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

Abstract

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. The 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.

Full text

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A meta-analysis of the uncanny valley’s
independent and dependent variables
Alexander Diel
School of Psychology, Cardiff University, 70 Park Place, Cardiff CF10 3AT, United
Kingdom, diela@cardiff.ac.uk
Sarah Weigelt
Department of Vision, Visual Impairments & Blindness, Faculty of Rehabilitation
Sciences, Technical University of Dortmund, Emil-Figge-Straße 50, 44227 Dortmund,
Germany, sarah.weigelt@tu-dortmund.de
Karl F. MacDorman
School of Informatics and Computing, Indiana University, 535 West Michigan St.,
Indianapolis, IN 46202, USA, kmacdorm@indiana.edu
ABSTRACT
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. The 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.
CCS Concepts
• Human-centered computing → HCI design and evaluation methods; • Computer systems
organization → External interfaces for robotics; • Computing methodologies → Animation
Keywords
Anthropomorphism, computer animation, face perception, robotics, uncanny valley

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1 Introduction
Royle (2003) gives an evocative and succinct description of the uncanny experience:
The uncanny is ghostly. It is concerned with the strange, weird, and mysterious, with
a flickering sense (but not conviction) of something supernatural. The uncanny
involves feelings of uncertainty, in particular regarding the reality of who one is and
what is being experienced. (p. 1)
Figure 1. The uncanny valley as proposed by Mori in 1970. The affective reaction towards an
entity (y-axis) is a function of its degree of human likeness (x-axis) and whether it is still or
moving (solid or dashed line). Bunraku puppets play character roles in ningyō jōruri, a
traditional form of musical puppet theater in Japan. Actors in nō theater wear masks: The yase
otoko mask (literally, thin man) signifies a ghost from hell, and the okina mask signifies an old
man.
Objects, situations, and events that do not fit our everyday understanding of the world are often
described as eerie, creepy, or uncanny. These ascriptions can be made regarding new technologies
(Langer & König, 2018), unusual human behavior (McAndrew & Koehnke, 2016), or peculiar
coincidences (Freud, 1919/2003). Negative evaluations can hinder the adoption of supportive
products like healthcare robots (Olaronke, Ojerinde, & Ikono, 2017) or service chatbots
(Ciechanowski, Przegalińska, Magnuski, & Gloor, 2019). As the robotics pioneer Mori proposed
in 1970, human-looking androids and other objects could elicit a reaction unlike the one typically
elicited by people or stylish technology. Mori (2012) illustrated this phenomenon with a graph
(Figure 1). The y-axis depicts affinity, the dependent variable (DV), as a function of human
likeness, the independent variable (IV), on the x-axis (Bartneck, Kulić, Croft, & Zoghbi, 2009b;
Ho & MacDorman, 2010, 2017; MacDorman & Ishiguro, 2006). The stimulus sets in Figure 2
show how different creation techniques have been used to operationalize the independent
variable.
According to Mori (2012), affinity for an entity increases with its human likeness but only up to a
point. Beyond this point, affinity falls and becomes negative, and the entity elicits a cold, eerie,

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repellant feeling. Then, affinity rises again, becoming positive, as human likeness increases
toward indistinguishability. When graphed, the fall and rise in affinity resemble a valley—hence,
the term uncanny valley (UV).
Since Mori’s proposal, a substantial body of research has replicated a valley-shaped curve and
found a significant effect (Burleigh, Schoenherr, & Lacroix, 2013; Ferrey, Burleigh, & Fenske,
2015; Jung & Cho, 2018; MacDorman, Green, Ho, & Koch, 2009; Mäkäräinen, Kätsyri, &
Takala, 2014; Mathur & Reichling, 2016; Mathur et al., 2020; McDonnell, Breidt, & Bülthoff,
2012; Palomäki et al., 2018; Sasaki, Ihaya, & Yamada, 2017; Strait et al., 2017; Strait, Vujovic,
Floerke, Scheutz, & Urry, 2015; Tinwell, Grimshaw, & Nabi, 2015; Tinwell, Grimshaw, Nabi, &
Williams, 2011; Tinwell & Sloan, 2014; Yamada, Kawabe, & Ihaya, 2013). However, some
studies have plotted functions other than a valley-shaped curve: For example, Kätsyri, de Gelder,
and Takala (2019) found affinity increased with human likeness, an “uncanny slope”; Cheetham,
Suter, and Jäncke (2014) interpreted increasing familiarity ratings with the transition from avatar
to ambiguous morph to human as a “happy valley”; and Bartneck, Kanda, Ishiguro, and Hagita
(2009a) and Cheetham, Wu, Pauli, and Jäncke (2015) found no difference in affective responses
toward androids and humans. Although the UV effect is seldom disputed, its theoretical basis and
methodologies have eluded consensus. This motivated us to examine how the independent and
dependent variables in Mori’s graph have been operationalized in the literature.
Although several reviews have examined the UV effect (Kätsyri, Förger, Mäkäräinen, & Takala,
2015; Lay, Brace, Pike, & Pollick, 2016; Wang, Lilienfeld, & Rochat, 2015; Zhang et al., 2020),
this is the first meta-analysis to do so. It confirmed the effect’s significance and determined its
effect size. This is also, of course, the first meta-analysis to evaluate the uncanny valley’s
stimulus creation methods and affect and indirect measures. The evaluation was accomplished
using meta-regression models. From the results, we distill design principles for future
experiments.
The UV effect has been conceptualized in different ways. These conceptualizations often stem
from different theories and their assumptions about elicitors of the effect (Diel & MacDorman,
2021). They include
1. a function like Mori’s graph that maps a given degree of human likeness to a level of affect
(Bartneck et al., 2009a; Burleigh, Schoenherr, & Lacroix, 2013; Chen, Russel, Nakayama, &
Livingstone, 2010; Gray & Wegner, 2012; Kätsyri, de Gelder, & Takala, 2019; Lin et al, 2021;
Ramey, 2005; Sasaki, Ihaya, & Yamada, 2017; Schneider, Wang, & Yang, 2009; Schwind et
al., 2018; Seyama & Nagayama, 2007);
2. deviations from norms of human appearance and movement (Chaminade, Hodgins, & Kawato,
2007; MacDorman & Ishiguro, 2006; Mathur & Reichling, 2016; Palomäki et al., 2018;
Schoenherr & Burleigh, 2015; Seyama & Nagayama, 2007; Tinwell, 2009; Tinwell &
Grimshaw, 2009; Tinwell, Grimshaw, & Nabi, 2014);
3. violations of expectations of human appearance and behavior (Bartneck et al., 2009a;
MacDorman & Ishiguro, 2006);
4. sensitivity to nonhuman features that increases with an entity’s human likeness (Chattopadhyay
& MacDorman, 2016; Green, MacDorman, Ho, & Vasudevan, 2008; MacDorman, Srinivas, &
Patel, 2013);

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5. a mismatch between human and nonhuman features (Ho & MacDorman, 2010; MacDorman,
Green, Koch, & Ho, 2009; Mitchell et al., 2011b; Moore, 2012; Takahashi, Fukuda, Samejima,
Watanabe, & Ueda, 2015; Tinwell & Sloan, 2014);
6. entities that elicit the concept human but have nonhuman traits (Steckenfinger & Ghazanfar,
2009); and
7. difficulty distinguishing between categories, such as human and robot, or a conflict between
categories (Cheetham, Pavlović, Jordan, Suter, & Jäncke, 2013; Cheetham, Suter, & Jäncke,
2011, 2014; Cheetham, Wu, Pauli, & Jäncke, 2015; Matsuda, Okamoto, Ida, Okanoya, &
Myowa-Yamakoshi, 2012).
Figure 2. Different operationalizations of the independent variable human likeness (Feng et al.,
2018; Ferrey, Burleigh, and Fenske, 2015; MacDorman et al., 2009; Mäkäräinen, Kätsyri, &
Talaka, 2014, derived from Langner et al., 2010; Mathur & Reichling, 2016; Schindler et al.,
2017).
Ferrey, Burleigh, and Fenske, 2015
MacDorman et al., 2009
Mäkäräinen, Kätsyri, and Takala, 2014
Mathur and Reichling, 2016
Schindler et al., 2017
Feng et al., 2018

