear unfamiliar, despite their physical similarity to real people, owing to perceptual narrowing. We further propose that their unfamiliar, fake appearance elicits cold, eerie feelings, motivating threat avoidance. In our experiment, 365 participants categorized and rated objects, animals, and humans whose realism was manipulated along consistency-reduced and control transitions. These data were used to quantify a Bayesian model of categorical perception. In hypothesis testing, we found reducing realism consistency did not make objects appear less familiar, but only animals and humans, thereby eliciting cold, eerie feelings. Next, structural equation models elucidated the relation among realism inconsistency (measured objectively in a two-dimensional Morlet wavelet domain inspired by the primary visual cortex), realism, familiarity, eeriness, and warmth. The fact that reducing realism consistency only elicited cold, eerie feelings toward anthropomorphic characters, and only when it lessened familiarity, indicates the role of perceptual narrowing in the uncanny valley.

PAGED: a pathway and gene-set enrichment database to enable molecular phenotype discoveries.

BACKGROUND: Over the past decade, pathway and gene-set enrichment analysis has evolved into the study of high-throughput functional genomics. Owing to poorly annotated and incomplete pathway data, researchers have begun to combine pathway and gene-set enrichment analysis as well as network module-based approaches to identify crucial relationships between different molecular mechanisms. METHODS: To meet the new challenge of molecular phenotype discovery, in this work, we have developed an integrated online database, the Pathway And Gene Enrichment Database (PAGED), to enable comprehensive searches for disease-specific pathways, gene signatures, microRNA targets, and network modules by integrating gene-set-based prior knowledge as molecular patterns from multiple levels: the genome, transcriptome, post-transcriptome, and proteome. RESULTS: The online database we developed, PAGED http://bio.informatics.iupui.edu/PAGED is by far the most comprehensive public compilation of gene sets. In its current release, PAGED contains a total of 25,242 gene sets, 61,413 genes, 20 organisms, and 1,275,560 records from five major categories. Beyond its size, the advantage of PAGED lies in the explorations of relationships between gene sets as gene-set association networks (GSANs). Using colorectal cancer expression data analysis as a case study, we demonstrate how to query this database resource to discover crucial pathways, gene signatures, and gene network modules specific to colorectal cancer functional genomics. CONCLUSIONS: This integrated online database lays a foundation for developing tools beyond third-generation pathway analysis approaches on for discovering molecular phenotypes, especially for disease-associated pathway/gene-set enrichment analysis.

Parental Acceptance of Children's Storytelling Robots: A Projection of the Uncanny Valley of AI.

Parent-child story time is an important ritual of contemporary parenting. Recently, robots with artificial intelligence (AI) have become common. Parental acceptance of children's storytelling robots, however, has received scant attention. To address this, we conducted a qualitative study with 18 parents using the research technique design fiction. Overall, parents held mixed, though generally positive, attitudes toward children's storytelling robots. In their estimation, these robots would outperform screen-based technologies for children's story time. However, the robots' potential to adapt and to express emotion caused some parents to feel ambivalent about the robots, which might hinder their adoption. We found three predictors of parental acceptance of these robots: context of use, perceived agency, and perceived intelligence. Parents' speculation revealed an uncanny valley of AI: a nonlinear relation between the human likeness of the artificial agent's mind and affinity for the agent. Finally, we consider the implications of children's storytelling robots, including how they could enhance equity in children's access to education, and propose directions for research on their design to benefit family well-being.

The doctor's digital double: how warmth, competence, and animation promote adherence intention.

BACKGROUND: Each year, patient nonadherence to treatment advice costs the US healthcare system more than $300 billion and results in 250,000 deaths. Developing virtual consultations to promote adherence could improve public health while cutting healthcare costs and usage. However, inconsistencies in the realism of computer-animated humans may cause them to appear eerie, a phenomenon termed the uncanny valley. Eeriness could reduce a virtual doctor's credibility and patients' adherence. METHODS: In a 2 × 2 × 2 between-groups posttest-only experiment, 738 participants played the role of a patient in a hypothetical virtual consultation with a doctor. The consultation varied in the doctor's Character (good or poor bedside manner), Outcome (received a fellowship or sued for malpractice), and Depiction (a recorded video of a real human actor or of his 3D computer-animated double). Character, Outcome, and Depiction were designed to manipulate the doctor's level of warmth, competence, and realism, respectively. RESULTS: Warmth and competence increased adherence intention and consultation enjoyment, but realism did not. On the contrary, the computer-animated doctor increased adherence intention and consultation enjoyment significantly more than the doctor portrayed by a human actor. We propose that enjoyment of the animated consultation caused the doctor to appear warmer and more real, compensating for his realism inconsistency. Expressed as a path model, this explanation fit the data. DISCUSSION: The acceptance and effectiveness of the animation should encourage the development of virtual consultations, which have advantages over creating content with human actors including ease of scenario revision, internationalization, localization, personalization, and web distribution.

