The uncanny valley is a theory in aesthetics: a humanoid object that looks almost, but not exactly, human can trigger eeriness or revulsion instead of familiarity. The closer it gets to looking real without quite reaching it, the more unsettling it feels.
Key Takeaways
- Definition: Affinity for a humanoid rises with human-likeness, then plunges into eeriness just short of fully human, before recovering.
- Origin: Robotics professor Masahiro Mori named the effect bukimi no tani genshō in 1970; Jasia Reichardt later translated it as “uncanny valley.”
- Explanations: Candidates include violated human norms, mortality salience, pathogen avoidance, threat to human identity, and mismatched perceptual cues.
- Design Fixes: Increasing or decreasing a humanoid’s human-like features can both pull it out of the valley, depending on the cause.
- Modern Evidence: A 2021 meta-analysis of 72 studies confirms the effect is large and reliable (Diel, Weigelt, & MacDorman, 2021), with perceptual mismatch the best-supported cause.
- Ongoing Debate: Heterogeneous causes, cultural background, and ordinary familiarity all complicate any single, universal explanation.
What Is Uncanny Valley?
The uncanny valley is the hypothesized relationship between how closely a humanoid entity resembles an actual human being and the emotional response the entity evokes (MacDorman & Ishiguro, 2006).
The theory holds that humanoid entities which closely resemble actual humans can provoke strangely familiar or ‘uncanny’ feelings of revulsion or eeriness in the onlookers.
The ‘valley’ is this drop in affinity. Liking would otherwise rise steadily as the object looks more human (MacDorman & Chattopadhyay, 2016).
The humanoid objects may include a variety of entities, including 3D animations, virtual reality, photorealistic animation, robotics, and lifelike dolls (MacDorman & Chattopadhyay, 2017).
As the object’s appearance gradually becomes indistinguishable from reality, the observers may feel a sense of creepiness, unease, or even disgust. The uncanny valley has manifold implications for artificial intelligence, robotics, and devices that are designed to serve and assist people.
Origin and History
In an article published in 1970, the Japanese professor of robotics Masahiro Mori identified this phenomenon as bukimi no tani genshō (Mori, 2012). Subsequently, this Japanese term was translated into English as the ‘uncanny valley’ by Jasia Reichardt in her book ‘Robots: Fact, Fiction, and Prediction’ in 1978 (Kageki, 2012).
In his original hypothesis, Mori held that the more a robot resembles an actual human, the more empathetic and positive the response becomes. That holds only until the resemblance reaches a certain point (Mori, 1970).
Past that point, the positive response flips into intense revulsion. As the robot’s appearance keeps becoming more human, the positive emotions return, and empathy climbs back toward the level we show fellow humans.
This revulsion sits in the valley itself, between barely-human and fully-human appearance. A robot at this stage looks almost human, and that near-miss is what makes it feel strange.
Mori did not discover the phenomenon himself, though; the concept predates his 1970 essay.
Charles Darwin described something similar after observing a trigonocephalous viper’s face, writing in The Voyage of the Beagle:
“I imagine this repulsive aspect originates from the features being placed in positions, with respect to each other, somewhat proportional to the human face; and thus, we obtain a scale of hideousness” (Darwin, 1839).
In light of Mori’s description of the uncanny valley, Darwin’s experience, as we will discuss later, seems to unveil certain cognitive processes possibly underlying this phenomenon.
The Role of Movement
Mori made a second claim, often overlooked. Movement, he argued, deepens the valley: motion stretches the curve so the peaks rise and the trough sinks further.
A still corpse already looks human, but it is inert. It sits at the bottom of the first dip.
A moving corpse, a zombie, plunges deeper still, because motion sharpens the conflict between “this looks alive” and “this is not” (Mori, 1970/2012).
The same logic explains a familiar pattern: a lifelike android’s stiff gait, or a CGI character’s slightly wrong facial motion, often disturbs viewers more than a still image would.
Movement is not always a liability, though.
Mori pointed to Bunraku puppetry: a puppet is only moderately humanlike, yet skilled, expressive movement pulls it toward the affinity peak rather than into the valley. Context and skill, not human-likeness alone, decide where it lands.