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1.1 The independent variable
1.1.1 Construct
In experiments on the UV effect, the independent variable is typically human likeness or a similar
term. However, it is unclear precisely how human likeness relates to the UV curve. Human
likeness can be characterized along many dimensions, which interact to create an overall
impression of humanness (Bartneck et al., 2009b; von Zitzewitz, Boesch, Wolf, & Riener, 2013).
Mori (2012) examines both the outward appearance and the behavior of androids, corpses, and
industrial and toy robots. In discussing mannequins, prostheses, and bunraku puppets, he draws in
other dimensions, such as the setting, lighting, story, time of day, and the perceiver’s gender and
distance. Research corroborates the multidimensionality of human likeness in exploring the
relation between the UV effect and an entity’s physical (MacDorman & Ishiguro, 2006; Seyama
& Nagayama, 2007), behavioral (MacDorman et al., 2005; Złotowski et al., 2015), and perceived
mental similarity to humans (Gray & Wegner, 2012; Stein & Ohler, 2017). The perception of
nonhuman animals can also elicit the UV effect (Chattopadhyay & MacDorman, 2016; Löffler,
Dörenbächer, & Hassenzahl, 2020; Schwind et al., 2018; Takahashi et al., 2015; Yamada,
Kawabe, & Ihaya, 2013). This result casts doubt on whether the independent variable solely
concerns human likeness. Realism or zoomorphism have served as alternative concepts.
Furthermore, Mori (2012) uses human likeness to denote interchangeably both an entity’s
physical properties and how it is perceived. In research, however, the distinction is necessary.
Physical properties, for example, can be directly manipulated as an independent variable.
1.1.2 Stimulus range
We compiled a list of categories to summarize stimulus creation techniques. The list derives from
the stimuli appearing in publications of empirical research and descriptions of how they were
created (e.g., Mitchell et al., 2011b; Seyama & Nagayama, 2007). We started with six a priori
categories and added categories during the literature search when a paper’s stimuli did not fit in
any existing category. Saturation was reached at 10 categories. The categories encompass the
research reviewed, enabling its techniques to be easily classified, and reflect its theoretical and
methodological breadth. The 10 categories of techniques are listed below:
Distinct entities: Selecting images or videos of existing robots, androids, computer-animated
characters, humans, or other entities (e.g., Mathur et al., 2020). This technique is theory-
independent and can be used with both still and moving entities, such as characters from films,
video games, and virtual worlds.
Emotion manipulation: Distorting affective expressions (e.g., Qiao & Roger, 2011; Qiao, Eglin,
& Beck, 2011; Tinwell et al., 2014). This technique visually manipulates the emotional
expression of the face. It has been used mainly to test empathy-related theories.
Face distortion: Distorting facial features and proportions (e.g., Mäkäräinen et al., 2014). This
technique visually manipulates facial features or the relations among them until the face no longer
appears real. The emotional expression is not intentionally manipulated. This technique has been
used to test theories related to configural processing (e.g., MacDorman et al., 2009).

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Mismatch: Swapping facial features with those of another face that differs along one or more
dimensions—typically animacy, human likeness, and realism (e.g., Seyama & Nagayama, 2007).
This technique has been used to test theories related to perceptual mismatch (MacDorman &
Chattopadhyay, 2016).
Morphing: Varying the stimulus in a stepwise transition between a pair of images to create a
range of stimuli (e.g., MacDorman & Ishiguro, 2006). This technique has been used to transform
the stimulus gradually from one kind of entity to another, thus making it suitable for testing
category-related theories (e.g., Cheetham et al., 2015; Sasaki, Ihaya, & Yamada, 2017).
Motion manipulation: Distorting an animation’s biological motion (e.g., gait, Destephe et al.,
2014; Handzic & Reed, 2015; motion quality, Piwek, McKay, & Pollick, 2014; Thompson,
Trafton, & McKnight, 2011). This technique has been used to test whether the UV effect occurs
in motion perception.
Realism render: Varying how real the stimuli appear by representing them as cartoons or as
computer models with a reduced polygon count or simplified textures (e.g., McDonnell et al.,
2012; Muniady & Ali, 2020). This technique is theory-independent and relevant to practical
applications of visual design.
Real-life encounter: Presenting different embodied entities like robots, androids, and humans for
observation or interaction (e.g., Złotowski et al., 2015). This technique encompasses multiple
modalities and, thus, can be used to measure a holistic UV effect. It is also useful because a
physical object could be perceived and evaluated differently from its two-dimensional depiction
(Snow, Skiba, Coleman, & Berryhill, 2014). Moreover, this technique is ecologically valid.
Visuo-auditory mismatch: Replacing a human voice with a synthesized voice or vice versa in an
animation (e.g., Mitchell et al., 2011b; Stein & Ohler, 2018). Although typically motivated by
perceptual mismatch theories, this technique differs from the mismatch category because the
mismatch is crossmodal.
Voice distortion: Distorting natural human voices as auditory stimuli (e.g., Baird et al., 2018;
Kühne et al., 2020). This technique has been used to test whether the UV effect can occur solely
within audition.
1.1.3 Measurement
To assess the degree of human likeness (or related concepts), either single-scale measures or
indices consisting of multiple scales have been used (e.g., Burleigh, Schoenherr, & Lacroix,
2013; Ho & MacDorman, 2010, 2017). Experiments typically vary the stimulus systematically in
its degree of human similarity. Manipulations include distorting it (Mäkäräinen, Kätsyri, &
Takala, 2014) or controlling its morphing proportion between two images (Cheetham & Jäncke,
2013). Experiments may include a manipulation check, such as rating the stimulus on human
likeness. For computer-modeled stimuli only, Burleigh, Schoenherr, and Lacroix (2013) proposed
two objective properties, which they define as follows: texture resolution, the number of pixels
per unit of surface area, and polygon count, the number of polygons constituting a three-
dimensional model. However, human likeness and realism are two different constructs. Thus, the
results of a study measuring human likeness may not be comparable to the results of a study

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measuring realism. Research has not compared how changes in these independent variables or
others may influence affect measures differently.
1.2 The dependent variable
1.2.1 Construct
Mori (2012) represents the y-axis with the term shinwakan, a neologism he translates as affinity.
The y-axis had initially been translated as familiarity (Reichardt, 1978). Other proposed
constructs include interpersonal warmth (or likability) and reverse-scaled eeriness (Bartneck et
al., 2009b; Ho & MacDorman, 2010, 2017; Redstone, 2013). Eeriness and its synonym
creepiness correlate with aversive experiences like disgust, fear, and anxiety (Ho, MacDorman, &
Pramono, 2008).
1.2.2 Measurement
In experiments on the UV effect, the dependent variable is typically measured with single-scale
measures or indices composed of self-reported affective items. Semantic differential scales are
common. Semantically, some items like eerie, creepy, and uncanny are specific and, on face
value, capture the distinctive experiential quality of the UV effect (Ho & MacDorman, 2010;
Mangan, 2015; Palomäki et al., 2018; Redstone, 2013; Tinwell, Nabi, & Charlton, 2013). Other
items like pleasantness or likability are nonspecific. An entity could rate low on them without
being uncanny at all (e.g., items in Bartneck et al., 2009b; Ferrey, Burleigh, & Fenske, 2015;
Rosenthal–von der Pütten & Krämer, 2014; Yamada, Kawabe, & Ihaya, 2013).
Questionnaires that have been developed to evaluate robots in general have been repurposed to
measure the UV effect. Examples include the Godspeed indices (Bartneck et al., 2009b) and the
Robotic Social Attribution Scale (Carpinella, Wyman, Perez, & Stroessner, 2017). Ho and
MacDorman’s (2010, 2017) set of indices includes humanness, interpersonal warmth,
attractiveness, and eeriness. They developed the set to decorrelate these dimensions so they could
be plotted against each other on orthogonal axes.
Indirect measures may indicate a construct by measuring a different construct. For example, the
UV effect may correlate with trust behavior (Mathur & Reichling, 2016). For simplicity, we
categorize implicit measures as indirect measures. Implicit measures center on processes that are
automatic, effortless, fast, goal-independent, stimulus-driven, uncontrolled, or unintentional. For
example, response time and other performance measures of the UV effect typically are implicit
measures. Implicit measures counter self-presentational bias, that is, respondents’ attempts to
influence how others perceive them. Implicit measures may indicate the UV effect in otherwise
inaccessible populations, such as infants or nonhuman animals.
Apart from trust behavior, the UV effect has been measured by such indirect measures as
avoidance behavior (Matsuda et al., 2012), perceived responsiveness (Tinwell et al., 2013), and
cognitive conflict and categorization reaction time (RT, Cheetham & Jäncke, 2013).
1.2.3 Other constructs
Other constructs and their associated measures and theories include the following:

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Aesthetics: Items measuring aesthetic appeal (Sansoni, Wodehouse, McFayden, & Buis, 2015;
Schwind et al., 2018). These items conceptualize the UV effect as a lack of physical
attractiveness. Thus, they can serve as a practical tool for design (Hanson et al., 2005; Ho &
MacDorman, 2010, 2017). Research has used nonhuman (e.g., Schwind et al., 2018) as well as
human stimuli with the latter leveraging on theories of evolutionary aesthetics. These theories
frame the UV effect as resulting from a mechanism for avoiding mates with low fitness as
determined by the absence of physical markers of fertility, health, and youthfulness (MacDorman
et al., 2009; MacDorman & Ishiguro, 2006).
Animacy and experience: Items measuring perceived animacy (Looser & Wheatley, 2010),
responsiveness (Tinwell et al., 2014), and mind (Appel et al., 2016). These items relate to theories
about how the perceived presence or absence of these qualities elicits the UV effect. For example,
Gray and Wegner (2012) proposed that a machine having conscious experiences—or a human
being lacking them—would be perceived as uncanny; the authors’ creation techniques are broad:
android robot videos, text about a supercomputer, and a photo of a man.
Anomaly: Items measuring an entity’s perceived deviation from the norm. Anomaly items, such
as strange or weird, are associated with atypicality theories. These theories predict that the UV
effect is elicited by an entity whose features cause it to deviate strongly from its prototype
(Kätsyri et al., 2015; Strait et al., 2017). Anomalies are easily created in images, where features
can be moved, reflected, rotated, and scaled (e.g., Diel & MacDorman, 2021).
Disgust: Items measuring disgust, a predictor of the UV effect (Ho, MacDorman, & Pramono,
2008). These items relate to the theory that the UV effect results from an evolved mechanism for
pathogen avoidance (MacDorman & Entezari, 2015).
Distinctive experience: Items measuring the UV effect as the subjective experience of
uncanniness or eeriness, which may be correlated with fear, anxiety, and disgust (Bartneck et al.,
2009a; Ho, MacDorman, & Pramono, 2008). This research conceives of the UV effect as an
experience distinct from general psychological discomfort or anxiety. Gahrn-Andersen (2020)
and Mangan (2015) have related the phenomenological study of the uncanny to the theories of
Martin Heidegger and William James.
Familiarity: Items measuring the UV effect as feelings of unfamiliarity, based on Reichardt’s
(1978) translation of shinwakan as familiarity. Typically, in cognitive psychology, familiarity is
contrasted with novelty: 0% familiarity is 100% novelty. However, when inspecting the y-axis of
Mori’s (2012) graph, the familiar–novel contrast leads to contradiction. On this interpretation, the
bottom of the valley lies in negative familiarity, beyond 100% novelty, which cannot exist. One
finds a different interpretation in Freud’s (1919/2003) theory of the uncanny. To Freud, the
uncanny is not the perception of something novel or unfamiliar. Rather, it is the recollection of
something intimately familiar, perhaps from early childhood, that has long been estranged
through repression (MacDorman & Entezari, 2015; MacDorman & Ishiguro, 2006). Freud asserts
that repression transforms every emotional affect—including uncanniness—into anxiety (Angst).
General anxiety: Items measuring a state of anxiety or stress without relating it specifically to the
subjective experience of the uncanny. The items are associated with theories based on category
inhibition, cognitive conflict (Ferrey et al., 2015), and perceptual tension (Moore, 2012). Their

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use may reflect the assumption that the experiential quality of the UV effect is no more specific
than the psychological discomfort caused by cognitive dissonance or cognitive load.
Interpersonal warmth: Items measuring the primary dimension of social perception, interpersonal
warmth, which accounts for 53% of the variance in perceptions of social behaviors (Fiske,
Cuddy, & Glick, 2007; Fiske, Cuddy, Glick, & Xu, 2002). This dimension is measured with
positive affect items, like likable, pleasant, and friendly, which load on the same factor in factor
analyses (Bartneck et al., 2009a; Ho & MacDorman, 2010). The construct is intended to measure
how feelings about an entity change with its degree of human likeness. The dimension is roughly
synonymous with affinity, the y-axis of Mori’s (2012) graph, though as a construct warmth has
been more thoroughly investigated. The use of warmth items to measure the UV effect is
grounded in the assumption that warmth and uncanniness are inversely related. However, feelings
of coldness—the low end of the scale—differ from feelings of uncanniness. For example, we
might have warm feelings for the conductor (Tom Hanks) in The Polar Express (2004) while also
having uncanny feelings because of the way he is computer animated. Furthermore, the generality
of warmth items makes them susceptible to confounds. Stimulus evaluation could be influenced
by, for example, background, clothing, color, narrative and framing, verbal and nonverbal
behavior, interactivity, personality, relationships, and culture (Brink et al., 2019; Kennedy, 2014;
Łupkowski, Rybka, Dziedzic, & Włodarczyk, 2018; MacDorman, 2019; Shin, Kim, & Biocca,
2019). Thus, warmth items do not indicate the UV effect but a related construct.
Threat: Items measuring a negative emotional response to dead animals, ranked by the species’
similarity to living humans, motivated by theories that conceive of the UV effect as an evolved
threat-avoidance mechanism (Moosa & Ud-Dean, 2010; Palomäki et al.,2018; Rosenthal et al.,
2014). The entities could also appear threatening because of their ambiguity (McAndrew &
Koehnke, 2016).
Trust: Numerical indicators of trust, such as the amount of money invested while playing a game,
with a smaller investment indicating less trust. A decrease in trust could result from the UV effect
in perceiving android robots or avatars. Mathur and Reichling (2016) relate this measure of trust
to Hardin’s (2002) theory of encapsulated interest: We trust those whose interest encapsulates our
own. In their game, they raise the question of whether human players were really taking an
intentional stance toward the robot or merely acting as if they were.
2 Methods
The lack of consensus in the UV literature, both theoretical and methodological, should now be
evident. It motivates our meta-analysis, the first of its kind. We evaluate the effectiveness of
stimulus creation techniques as well as affect and indirect measures. Based on the results, we
propose empirically derived design principles for future research.
2.1 Inclusion criteria
The meta-analysis only included a study if it met the criteria below based on the information
given:

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Empirical study: The study contains the results of at least one data analysis conducted by its
authors.
Representative participants: The study uses healthy adults, children, or infants. Excluded were
studies restricted to a specific subgroup, such as people with autism spectrum disorder.
Relevant stimuli: The stimuli belong to at least one of the 10 creation techniques.
Adequate stimuli: The stimuli lack obvious confounds like noise created by editing images.
Affect or indirect measures: Affect measures include single-scale items or indices used to self-
report an affective appraisal of the stimulus. Indirect measures include everything else. Studies
with either or both were included.
Testing a UV hypothesis for statistical significance: The study has one or more hypotheses
designed to test the UV effect. For each hypothesis, a test statistic is applied to the collected data.
Studies with both significant and nonsignificant effects were included.
Appropriate variables: Testing for a change in an affect or indirect measure resulting from a
change in human likeness or a related variable (e.g., realism, zoomorphism). Thus, all studies
were experiments.
Effect size determinable: The study must give enough information to calculate an effect size and
its variance.
Figure 3. The flowchart depicts the process of study selection.