Categorization-based stranger avoidance does not explain the uncanny valley effect.

The 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.

Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not.

Human 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.

The Uncanny Valley Does Not Interfere with Level 1 Visual Perspective Taking.

When a computer-animated human character looks eerily realistic, viewers report a loss of empathy; they have difficulty taking the character's perspective. To explain this perspective-taking impairment, known as the uncanny valley, a novel theory is proposed: The more human or less eerie a character looks, the more it interferes with level 1 visual perspective taking when the character's perspective differs from that of the human observer (e.g., because the character competitively activates shared circuits in the observer's brain). The proposed theory is evaluated in three experiments involving a dot-counting task in which participants either assumed or ignored the perspective of characters varying in their human photorealism and eeriness. Although response times and error rates were lower when the number of dots faced by the observer and character were the same (congruent condition) than when they were different (incongruent condition), no consistent pattern emerged between the human photorealism or eeriness of the characters and participants' response times and error rates. Thus, the proposed theory is unsupported for level 1 visual perspective taking. As the effects of the uncanny valley on empathy have not previously been investigated systematically, these results provide evidence to eliminate one possible explanation.

Facilitators and barriers to adopting robotic-assisted surgery: contextualizing the unified theory of acceptance and use of technology.

Robotic-assisted surgical techniques are not yet well established among surgeon practice groups beyond a few surgical subspecialties. To help identify the facilitators and barriers to their adoption, this belief-elicitation study contextualized and supplemented constructs of the unified theory of acceptance and use of technology (UTAUT) in robotic-assisted surgery. Semi-structured individual interviews were conducted with 21 surgeons comprising two groups: users and nonusers. The main facilitators to adoption were Perceived Usefulness and Facilitating Conditions among both users and nonusers, followed by Attitude Toward Using Technology among users and Extrinsic Motivation among nonusers. The three main barriers to adoption for both users and nonusers were Perceived Ease of Use and Complexity, Perceived Usefulness, and Perceived Behavioral Control. This study's findings can assist surgeons, hospital and medical school administrators, and other policy makers on the proper adoption of robotic-assisted surgery and can guide future research on the development of theories and framing of hypotheses.

A mismatch in the human realism of face and voice produces an uncanny valley.

The uncanny valley has become synonymous with the uneasy feeling of viewing an animated character or robot that looks imperfectly human. Although previous uncanny valley experiments have focused on relations among a character's visual elements, the current experiment examines whether a mismatch in the human realism of a character's face and voice causes it to be evaluated as eerie. The results support this hypothesis.

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.

Dynamics of co-transcriptional pre-mRNA folding influences the induction of dystrophin exon skipping by antisense oligonucleotides.

Antisense oligonucleotides (AONs) mediated exon skipping offers potential therapy for Duchenne muscular dystrophy. However, the identification of effective AON target sites remains unsatisfactory for lack of a precise method to predict their binding accessibility. This study demonstrates the importance of co-transcriptional pre-mRNA folding in determining the accessibility of AON target sites for AON induction of selective exon skipping in DMD. Because transcription and splicing occur in tandem, AONs must bind to their target sites before splicing factors. Furthermore, co-transcriptional pre-mRNA folding forms transient secondary structures, which redistributes accessible binding sites. In our analysis, to approximate transcription elongation, a "window of analysis" that included the entire targeted exon was shifted one nucleotide at a time along the pre-mRNA. Possible co-transcriptional secondary structures were predicted using the sequence in each step of transcriptional analysis. A nucleotide was considered "engaged" if it formed a complementary base pairing in all predicted secondary structures of a particular step. Correlation of frequency and localisation of engaged nucleotides in AON target sites accounted for the performance (efficacy and efficiency) of 94% of 176 previously reported AONs. Four novel insights are inferred: (1) the lowest frequencies of engaged nucleotides are associated with the most efficient AONs; (2) engaged nucleotides at 3' or 5' ends of the target site attenuate AON performance more than at other sites; (3) the performance of longer AONs is less attenuated by engaged nucleotides at 3' or 5' ends of the target site compared to shorter AONs; (4) engaged nucleotides at 3' end of a short target site attenuates AON efficiency more than at 5' end.