Uncanny Valley Examples
- Realistic robots or androids that mimic human appearance and movement, but aren’t completely convincing, often trigger the uncanny valley.
- Some high-resolution video games or computer-generated imagery (CGI) in films can fall into the uncanny valley if characters look almost real but have unnatural movements.
- Lifelike dolls or mannequins that are almost human-looking can also cause the uncanny valley effect.
The uncanny valley has been observed in a variety of spheres, but its salience in movies merits special attention.
Tin Toy
The short, animated film, Tin Toy, produced by Pixar in 1988, seems to be the first instance of the uncanny valley associated with movies. Billy, the silly infant character in the film, which was animated using the PhotoRealistic RenderMan software, elicited negative reactions from the audience (Capps, 2009).
Despite the success of the film, the issues associated with the animation of Billy would subsequently lead the film industry to take the uncanny effect seriously.
Final Fantasy: The Spirits Within
Final Fantasy: The Spirits Within was a computer-animated science fiction movie directed by Hironobu Sakaguchi. The almost natural yet imperfect depictions of humans in this photorealistic film provoked eerie reactions from viewers.
Observing the movie’s characters, Peter Travers wrote in Rolling Stone, “You notice coldness in the eyes, a mechanical quality in the movements” (Travers, 2001).
Furthermore, in The Guardian, Peter Bradshaw commented that the faces of the characters look “shriekingly phony precisely because they’re almost there but not quite” (Bradshaw, 2001).
A Christmas Carol
The 2009 computer-animated film directed by Robert Zemeckis was an adaptation of Charles Dickens’s A Christmas Carol. Reviewers described the film’s animation as creepy.
The New York Daily News’ Joe Neumaier wrote that “the animated eyes never seem to focus” and that “for all the photorealism, when characters get wiggly-limbed and bouncy as in standard Disney cartoons, it’s off-putting” (Neumaier, 2009).
The Adventures of Tintin: The Secret of the Unicorn
The 2011 3D action-adventure film, based on Hergé’s The Adventures of Tintin, also produced an uncanny effect for some viewers. The Economist’s N.B. noted that the characters’ “features are those of flesh-and-blood people” and “yet they still have the sausage fingers and distended noses of comic-strip characters” (N.B., 2011).
Moreover, The Atlantic’s Daniel D. Snyder wrote that Tintin’s original face is “now outfitted with an alien and unfamiliar visage” with “his plastic skin dotted with pores and subtle wrinkles” (Snyder, 2011).
Effects of the Uncanny Valley
The uncanny valley can trigger a range of effects. Some are emotional, such as repulsion and decreased empathy; others are more practical, such as slowing the development and acceptance of artificial intelligence and robotics.
Below are detailed elaborations on each of these effects:
- Discomfort or Repulsion: a deep unease when exposed to a near-human object, likely a biological response to something that looks human but is “off,” historically a sign of disease or danger.
- Decreased Empathy and Trust: a humanoid robot that falls into the valley is less likely to be trusted or empathized with, which limits how effectively it can interact with people.
- Detraction from Realism: in games and films, a character that looks nearly human but moves awkwardly breaks immersion and makes the character feel less real.
- Challenges for Robotics and AI: designers who risk the valley may see a robot’s adoption limited; many deliberately choose clearly artificial aesthetics instead.
Theories Explaining the Uncanny Valley
Several theories have been proposed to account for the cognitive mechanism responsible for the uncanny valley.
The Violation of Human Norms
The uncanny valley may result from an entity’s failure to meet human standards (Saygin, Chaminade, Ishiguro, Driver, & Frith, 2012; MacDorman & Ishiguro, 2006).
When an object looks sufficiently non-human, its human features stand out and tend to elicit empathy. But when an object looks almost human, its non-human features become the ones that stand out.
This mismatch makes the object seem strange. It fails to meet the standards we hold for an actual human being.
A robot here faces a different bar. It is not judged as a robot. It is judged as a human being, expected to act like a normal person rather than a machine built for certain tasks.
The robot’s inability to fully resemble human norms, on this account, is what causes the uncanny valley.
The Salience of Mortality
Another explanation holds that the uncanny valley results from an inborn fear of death coupled with culturally accepted mechanisms for coping with the inevitability of death (MacDorman & Ishiguro, 2006). This connects the valley to terror management theory, which holds that reminders of death trigger anxiety-buffering psychological defences.