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2.2 Study search and selection
In March 2021, we searched on PubMed, Science.Gov, and the Web of Science for papers with
uncanny valley in their title, abstract, or keywords. After removing 33 duplicates, 488 studies
remained of which 155 included UV significance testing (see Data Availability). Although 98 met
other review criteria, only 72 had determinable effect sizes. These studies appeared in 56 papers
published from 2008 to 2021. Figure 3 summarizes the article selection process.
From its description, we placed each IV operationalization under the best-fitting stimulus creation
technique.
For DV operationalizations, single items were generally grouped separately. Nouns formed from
adjectives were grouped with those adjectives (e.g., eeriness with eerie). The item creepy and
semantic differential scales like creepy–friendly and creepy–pleasant were group as creepy*.
Affect measures were grouped separately from indirect measures. For example, the item
trustworthy was counted as an affect measure, separate from trust behavior, an indirect measure.
If a study used a negative variant of an often-used positive item, the item was grouped with the
positive variant (e.g., unpleasant with pleasant). Indices used in multiple studies were counted as
separate index items and marked with the suffix -i (e.g., those developed by Bartneck et al.,
2009b; Ho and MacDorman, 2010, 2017; Schwind et al., 2018).
We then recorded or calculated effect sizes and effect size variances, labeling each with its
corresponding IV and DV. If a study used more than one IV or DV operationalization, each effect
size was recorded or calculated.
2.3 Data analysis
A random-effects model was selected for the meta-analysis because study populations and
designs differed and affect and indirect measures were used in combination with different
stimulus creation techniques. A three-level model was used with effect nested by study. The
meta-regression for moderation analysis was performed using a mixed-effects model. The model
was fitted by restricted maximum-likelihood estimation.
Effect size is reported here as Hedges’ g. The effect size, its 95% confidence interval, and the
number of measures from which it was derived, k, are all reported. Effect size is interpreted with
small = 0.20, medium = 0.50, and large = 0.80 thresholds.
If three or more conditions were compared, such as robot, android, and human, two separate g’s
were calculated: one for the posited descent from the first peak in Mori’s graph to the base of the
valley and the second for the posited ascent from the base of the valley to the second peak. For
convenience, the descent is denoted as the UV’s nonhuman side and the ascent as the UV’s
human side.
The definition of an influential effect was adopted from Viechtbauer and Cheung (2010), as
explained in the results section.
Moderator variables for the independent variable were the creation technique. Moderator
variables for the dependent variable were (separately) the side of the valley, side × valence

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(positive or negative) × measure type (affect or indirect), affect measure, indirect measure, and
other construct. Finally, paper was used as a moderator variable.
2.3.1 Effect size calculation
The meta-analysis used the standardized mean difference and its variance. Hedges’ g was used to
correct for the positive bias of Cohen’s d in smaller studies,
  13
4df1, (1)
 13
4df12, (2)
where df indicates the degrees of freedom (Borenstein et al., 2011). If a study did not report g, it
was calculated from the means and standard deviations or by converting another reported
measure of effect size. For within-group studies, which were the majority, dav and vav were used,
av 12
1
212, (3)
av 1
2
2, (4)
where n is the number of participants (Lakens, 2013). This approach leads to slightly wider
confidence intervals than d for repeated measures. However, the calculation of drm requires the
correlation between means, which no study reported. For ANOVAs, η2 was first calculated:
η2df1
df1df2 (5)
Next, to calculate g, η2 was converted to d (Cohen, 1988):
  2η2
1η2 (6)
R2, Pearson’s r, and Cramér’s V were plugged into the same formula. For the t statistic, d was
calculated for between-groups studies by imputing r = 0.5 in the formula
  21
 (7)
3 Results
The 72 studies in the meta-analysis employed 10 different stimulus creation techniques and 53
different measures, 39 of which were affect measures and 14 of which were indirect measures.
In total, 61 studies included affect measures, and 23 included indirect measures. The studies
ranged in size from 10 to 1,311 participants with a median size of 64.5 and an interquartile range
of 34 to 203.5. Of the 249 measured effects, 85 involve the nonhuman side of the UV, 71 involve
the human side, and 93 involve both sides simultaneously.
The three-level meta-analysis model, including two outliers, revealed that the UV effect had a
large effect size, g = 0.95 [0.76, 1.14], p < .001, k = 249, Akaike information criterion (AIC) =

13
724.92, QE(248) = 10241.38, p < .001, QM(1) = 93.30, p < .001. Excluding the two outliers,
discussed below, increased the effect size, g = 1.01 [0.80, 1.22], p < .001, k = 247.
3.1 Three-level model
The meta-analysis often draws multiple effect sizes from the same paper and even from the same
study. Thus, the effect sizes are not statistically independent (Cheung, 2019). To address this, we
investigated different three-level models.
The model with the lowest estimated prediction error, excluding outliers, has paper as its higher-
order grouping variable and effect as its nested lower-order grouping variable, QE(246) =
9725.21, p < .001, QM(1) = 88.53, p < .001. The model has lower estimated prediction error
(paper/effect: AIC = 675.17) than the other three-level models (study/effect: AIC = 683.05,
technique/effect: AIC = 714.85, measure/effect: AIC = 715.20). Its prediction error is significantly
lower than two-level models (effect: AIC = 717.57, p < .001, paper: AIC = 4915.67, p < .001). Of
the total variance, 38.53% is between-paper heterogeneity, 60.34% is within-paper heterogeneity
(total I² = 98.87), and 1.13% is sampling error.
3.2 Bias
Figure 4(a) shows a funnel plot of effect sizes against their standard errors for meta-analysis.
Since standard error is inversely proportional to sample size, larger studies appear at the top and
smaller studies at the bottom. In the absence of bias, sampling error should distribute effect sizes
randomly but symmetrically about their weighted mean. In the funnel plot, however, the effect
sizes tend to increase with their standard errors. A regression test with standard error as the
predictor variable and Hedges’ g as the outcome variable indicated significant funnel plot
asymmetry (z = 6.72, p < .001, k = 249).
Funnel plot asymmetry could result from publication bias because the meta-analysis relied on
published data only. In general, studies reporting a significant effect are more likely to be
published. If a true effect exists, a smaller study will require a larger effect size to reach
significance. Moreover, given that large studies constitute a major commitment of resources, they
are more likely to be published even if their effects are nonsignificant.
One approach to addressing bias is to limit the meta-analysis to larger studies and then to check
whether bias is still present and whether the effect size is still large enough to be of substantive
importance (Borenstein et al., 2009). We tried a version of this approach by excluding the effects
with the largest standard errors and retesting for funnel plot asymmetry. After excluding 66
effects—that is, 27% of the total, as shown in Figure 4(b)—funnel plot asymmetry for the
remaining effects became nonsignificant (z = 1.95, p = .051, k = 183). The effect size, however,
was reduced 28%, g = 0.68 [0.51, 0.85], k = 183. Though smaller, it remains of substantive
importance.

14
Figure 4. The funnel plot graphs effect sizes from the meta-analysis against their standard
errors: (a) all standard errors; (b) the lowest 73% of standard errors. Influential effects are
indicated in red.
Figure 5. The p-curve for the meta-analysis’s 249 effects.
Bias was next assessed by p-curve analysis. A plot of p values against percentage of effects
should be flat if there is no effect and right skewed if there is one. A left skew indicates bias, a
publication environment in which obtaining significance at the .05 level is incentivized, but lower
p values are unnecessary. This could result from publication bias or from p-hacking, mining the
data for patterns and then failing to control for multiplicity in reporting significance. Of 249
effects, p ≤ .05 for 213 (86%), and p ≤ .025 for 207 (83%). The right-skewness test, pbinomial <
.001, zfull = –73.80, pfull < .001, zhalf = –72.50, phalf < .001, was significant, which indicates a true
effect (Figure 5). The flatness test was nonsignificant, pbinomial > .999, zfull = 65.35, pfull > .999, zhalf

15
= 69.70, phalf > .999; thus, the test did not indicate insufficient power or the absence of a true
effect. The power estimate is 0.99 [0.99, 1.00]. The tests were repeated, with similar results, for
only the 66 effects with the largest standard errors. Thus, p-curve analysis supports the conclusion
that the effect is true. It is not simply the result of publication bias or p-hacking.
3.3 Influential effects
Viechtbauer and Cheung (2010) proposed that an effect is influential if it meets one of the
following four criteria:
DFFITS3
, (8)
where p is the number of model coefficients and k the number of effects, the Cook’s distance,
χ50%
2, (9)
where p is the model’s degrees of freedom, indicating the deletion if the i’th effect decreases the
Mahalanobis distance between effects,
 3
, and any (10)
DFBETA 1. (11)
Figure 6. DFFITS and Cook’s D for the effects in the meta-analysis, sorted from lowest to
highest standard error. Influential effects are indicated in red.
Figure 7. Creation technique is the moderator variable in the meta-regression model. For each
of its values, Hedges’ g, the 95% confidence interval, and number of effects (k) are listed. The
position of the blue square depicts the effect size, and its relative size depicts the precision. The
width of the diamond depicts the confidence interval of the summary effect size.

