Uncanny valley hypothesis and hierarchy of facial features in the human likeness continua: An eye-tracking approach.

2022 ◽  
Author(s):  
Ivan Bouchardet da Fonseca Grebot ◽  
Pedro Henrique Pinheiro Cintra ◽  
Emilly Fátima Ferreira de Lima ◽  
Michella Vaz de Castro ◽  
Rui de Moraes
2013 ◽  
Vol 4 ◽  
Author(s):  
Marcus Cheetham ◽  
Ivana Pavlovic ◽  
Nicola Jordan ◽  
Pascal Suter ◽  
Lutz Jancke

2015 ◽  
Vol 24 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Himalaya Patel ◽  
Karl F. MacDorman

Just as physical appearance affects social influence in human communication, it may also affect the processing of advice conveyed through avatars, computer-animated characters, and other human-like interfaces. Although the most persuasive computer interfaces are often the most human-like, they have been predicted to incur the greatest risk of falling into the uncanny valley, the loss of empathy attributed to characters that appear eerily human. Previous studies compared interfaces on the left side of the uncanny valley, namely, those with low human likeness. To examine interfaces with higher human realism, a between-groups factorial experiment was conducted through the internet with 426 midwestern U.S. undergraduates. This experiment presented a hypothetical ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (digitally recorded or computer animated), motion quality (smooth or jerky), and advice (disclose or refrain from disclosing sensitive information). Of these, only the advice changed opinion about the ethical dilemma, even though the animated depiction was significantly eerier than the human depiction. These results indicate that compliance with an authority persists even when using an uncannily realistic computer-animated double.


2018 ◽  
Author(s):  
Jari Kätsyri ◽  
Beatrice de Gelder ◽  
Tapio Takala

The uncanny valley (UV) hypothesis suggests that increasingly human-like robots or virtual characters elicit more familiarity in their observers (positive affinity) with the exception of near-human characters that elicit strong feelings of eeriness (negative affinity). We studied this hypothesis in three experiments with carefully matched images of virtual faces varying from artificial to realistic. We investigated both painted and computer-generated (CG) faces to tap a broad range of human-likeness and to test whether CG faces would be particularly sensitive to the UV effect. Overall, we observed a linear relationship with a slight upward curvature between human-likeness and affinity. In other words, less realistic faces triggered greater eeriness in an accelerating manner. We also observed a weak UV effect for CG faces; however, least human-like faces elicited much more negative affinity in comparison. We conclude that although CG faces elicit a weak UV effect, this effect is not fully analogous to the original UV hypothesis. Instead, the subjective evaluation curve for face images resembles an uncanny slope more than a UV. Based on our results, we also argue that subjective affinity should be contrasted against subjective ratherthan objective measures of human-likeness when testing UV.


2021 ◽  
Author(s):  
Shira C. Segal

The ability to recognize facial expressions of emotion is a critical part of human social interaction. Infants improve in this ability across the first year of life, but the mechanisms driving these changes and the origins of individual differences in this ability are largely unknown. This thesis used eye tracking to characterize infant scanning patterns of expressions. In study 1 (n = 40), I replicated the preference for fearful faces, and found that infants either allocated more attention to the eyes or the mouth across both happy and fearful expressions. In study 2 (n = 40), I found that infants differentially scanned the critical facial features of dynamic expressions. In study 3 (n = 38), I found that maternal depressive symptoms and positive and negative affect were related to individual differences in infants’ scanning of emotional expressions. Implications for our understanding of the development of emotion recognition are discussed. Key Words: emotion recognition, infancy eye tracking, socioemotional development


Author(s):  
Boyoung Kim ◽  
Elizabeth Phillips

Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design.


2017 ◽  
Vol 13 (3) ◽  
Author(s):  
Paweł Łupkowski ◽  
Marek Rybka ◽  
Dagmara Dziedzic ◽  
Wojciech Włodarczyk

AbstractThe Uncanny Valley Hypothesis (UVH, proposed in the 1970s) suggests that looking at or interacting with almost human-like artificial characters would trigger eeriness or discomfort. We studied how well subjects can assess degrees of human likeness for computer-generated characters. We conducted two studies, where subjects were asked to assess human likeness of given computer-generated models (Study 1) and to point the most typical model for a given category (Study 2). The results suggest that evaluation of the way human likeness is assessed should be an internal part of UVH research.


Author(s):  
Hidenobu Sumioka ◽  
Takashi Minato ◽  
Yoshio Matsumoto ◽  
Pericle Salvini ◽  
Hiroshi Ishiguro

2018 ◽  
Vol 71 (8) ◽  
pp. 1797-1806
Author(s):  
Peter J Hills ◽  
Dominic M Hill

Sad individuals are more accurate at face identity recognition, possibly because they scan more of the face during encoding. During expression identification tasks, sad individuals do not fixate on the eyes as much as happier individuals. Fixating on features other than the eyes leads to a reduced own-ethnicity bias. This background indicates that sad individuals would not view the eyes as much as happy individuals, and this would result in improved expression recognition and reduced own-ethnicity bias. This prediction was tested using an expression identification task with eye tracking. We demonstrate that sad-induced participants show enhanced expression recognition and a reduced own-ethnicity bias than happy-induced participants due to scanning more facial features. We conclude that mood affects eye movements and face encoding by causing a wider sampling strategy and deeper encoding of facial features diagnostic for expression identification.


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