According to this theory, androids evoke our subconscious fears of replacement, reduction, or annihilation. For instance, when androids resemble actual people, they may be construed as doppelgängers.
Consequently, an observer could be afflicted with the fear of being replaced in a certain sphere of life, such as a relationship or a job. Androids shown partially disassembled, decapitated, or mutilated can evoke images of a battlefield’s aftermath.
Hence, such scenes can be reminiscent of human mortality. Additionally, the mechanical interior of an almost humanlike robot can evoke the thought that human beings, too, are merely soulless machines.
Furthermore, the mechanical and jerky movements of such an android may elicit the fear of losing control over one’s own body.
The Avoidance of Pathogens
This theory holds that the uncanny valley might be activating the cognitive mechanism which had originally evolved to help humans avoid sources of pathogens (Rhodes & Zebrowitz, 2002) (Moosa & Ud-Dean, 2010) (Roberts, 2012).
According to this proposition, robots and androids in the uncanny valley may resemble human organisms with defects. Since the presence of defects implies disease, a feeling of aversion may be induced in the observers.
We know that the more a particular organism resembles a human, the more closely related genetically that organism is likely to be to humans. Moreover, greater genetic similarity is associated with a higher probability of contracting pathogenic viruses, bacteria and other parasites.
Therefore, the visual stimuli of the uncanny valley may elicit the same reactions such pathogens do. Robots and androids, for these reasons, can engender the feelings of revulsion or alarm that diseased humans and dead corpses produce.
The Challenge to Human Identity
Ferrari, Paladino, and Jetten (2016) found that a more humanlike social robot is perceived as more threatening to human distinctiveness. That damaged sense of distinctiveness predicts discomfort.
The more an object resembles an actual human being, the more it seems to challenge the social identity of humans. That, in effect, redefines what makes someone human.
This blurring of the human/non-human boundary may elicit negative reactions and unease (MacDorman & Entezari, 2015; Ferrari, Paladino, & Jetten, 2016). Kaplan (2004) found that acceptance of humanoid robots also varies culturally, with greater acceptance often reported in Japan than elsewhere.
The Discord between Perceptual Cues
This theory posits that the visual stimuli of the uncanny valley activate contradictory cognitive representations (Elliot & Devine, 1994; Ferrey, Burleigh & Fenske, 2015).
For instance, a humanlike figure with robotic features creates perceptual tension: the observer gets contradictory cues about which category it belongs to. This tension feels like a kind of cognitive dissonance.
Mathur and Reichling (2016) found that the deeper a robot’s face sits in the uncanny valley, the longer observers take to judge whether it is human. That extra hesitation reflects a greater cognitive challenge in categorizing the face.
Studies also suggest that this cognitive challenge is associated with the negative emotions of the uncanny valley (Yamada, Kawabe & Ihaya, 2013).
Perceptual mismatch and categorization difficulty, according to this explanation, seem to be the primary causes of revulsion or eeriness (Kätsyri, Förger, Mäkäräinen & Takala, 2015).
Key Studies on the Uncanny Valley
Monkeys: Steckenfinger and Ghazanfar (2009)
If the uncanny reaction has evolutionary roots rather than a purely cultural one, it should appear in other primates too. Steckenfinger and Ghazanfar (2009) tested this directly.
Aim: to test whether long-tailed macaques show an uncanny-valley-like aversion to realistic but imperfect synthetic faces, pointing to an evolutionary rather than cultural origin.
Method: macaques viewed three face types: real, realistic-synthetic, and unrealistic-synthetic. Researchers measured looking time at each, since monkeys look longer at stimuli they prefer.
Results: the monkeys looked longer at the real faces and the unrealistic synthetic faces than at the realistic synthetic faces. The dip was real. They avoided the near-real renderings specifically, the same “valley” pattern seen in humans.
Conclusion: an uncanny-valley-shaped response appears in a non-human primate, supporting an evolutionary basis for the effect rather than a purely cultural one.
Evaluation: Looking-time is non-verbal, immune to human demand characteristics. That is a real strength. But the small sample and its reliance on gaze rather than reported feeling limit how much the study can say about which explanation drives the reaction.