16
Two effects were identified as influential by the first two criteria (Figure 6), and both pertained to
the UV’s nonhuman side: Rosenthal et al.’s (2014) unfamiliar-i, g = –2.95, DFFITS = –0.224, D
= 0.047, hat = 0.004, DFBETA = –0.224, and Wang et al.’s (2020) alive, g = –2.77, DFFITS = –
0.205, D = 0.040, hat =0.004, DFBETA = –0.205. They were treated as outliers for reasons
discussed below and included in analyses selectively.
3.4 Independent variable operationalizations
3.4.1 Moderator: Creation techniques
Moderation analysis was performed, excluding outliers, using a mixed-effects meta-regression
model with effect as the random variable and creation technique as the moderator variable, AIC =
701.33, QE(237) = 8984.08, p < .001, τ² = 0.91, I² = 98.62, QM(10) = 272.53, p < .001. Face
distortion produced the largest effect size, followed by distinct entities, realism render, and
morphing (Figure 7).
Distinct entities studies typically used stimuli that could have confounding effects (e.g., body
language, facial expressions, lighting, viewing perspective). To reduce their risk, a few studies
applied standards for stimulus selection—for example, full face shown in frontal or three-fourths
aspect, resolution sufficient to generate a final image three inches in height at 100 dpi, and no
other body parts visible (Brink, Gray, & Wellman, 2017; Mathur & Reichling, 2016). When only
distinct entities studies with standardized stimuli were considered, three in total, g fell to 0.82 [–
0.12, 1.77], k = 4, and the effect became nonsignificant, p = .089.
Four studies used nonhuman animal stimuli, AIC = 32.95, QE(17) = 373.46, p < .001, QM(1) =
32.95, p < .001 (MacDorman & Chattopadhyay, 2017; Schwind et al., 2018; Yamada et al.,
2013). Their 18 effects were all significant, g = 1.94 [1.28, 2.60], k = 18. Stimulus
operationalization techniques for animal stimuli were comparable with those for human stimuli,
including distinct entities (Rativa et al., 2020; Takahashi et al., 2015), emotion manipulation, face
distortion, realism render (Chattopadhyay & MacDorman, 2016; Schwind et al., 2018), and
morphing (Yamada et al., 2013).
Figure 8. Side of the uncanny valley is the moderator variable in the meta-regression model.

17
3.5 Dependent variable operationalizations
3.5.1 Moderator: Side of the uncanny valley, valence, and type of measure
Moderation analysis was performed, including outliers, with effect as the random variable and
side of the valley as the moderator variable, AIC = 731.92, QE(246) = 9942.04, p < .001, τ² =
1.00, I² = 98.80, QM(3) = 239.92, p < .001. If possible, an effect size was calculated for each side
of the uncanny valley. However, this was not possible for 37% of effect sizes, usually because the
means and standard deviations were not reported. In these cases, a combined effect size for both
sides of the valley was calculated (e.g., based on an F statistic). For the human side, g = 1.34
[1.10, 1.57], p < .001, and k = 71, for the nonhuman side, g = 0.64 [0.43, 0.86], p < .001, and k =
85, and for both sides, g = 0.98 [0.77, 1.19], p <.001, and k = 93. Thus, the effect size for the
human side was more than double that of the nonhuman side.
To investigate this disparity, we repeated the analysis with side × valence (positive or negative) ×
measure type (affect or indirect) as the moderator variable (Figure 8). The combined value human
positive affect had the largest affect size, g = 1.69 [1.34, 2.03], p < .001, k = 32 and nonhuman
positive affect had the smallest. Thus, among all measures, positive affect measures were the most
effective at measuring the human side of the valley and the least effective at measuring the
nonhuman side. A Wald-type test revealed this difference in effectiveness was significant,
QM(12) = 276.73, p < .001. For the human side, affect measures were more effective than
indirect measures. For the nonhuman side, indirect measures were more effective than affect
measures, and negative measures were more effective than positive ones.
3.5.2 Moderator: Affect measures
Moderation analysis was performed, excluding outliers, with effect as the random variable and
affect measure as the moderator variable, AIC = 537.05, QE(159) = 4544.64, p < .001, τ² = 0.92,
I² = 98.51, QM(38) = 247.70, p < .001 (Figure 9). Indices producing effects that were larger than
average include threatening-i (threatening, eerie, uncanny, dominant, harmless), likable-i
(pleasant, likable, attractive, familiar, natural, intelligent), aesthetics-i (ugly–beautiful,
unaesthetic–aesthetic), familiarity-i (uncanny–familiar, freaky–numbing), and eeriness-i (dull–
freaky, predictable–eerie, plain–weird, ordinary–supernatural, boring–shocking, uninspiring–
spine-tingling, predictable–thrilling, bland–uncanny, unemotional–hair-raising). Individual items
include reassuring, threatening, believable, appealing, acceptable, alive, and eerie. However,
when the two outliers are included, alive falls from the 12th highest effect size, g = 1.19 [0.33,
2.06], p = .007, k = 5, to the 29th, g = 0.55 [–0.27, 1.37], p = .191, k = 6, and is no longer
significant. The other outlier, unfamiliar-i (strange, unfamiliar) appears last, g = –2.95 [–4.94, –
0.95], p = .004, k = 1.
3.5.3 Indices and multiple scale analyses
A variety of terms have been used to measure different constructs underlying the UV effect. The
relations among the terms can give insight into the UV effect’s experiential quality. In studies
with several terms, we investigated their intercorrelations to determine whether they reflect the
UV effect or instead a related construct. Table A1 in the Appendix lists the interscale correlations
observed in the reviewed research.

18
Figure 9. Affect measure is the moderator variable in the meta-regression model. Creepy*
combines the item creepy with scales including the term, such as creepy–pleasant and creepy–
friendly.
As a measure of reliability, 15 studies in the meta-analysis reported the Cronbach’s α of the
indices used. Ho and MacDorman’s (2010, 2017) eeriness and warmth indices and their
derivations were generally reliable. Distinctive experience terms (e.g., creepy, eerie, and
uncanny) tended to load on the same factor (e.g., Destephe et al., 2015; Lischetzke et al. 2017). In
a principal component analysis (PCA), the items uncanny and eerie loaded on the same
component as threat-related items, and the items strange and unfamiliar as anxiety-related items
(Rosenthal–von der Pütten & Krämer, 2014; Ho, MacDorman, & Pramono, 2008, found fear and
disgust to be stronger predictors of eerie and creepy than anxiety). In a similar vein, removing
strange from an index consisting of eerie, unsettling, and strange improved its reliability (Kätsyri,
Mäkäräinen, & Takala, 2017). This indicates uncanniness and strangeness may be different
constructs.

19
Finally, likable, friendly, pleasant, and other warmth items typically comprise reliable indices
(e.g., Kätsyri, Mäkäräinen, & Takala, 2017; Rosenthal–von der Pütten & Krämer, 2014; Tung,
2016), which indicates an interpersonal warmth construct for the tested stimuli (e.g., Bartneck et
al., 2009a).
Figure 10. Indirect measure is the moderator variable in the meta-regression model.
3.5.4 Moderator: Indirect measures
Moderation analysis was performed, excluding outliers, with effect as the random variable and
indirect measure as the moderator variable (Figure 10). Dislike frequency, which indicates the
number of times disliked, had the largest effect size (Strait et al., 2019), followed by
categorization reaction time (Carr et al., 2017; Cheetham & Jäncke, 2013; MacDorman &
Chattopadhyay, 2017; Wang & Rochat, 2017; Yamada et al., 2013), like frequency (Strait et al.,
2019), avoidance behavior attributions to uncanniness (Perez et al., 2020), viewing duration
(Strait et al., 2015, 2019), preference choice in a two-alternative forced-choice categorization task
(Feng et al., 2018; Prakash & Rogers, 2015), and preferential looking, that is, preferring to view
one stimulus more than another (Matsuda et al., 2015; Nitta & Hashiya, 2021).
Nonsignificant effect sizes include lie detection, that is, frequency of rating a statement as a lie
(McDonnell & Breidt, 2010), cognitive conflict, operationalized as number of reversals of
direction when moving a stimulus with a mouse pointer towards one of two categories (Weis &
Wiese, 2017), trust behavior, specifically the amount of money entrusted with an entity in an
investment game (Mathur & Reichling, 2016), encounter duration, that is, viewing duration until
the participant terminates the encounter (Perez et al., 2020), termination frequency, measured by
the number of times terminated (Perez et al., 2020; Strait et al., 2015, 2017, 2019), information
processing about an entity, as indicated by the number of personality judgments made (Shin,
Kim, & Biocca, 2019), and ABX task, which entails visual same–different discriminations
(Cheetham et al., 2014).

