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

Diel, Alexander; Weigelt, Sarah; MacDorman, Karl F.

doi:10.1145/3470742

Cited by 41 publications

(50 citation statements)

References 139 publications

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“…Rating scales are the preferred method of measuring uncanniness in UV research, as they allow measuring a differentiated subjective experience ( Diel, Weigelt, & MacDorman, 2022 ; Ho & MacDorman, 2017 ). For stimulus ratings, some of the most used and most effective ratings scales in uncanny valley research were used according to a meta-analysis ( Diel et al, 2022 ): creepy , eerie , repulsive , and strange . Items were combined into an uncanniness index.…”

Section: Methodsmentioning

confidence: 99%

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Familiarity, orientation, and realism increase face uncanniness by sensitizing to facial distortions

Diel¹,

Lewis

2022

Journal of Vision

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The uncanny valley predicts aversive reactions toward near-humanlike entities. Greater uncanniness is elicited by distortions in realistic than unrealistic faces, possibly due to familiarity. Experiment 1 investigated how familiarity and inversion affect uncanniness of facial distortions and the ability to detect differences between the distorted variants of the same face (distortion sensitivity). Familiar or unfamiliar celebrity faces were incrementally distorted and presented either upright or inverted. Uncanniness ratings increased across the distortion levels, and were stronger for familiar and upright faces. Distortion sensitivity increased with increasing distortion difference levels, again stronger for familiar and upright faces. Experiment 2 investigated how face realism, familiarity, and face orientation interacted for the increase of uncanniness across distortions. Realism increased the increase of uncanniness across the distortion levels, further enhanced by upright orientation and familiarity. The findings show that familiarity, upright orientation, and high face realism increase the sensitivity of uncanniness, likely by increasing distortion sensitivity. Finally, a moderated linear function of face realism and deviation level could explain the uncanniness of stimuli better than a quadratic function. A re-interpretation of the uncanny valley as sensitivity toward deviations from familiarized patterns is discussed.

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Section: Methodsmentioning

confidence: 99%

“…All faces were either upright or inverted, creating a total of 300 faces (2 familiarity × 2 orientation × 5 realism levels × 3 distortion level × 5 faces). Selecting images of different characters or objects is one of the most common practices in uncanny valley research (see distinct entities in Diel et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Familiarity, orientation, and realism increase face uncanniness by sensitizing to facial distortions

Diel¹,

Lewis

2022

Journal of Vision

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“…It is possible that with the framing of the Aibo as a robot, expectations for matching the exact fidelity of a dog were lower compared to the puppy frame. Because possible mechanisms of uncanniness are routed in perceptual mismatches [ 49 , 63 ], the mismatch between the robot frame and the fur may have been much lower or non-existent compared to the other conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Not all efforts to improve a robot’s dog-likeness may result in a positive outcome. A well-studied phenomenon in human-robot interaction is the uncanny valley hypothesis which predicts that as artificial agents become increasingly, but not perfectly human-like, peoples’ responses towards these agents become increasingly positive (e.g., likable, trustworthy) up to a point after which responses fall into a markedly negative “valley” (e.g., eerie, creepy) [ 46 , 47 , 48 , 49 ]. Recent studies have shown that there may also be an uncanny valley for robots that are animal-like (zoomorphic), such that robots high and low in animal-likeness (i.e., PARO, MiRO, etc.)…”

Section: Introductionmentioning

confidence: 99%

Designing Man’s New Best Friend: Enhancing Human-Robot Dog Interaction through Dog-Like Framing and Appearance

Visser

Topoglu

Joshi

et al. 2022

Sensors

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To understand how to improve interactions with dog-like robots, we evaluated the importance of “dog-like” framing and physical appearance on interaction, hypothesizing multiple interactive benefits of each. We assessed whether framing Aibo as a puppy (i.e., in need of development) versus simply a robot would result in more positive responses and interactions. We also predicted that adding fur to Aibo would make it appear more dog-like, likable, and interactive. Twenty-nine participants engaged with Aibo in a 2 × 2 (framing × appearance) design by issuing commands to the robot. Aibo and participant behaviors were monitored per second, and evaluated via an analysis of commands issued, an analysis of command blocks (i.e., chains of commands), and using a T-pattern analysis of participant behavior. Participants were more likely to issue the “Come Here” command than other types of commands. When framed as a puppy, participants used Aibo’s dog name more often, praised it more, and exhibited more unique, interactive, and complex behavior with Aibo. Participants exhibited the most smiling and laughing behaviors with Aibo framed as a puppy without fur. Across conditions, after interacting with Aibo, participants felt Aibo was more trustworthy, intelligent, warm, and connected than at their initial meeting. This study shows the benefits of introducing a socially robotic agent with a particular frame and importance on realism (i.e., introducing the robot dog as a puppy) for more interactive engagement.

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“…Apart from our main outcome variable eeriness (Gray & Wegner, 2012), which remains one of the most well-established ways of operationalizing robot acceptance (Diel et al, 2022), we used two additional dependent variables to get a more general overview of participants' assessment of this type of robotic technology. First, we focused on concerns about human identity, which emerged as a meaningful predictor of technology-related experience in previous research (Stein et al, 2019).…”

Section: The Current Researchmentioning

confidence: 99%

Mind-reading machines: Distinct user responses to thought-detecting and emotion-detecting robots.

Grundke¹,

Stein²,

Appel³

2022

Technology, Mind, and Behavior

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Human-like robots and other systems with artificial intelligence are increasingly capable of recognizing and interpreting the mental processes of their human users. The present research examines how people evaluate these seemingly mind-reading machines based on the well-established distinction of human mind into agency (i.e., thoughts and plans) and experience (i.e., emotions and desires). Theory and research that applied this distinction to human-robot interaction showed that machines with experience were accepted less and were perceived to be eerier than those with agency. Considering that humans are not yet used to having their thoughts read by other entities and might feel uneasy about this notion, we proposed that thought-detecting robots are perceived to be eerier and are generally evaluated more negatively than emotion-detecting robots. Across two pre-registered experiments (N 1 = 335, N 2 = 536) based on text vignettes about different kinds of mind-detecting robots, we find support for our hypothesis. Furthermore, the effect remained independent of the six HEXACO personality dimensions, except for an unexpected interaction with conscientiousness. Implications and directions for future research are discussed.

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

Cited by 41 publications

References 139 publications

Familiarity, orientation, and realism increase face uncanniness by sensitizing to facial distortions

Familiarity, orientation, and realism increase face uncanniness by sensitizing to facial distortions

Designing Man’s New Best Friend: Enhancing Human-Robot Dog Interaction through Dog-Like Framing and Appearance

Mind-reading machines: Distinct user responses to thought-detecting and emotion-detecting robots.

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