20
Figure 11. Construct is the moderator variable in the meta-regression model.
3.6 Other constructs
After grouping measures by other UV construct, moderation analysis was performed, excluding
outliers, with effect as the random variable and other construct as the moderator variable, AIC =
386.28, QE(122) = 2999.63, p < .001, τ² = 1.02, I² = 98.29, QM(10) = 119.67, p < .001 (Figure
11). Animacy and experience had the largest effect size, g = 1.26 [0.44, 2.09], p = .003, k = 6.
However, if outliers are included, this construct falls from first to eighth and becomes
nonsignificant, g = 0.70 [–0.10, 1.51], p = .088, k = 7. Other constructs with significant effects, in
decreasing order of effect size, were aesthetics, interpersonal warmth, distinctive experience,
threat, trust, anomaly, and disgust. General anxiety and familiarity had nonsignificant effects.
3.7 Papers
For reference, a moderation analysis was performed, excluding outliers, with effect as the random
variable and paper as the moderator variable, AIC = 585.95, QE(191) = 5058.35, p < .001, τ² =
0.61, I² = 98.05, QM(56) = 552.95, p < .001 (Figure 12).
3.8 Data availability
The meta-analysis was performed in the R statistical computing environment with the metafor
package. The p-curve analysis and variance distribution analysis of the three-level model were
performed with the dmetar package. The remaining R packages were devtools, forestplot,
ggplot2, and readxl. The dataset, R script, and other supplementary materials are available at
https://doi.org/10.17605/osf.io/57sme.

21
Figure 12. Paper is the moderator variable in the meta-regression model.

22
4 Discussion
4.1 Independent variable operationalizations
Among all the stimulus creation techniques, face distortion produced the largest effect size,
followed by distinct entities, realism render, morphing, voice distortion, and motion
manipulation. Techniques producing a nonsignificant effect include mismatch, visuo-auditory
mismatch, emotion manipulation, and real-life encounter, though real-life encounter was based
on only one paper. Nonhuman animal stimuli performed well. Our evaluation of stimulus creation
techniques is summarized in Table A2 of the Appendix.
Face distortion was only tested in four of the papers reviewed (Feng et al., 2018; MacDorman et
al., 2009; Mäkäräinen et al., 2014; Schwind et al., 2018). Nevertheless, it is a promising
technique to explore configural processing theories (Diel & MacDorman, 2021).
Distinct entities were used in 46% of significance tests (114 out of 249), more than any other
technique. This creation technique has greater ecological validity than all techniques except—at
least for robots—real-life encounter. However, stimuli in these studies typically varied in body
language, facial expression, familiarity, gaze direction, lighting, perspective, and other aspects.
These potential confounding variables indicate a lack of experimental control, which could limit
the generalizability of the results (Kätsyri, Förger, Mäkäräinen, & Takala, 2015; Kätsyri, de
Gelder, & Takala, 2019). This interpretation aligns with our results. When the moderation
analysis was limited to studies using standardized stimuli, distinct entities produced a
nonsignificant effect.
Although morphing produced a large effect size in the meta-analysis, it was nonsignificant for 8
out of 44 effects. Nonsignificance may stem from the choice of endpoint stimuli. Studies that did
not find a UV effect used endpoint stimuli with the same shape, such as a human face and a
matching avatar face (Cheetham et al., 2015; Kätsyri, de Gelder, & Takala, 2019; the same issue
arises for realism render, MacDorman & Chattopadhyay, 2016). By contrast, studies that did find
a UV effect used morphologically different endpoint stimuli to produce a robot-to-human,
animal-to-human, or cartoon-to-real transition (Ferrey et al., 2015; Lischetzke et al., 2017;
Palomäki et al., 2018; Sasaki, Ihaya, & Yamada, 2017).
Creating stimuli from insufficiently distinct endpoint images may result in a morphing sequence
with too narrow a range in human likeness to include the uncanny valley part of the graph. For
example, although animals and robots have facial proportions that are atypical for humans, they
are not judged by human standards. Morphing them with human faces may elicit human-specific
processing, heightening sensitivity to those features that still deviate from human proportions,
thus eliciting the UV effect. This effect could not occur if the facial proportions of the low human
likeness endpoint stimuli were already human (e.g., human avatars). Thus, it is possible that, for
morphing stimuli to elicit a UV effect reliably, they must distort an entity’s configural pattern,
which would support theories predicting the UV effect results from configural processing
(Chattopadhyay & MacDorman, 2016; Diel & MacDorman, 2021; Kätsyri, 2018).
Alternatively, the large effect sizes for endpoint stimuli that differ greatly in their morphology
may be an unintended consequence of the creation technique. Endpoint stimuli like robots and

23
dolls tend to be attractive because they are the product of design. Human beings, though not
designed, tend to find each other attractive because their faces and bodies co-evolved with their
perceptual systems. In this context, attractiveness serves a purpose: It supports mate bonds and
parental bonds (see Kozak, Head, Lackey, & Boughman, 2013; Wyman, Charlton, Locatelli, &
Reby, 2011). However, intermediate stimuli in a morphing sequence neither evolved nor were
designed to be perceived as anything. This arbitrariness could heighten their uncanniness.
We advise researchers to avoid using similar endpoint images when creating stimuli through
morphing, or to use such techniques as morphing different regions of the face in different
morphing steps (Seyama & Nagayama, 2007). However, it is also important to avoid creating
strange or ghostly artifacts that could appear eerie for reasons other than their being intermediate
in human likeness (discussed in MacDorman & Chattopadhyay, 2016). The effect of endpoint
stimulus choice on the UV effect is a topic for investigation.
In their review, Wang, Lilienfeld, and Rochat (2015) found evidence against the UV effect comes
from studies using distinct entities, while evidence for the UV effect comes from studies using
morphing. The reason is perhaps that Wang and colleagues cited studies our analysis excluded for
not using a test statistic (Hanson et al., 2005) or for having image noise (e.g., one face with two
sets of hair, Seyama & Nagayama, 2007). In addition, several distinct entities studies with
supportive results were published after their review (Brink, Gray, & Wellman, 2017; Jung & Cho,
2018; Kätsyri, de Gelder, & Takala, 2019; Mathur & Reichling, 2016; Mathur et al., 2020;
Palomäki et al., 2018; Strait et al., 2017).
Finally, Wang, Lilienfeld, and Rochat (2015) criticizes using face distortion as an independent
variable because face distortion differs from human likeness. However, our review found face
distortion can elicit UV-specific subjective experiences (e.g., Mäkäräinen et al., 2014). Moreover,
our meta-analysis found a significant UV effect in perceiving animal stimuli (e.g., Löffler et al.,
2020; Schwind et al., 2017, 2018). Thus, human likeness alone cannot predict the range of
observed UV effects. A more encompassing DV conceptualization, like norm deviation, would
predict a broader range of UV effects. However, norm deviation is not necessarily uncanny.
Sometimes it does harm aesthetics but rather improves it (e.g., supernormal stimuli, Diel &
MacDorman, 2021).
4.2 Dependent variable operationalizations
The effect size of the uncanny valley’s human side was more than double that of its nonhuman
side. This difference may seem to reflect Mori’s graph because the second peak is higher than the
first. However, we also noted that, among all measures, positive affect produced the largest effect
sizes for the human side and the smallest for the nonhuman side. Thus, a more plausible
explanation is that positive affect is a poor measure of the UV effect.
Setting aside the miraculous and the extraterrestrial, people tend to perceive human beings as
superior to nonhuman entities. This applies to stimuli appearing in UV experiments to date, such
as robots, animals, and dolls. Perceived limitations in present-day human artifacts or other species
reinforce our ingroup bias, rooted in our common identity, to privilege the human (MacDorman
& Entezari, 2015; Mitchell et al., 2011a). Humans are often seen as more appealing, attractive,
friendly, likable, pleasant, reassuring, and warm than nonhuman alternatives, not to mention more

24
cultured, intelligent, and sociable. We can immediately see why positive affect measures are poor
for measuring the UV effect because, despite how uncanny an android may appear, it will still
appear more lifelike and less unfamiliar than a mechanical-looking robot of a novel design. Thus,
it is important to focus on effective measures for the uncanny valley’s nonhuman side: negative
affect measures and positive indirect measures.
The effectiveness of negative affect measures like eerie, creepy, threatening, and disgusting align
with the view that the UV effect is characterized by a distinctive experience of uncanniness rather
than an overall decrease in positive affect (e.g., Ho, MacDorman, & Pramono, 2008; Mangan,
2015; Redstone, 2013). This negative experience may still reduce positive affect, though
indirectly (Patrick & Lavoro, 1997).
The most frequently used item was eerie (e.g., Ho & MacDorman, 2010, 2017; Kätsyri, de
Gelder, & Takala, 2019). Other negative items included creepy, disgusting, repulsive, strange,
threatening, and weird. Concordantly, positive items with the largest effect sizes were
nonspecific, such as interpersonal warmth items (likable, pleasant) or familiar (e.g., MacDorman
& Ishiguro, 2006). Despite a correlation between the UV effect and feelings of disgust (e.g., Ho,
MacDorman, & Pramono, 2008; MacDorman & Entezari, 2015), the item repulsive was
nonsignificant.
Among indirect measures, dislike frequency produced the largest effect size, followed by
categorization RT, like frequency, avoidance, viewing duration, preference choice, and
preferential looking. Indirect measures, such as performance measures, are not without their
limitations. Although some research uses performance measures to quantify a construct related to,
but distinct from, the UV effect, other research claims they measure the UV effect itself (e.g.,
Lewkowicz & Ghazanfar, 2012; Matsuda et al., 2015). Measures like preferential looking and
preference choice reflect general avoidance behavior, which could be elicited by the UV effect or
by extraneous factors that must be controlled for, such as an ugly appearance or inhospitable
disposition. Furthermore, most studies measuring performance omitted affect. Those that
measured it tended to find a UV effect for affect but not for performance (Strait et al., 2015;
Strait, Urry, & Muentener, 2019; for the opposite case, see Wang & Rochat, 2017).
These findings point to broader issues with measurement in UV research: First, many studies do
not measure affect, but they should endeavor to do so insofar as it is possible. It is better to avoid
relying solely on task performance measures (e.g., categorization RT, Cheetham, Suter, & Jäncke,
2011; Cheetham et al., 2013; Cheetham, Suter, & Jäncke, 2014; Chen, Russell, & Nakayama,
2010; Saygin, Chaminade, Ishiguro, Driver, & Frith, 2012; avoidance or preference, Lewkowicz
& Ghazanfar, 2012; Matsuda et al., 2012; Steckenfinger & Ghazanfar, 2009). The reason is that
we cannot infer affect and its influence on motivation solely from nonaffective behavior, though
we can code it from displays of emotion. For example, in a study that used termination frequency
to measure the UV effect, “the stimulus was boring” had a larger effect size than “the stimulus
was unnerving” (Strait et al., 2015; Strait, Urry, & Muentener, 2019). However, boring has never
been considered the dependent variable in Mori’s graph. In addition, task performance measures
can diverge from affect measures (MacDorman & Chattopadhyay, 2016, 2017; Mathur et al.,
2020). Research should aim to validate performance measures by testing their specificity for the
UV effect.

25
Second, although likability, pleasantness, and other nonspecific items used to measure overall
affect tend to correlate with UV-specific items, they do not capture the experiential quality of the
UV effect. Thus, unrelated factors could cause them to increase or decrease. This makes
nonspecific items more susceptible to confounding variables. Perceptual variables that can
influence stimulus evaluation include attractiveness (Ho & MacDorman, 2010, 2017; Principe &
Langlois, 2011), atypical (Kätsyri et al., 2015; Strait et al., 2017), disgusting (Curtis, de Barra, &
Aunger, 2011), or misaligned features (MacDorman & Chattopadhyay, 2016), background
(Łupkowski et al., 2019), color (Kennedy, 2014; Valdez & Mehrabian, 1994), morphing artifacts
(MacDorman & Chattopadhyay, 2016), realism (McDonnell et al., 2012), and size (Cesarei &
Codispoti, 2006). These variables tend to be automatic and stimulus-driven. Perceptual-cognitive
variables include categorization difficulty (Cheetham et al., 2013; Yamada et al., 2013),
expectation violation (Saygin et al., 2012), frequency (Burleigh & Schoenherr, 2015; Moreland &
Zajonc, 1982), inhibitory devaluation (Ferrey, Burleigh, & Fenske, 2015; Weis & Wiese, 2017),
and multimodal mismatch (Mitchell et al., 2011b; Tinwell et al., 2015). Social variables include
animacy (Koldewyn, Hanus, & Balas, 2014; Mäkäräinen et al., 2014), context (Jung & Cho,
2018), facial expressions (Paulus & Wentura, 2015; Tinwell et al., 2011), mind (Gray & Wegner,
2012), narrative structure (MacDorman, 2019), outgroup membership (Hugenberg, 2005), and
perceived warmth or competence (MacDorman, 2019). Thus, studies should include UV-specific
measures to mitigate potential confounds.
Third, even when UV-specific measures are used, they can be influenced by the flow of the
interaction and its narrative structure (Dai & MacDorman, 2018). Thus, it may be necessary to
test for the UV effect before the interaction begins.
Fourth, the UV effect is correlated with fear, anxiety, and disgust (Ho, MacDorman, & Pramono,
2008). Thus, a UV measure should be able to discriminate UV stimuli from non-UV stimuli that
elicit similar emotions. However, discriminant validity has not yet been demonstrated for a UV
measure.
Fifth, regardless of the strength of a change in affect, at least three stimulus conditions are
necessary to produce measurements that could fit a U-shaped curve—the valley part of Mori’s
graph. Even if those measurements fit, a dip in a measure like interpersonal warmth could occur
for a myriad of reasons other than the UV effect. Thus, experimental control is vital.
Sixth, what eeriness is and which situations elicit it has not been specified precisely. Redstone
(2013) proposed that eeriness is elicited when the ontological nature of a stimulus is unclear.
Langer and König (2018) differentiate between eeriness (which they assert is a fear-related
response to humanoid entities) and creepiness (an anxiety-related response to novel or
unpredictable people or situations). However, these claims are untested. In general, UV research
lacks a common definition and conceptualization of the UV effect.
4.3 Limitations
4.3.1 Study exclusion
This meta-analysis excluded a wide range of impactful UV studies that were never intended to
replicate a UV curve. For example, Gray and Wegner (2012) found the UV effect was elicited by

26
a conscious machine or the philosophers’ zombie (a person lacking conscious experience). Their
findings were replicated by Appel and colleagues (2020). Schein and Gray (2015) found that,
among facial features, the UV effect was especially sensitive to the manipulation of the eyes. The
review also excluded specific subgroups and nonhuman primates. For example, Steckenfinger
and Ghazanfar (2009) found a UV effect in macaque monkeys. The meta-analysis also excluded
studies on the neurophysiological correlates of the perception of humanlike appearance or
behavior, which shed light on the neural mechanisms underlying the UV effect (e.g., Saygin et
al., 2011; Urgen et al., 2018).
The meta-analysis excluded interaction effects for simplicity. However, these effects have
elucidated the UV effect. For example, Green and colleagues (2008) found an interaction between
the degree of face distortion and realism render by showing that sensitivity to acceptable facial
proportions increased as the stimulus appeared more human. Similarly, Mäkäräinen and
colleagues (2014) showed that the strangeness of faces with exaggerated expressions increased as
faces were rendered more realistically. Both studies indicate realism increases the perceiver’s
sensitivity to human features. Thus, deviations from norms are more likely to be noticed and
perceived as uncanny in realistic representations. Sensitivity increases with realism logistically
(S-shaped curve), not linearly, indicating a perceptual magnet effect (Chattopadhyay &
MacDorman, 2016) like the one found for animacy (Looser and Wheatley, 2010). In a similar
vein, Deska and colleagues (2017) found that the perception of a mind occurs when a face
appears nearly human and is processed configurally (cf. Gray & Wegner, 2012; Tinwell et al.,
2013).
Smaller studies, which require a larger effect size to obtain significance, tended to have larger
effect sizes in our meta-analysis. Specifically, the average effect size of smaller studies, those in
the quartile with the largest standard errors, was more than double that of the other three quartiles.
Typically, inflated effect sizes in smaller studies are explained by publication bias or p-hacking.
Publication bias results from unpublished or unreported nonsignificant effects in a meta-analysis,
and p-hacking is the failure to control for multiplicity in significance testing. However, p-curve
analysis found no signs of publication bias or p-hacking.
Twenty-six of 98 studies that met selection criteria, including significance testing, were excluded
from the meta-analysis because they provided insufficient information to calculate effect sizes.
This issue arose mainly for nonsignificant effect sizes. Nevertheless, the field has shown interest
in nonsignificant and contrary effects, and papers reporting them have been well-cited (e.g.,
Cheetham, Suter, & Jäncke, 2014; Thompson, Trafton, & McKnight, 2011). Because this paper
focuses on comparing methodologies, bias affecting relative comparisons between effect sizes is
more worrisome than bias affecting their absolute magnitude.
4.3.2 Diverse methodologies
The diversity of UV methodologies impeded the meta-analysis. The volume of IV–DV
combinations complicated the interpretation of effect sizes for creation techniques and for
measures, especially for IV–DV combinations used in only a few studies. Precision in meta-
regression requires having enough combinations in each cell. At least five is one rule of thumb
(Borenstein et al., 2009). However, three of 10 techniques, 23 of 39 affect measures, and 12 of 14
indirect measures were used in fewer than five studies. The variety of experimental designs and

27
other study-specific variables also complicates interpretation of the results. To draw conclusions
about techniques and methods simultaneously requires enough significance tests or effect sizes to
make comparisons (Lay, Brace, Pike, & Pollick, 2016). Future research could give priority to the
validation of rarely used methods.
5 Conclusion
This is the first meta-analysis on the UV effect. We used meta-regression to evaluate the methods
used to operationalize the axes of Mori’s graph. Our findings provide a methodological
foundation for UV research. After discussing the conceptual foundations of the uncanny valley,
we have presented successful research methodologies and raised methodological concerns.
5.1 Recommendations
We end by proposing the following design principles for stimulus creation techniques and
measures in UV research:
Items that measure the UV experience as a distinct experience of uncanniness, such as uncanny
and eerie, or of strangeness, such as weird or strange, are preferred to nonspecific items. They
also have face validity. In this vein, negative items are preferred to positive ones. Negative items
can always be reverse scaled to plot the valley.
Affect or preference measures are necessary to assess the UV effect. Although indirect measures
may complement them, a study should not rely solely on indirect measures, if possible. The
validity of performance measures warrants further investigation.
The stimulus creation techniques producing the largest effect sizes were face distortion, distinct
entities, realism render, and morphing.
A drawback of morphing is that, if the endpoint images are too similar, the x-axis may not include
the uncanny valley. Morphing that disrupts the configural pattern may produce a larger effect;
however, it should avoid creating visual artifacts from the morphing process. How best to
approach morphing is a topic for future research.
Useful stimulus creation techniques include distorting facial features, rendering at different
realism levels, and using different emotional expressions. Their choice depends on theoretical
considerations and the research question. Further investigation is needed on realism rendering
and how it influences UV-specific negative measures compared with nonspecific positive
measures.
When using distinct entities, researchers should apply standards for stimulus selection (e.g.,
similar size, perspective, facial expression, and lighting). The effect of stimulus standardization
on the UV effect also warrants investigation.

28
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A. APPENDIX
Table A1. Indices and Cronbach’s α’s of UV studies.
Authors (year)
[study no.]
Indices: separate scales
UV effect
significance?
Cronbach’s α
per condition
Stimulus
creation
technique
Bartneck et al.
(2009a)
Likability: awful–nice,
unfriendly–friendly, unkind–
kind, and unpleasant–
pleasant
No
.92, .88, .84
Real-life
encounter
Destephe et al.
(2015)
Eeriness: eerie–reassuring,
freaky–numbing,
supernatural–ordinary, spine-
tingling–uninspiring,
thrilling–boring, mortal–
predictable, uncanny–bland,
and hair-raising–unemotional
Yes
.85
Motion
manipulation
Ho &
MacDorman
(2017)
Eeriness: dull–freaky,
predictable–eerie, plain–
weird, ordinary–supernatural,
boring–shocking,
uninspiring–spine-tingling,
predictable–thrilling, bland–
uncanny, and unemotional–
hair-raising
Yes
.86
Distinct
entities
Ho &
MacDorman
(2010)
Eeriness: reassuring–eerie,
numbing–freaky, ordinary–
supernatural, and
uninspiring–spine-tingling
Yes
.74
Distinct
entities
Warmth: cold-hearted–
warm-hearted, hostile–
friendly, spiteful–well-
intentioned, ill-tempered–
good-natured, and grumpy–
cheerful
Yes
.88
Kätsyri,
Mäkäräinen, &
Takala (2017)
Likable: likable, aesthetic,
and pleasant
No
.90
Distinct
entities
Eerie: eerie and unsettling
No
.70

43
Eerie: eerie, unsettling, and
strange
No
.64
Lischetzke et
al. (2017)
Index: creepy, eerie, and
uncanny
Yes
.92
Morphing
MacDorman &
Chattopadhyay
(2016)
Eeriness: ordinary–creepy,
plain–weird, and
predictable–eerie
No
N.A.
Realism render
Warmth: cold-hearted–
warm-hearted, hostile–
friendly, and grumpy–
cheerful
No
N.A.
Mitchell et al.
(2011b)
Eeriness (see Ho &
MacDorman, 2010)
Yes
.70
Visuo-auditory
mismatch
Warmth (see Ho &
MacDorman, 2010)
Yes
.88
Rosenthal–von
der Pütten &
Krämer (2014)
Threatening: threatening,
eerie, uncanny, dominant,
and harmless
Maybe
.89
Distinct
entities
Likable: pleasant, likable,
attractive, familiar, natural,
and intelligent
Maybe
.83
Submissive: incompetent,
weak, and submissive
No
.66
Unfamiliar: strange and
unfamiliar
No
.67
Schwind et al.
(2018)
Familiarity: uncanny–
familiar and freaky–numbing
Yes
N.A.
Distinct
entities (cats)
Aesthetics: ugly–beautiful
and unaesthetic–aesthetic
Yes
N.A.
Shin, Kim, &
Biocca (2019)
Eeriness: reassuring–eerie,
numbing–freaky, and
ordinary–supernatural
Yes
.76
Realism render
Stein & Ohler
(2018)
Eeriness (n.a.)
Yes
.74
Emotion
manipulation,
face distortion,
realism render,
visuo-auditory
mismatch

44
Tinwell et al.
(2013)
Uncanniness: eerie,
nonhumanlike, repulsive,
unattractive, unlikable, and
unresponsive
Yes
.74, .80, .80
Emotion
manipulation
Tung (2016)
[1][2]
Social attraction: friendly,
likable, and pleasant
Yes [1]
No [2]
≥ .70
Distinct
entities
Zlotowski et
al. (2015)
Eeriness (n.a.)
Yes
.62 (lowest of
three
measurements)
Real-life
encounter
Note. Eeriness and Warmth denote the indices developed by Ho and MacDorman (2010,
2017) and their derivations. We did not find studies with information on correlations
between individual scale items.
Table A2. Summary and evaluation of stimulus creation
techniques.
Stimulus
creation
technique
Exemplar
studies
Advantages
Disadvantages
Further
considerations
Distinct entities
Mathur et al.,
2020
Rosenthal–von
der Pütten &
Krämer, 2014
Relatively high
ecological
validity,
variable
stimulus
control, easy
access
Confounding
variables, no
gradual range
Additional control
when selecting
stimuli can
decrease
confounding
variables
Emotion
manipulation
Tinwell et al.,
2014
Specific,
controllable
stimulus
manipulation
stimulus noise
Face distortion
Mäkäräinen et
al., 2014
MacDorman et
al., (2009)
Controllable
stimulus
manipulation,
gradual range
Stimulus noise
Strength of
distortion should
have a sufficient
range

45
Morphing
Lischetzke et
al., 2017
Sasaki, Ihaya, &
Yamada
Controllable
stimulus
manipulation,
gradual range
Results depend on
endpoint stimuli
choice, stimulus
noise
Endpoint stimuli
should be
sufficiently
distinct
Mismatch
Seyama &
Nagayama,
2007
Controllable
stimulus
manipulation
Stimulus noise, no
gradual range
Selection of
mismatched
features (e.g.,
eyes)
Lack of research
Motion
manipulation
Handzic &
Reed, 2015
Lack of research
Realism render
McDonnell et
al., 2012
MacDorman &
Chattopadhyay,
2017
Controllable
stimulus
manipulation
Stimulus noise
Real-life
encounter
Zlotowski et al.,
2015
Bartneck,
Kanda,
Ishiguro, &
Hagita, 2009
High ecological
validity for
android science
Low internal
validity, difficult
setup and stimulus
acquisition
Android/robotic
and human
counterpart stimuli
should match
Lack of research
Visuo-auditory
mismatch
Mitchell et al.,
2011b
Lack of research
Voice distortion
Baird et al.,2018
Lack of research