Video Games
Tinwell, Grimshaw, and Williams (2010) examined how a virtual character’s sound and motion affect perceived eeriness. Anthropomorphic sound and motion, they found, can exaggerate a character’s uncanniness.
Uncanniness also rose as a character’s facial expression, voice, and mouth movement during speech grew less human-like.
Another similar study was conducted to assess the uncanniness produced by humanlike virtual characters whose upper faces showed a perceived lack of expression for various emotions (Tinwell, Grimshaw, Williams & Nabi, 2011).
The investigation controlled individual parameters for the facial muscles for six different emotions: disgust, happiness, surprise, anger, fear, and sadness. The results of this study seemed to indicate that humanlike, animated, talking-head, and high-fidelity virtual characters would be rated as uncanny.
Notably, the same virtual characters would be rated significantly uncannier when their emotional expressivity and movement were limited to the upper face. Moreover, the level of this heightened uncanniness seemed to depend on the type of emotion being conveyed.
While sadness, surprise, fear, and disgust seemed to elicit more uncanniness, happiness, and anger seemed to be associated with relatively less uncanniness.
Static Images
Mathur and Reichling (2016) tested whether the uncanny valley appears in static images of real robotic faces. Subjects rated the likability of two face sets: 80 robotic faces gathered from the internet, and a second, graphically and morphometrically controlled set.
These two sets spanned from extremely humanlike to very mechanical. To gauge the level of trust toward each face, the subjects engaged in an investment game that indicated how much they would ‘wager’ on a robot’s trustworthiness.
The explicit likability ratings showed a robust uncanny valley effect. The implicit trust ratings, from the investment game, showed the effect too, though it depended more on context.
This outcome seemed to imply that while category confusion is associated with the uncanny valley, it does not mediate the impact on emotional reactions.
Neuroscience: Saygin et al. (2012)
This neuroimaging study asked a sharp question. Does the uncanny reaction come from a real conflict between an agent’s human appearance and its non-human movement?
Aim: to test whether that appearance-motion conflict shows up as heightened activity in the brain’s action-perception network.
Method: The design used fMRI repetition suppression. Participants watched a human, a robot, and an android perform identical actions. Only the android mismatched appearance and motion.
Results: only the android produced heightened responses in the bilateral anterior intraparietal sulcus, part of the action-perception system spanning parietal, temporal, and frontal regions. The brain had expected human-looking motion. It got robotic kinematics instead.
Conclusion: the uncanny valley has a neural signature consistent with predictive coding (Saygin, Chaminade, Ishiguro, Driver, & Frith, 2012). The eerie feeling tracks a mismatch between how a human-looking agent should move and how it actually moves.
Evaluation: The design cleanly separates appearance from movement. That is a real strength. But the sample was small, and one android cannot rule out other explanations.
Avoiding the Uncanny Valley
Avoiding the uncanny valley means matching the cause to the fix: voice, motion, and appearance can each trigger the effect on their own (Saygin, Chaminade, & Ishiguro, 2010; Saygin, Chaminade, Ishiguro, Driver, & Frith, 2012).
Matching Voice and Appearance
A human with a human voice avoids eeriness. So does a robot with a synthetic voice.
A robot with a human voice does not: the mismatch itself lands it in the valley.
Designers can fix this two ways.
They can make the robot’s appearance more human, to match the voice. Or they can swap the human voice for a synthetic one, to match the appearance.
A synthetic voice paired with a clearly mechanical robot never invites the human expectation in the first place. Nothing is violated, so no fix is even needed.
Voice is just one channel among several; the same logic that governs it turns out to govern motion and appearance too.
That link is worth remembering: fixing one channel rarely helps if the others still clash.
Matching Motion and Appearance
A virtual character that looks human but moves stiffly can also produce an uncanny effect (Goetz, Kiesler, & Powers, 2003; Saygin, Chaminade, Ishiguro, Driver, & Frith, 2012).
The fix again is to match the two cues.
Giving the character a less humanlike appearance, or a more humanlike movement, brings appearance and motion back into line and levels out the valley.
Goetz, Kiesler, and Powers (2003) made a related design point: matching a robot’s whole manner, not just one feature, to its task improves how well people cooperate with it.
A serious-looking robot suits a serious task. A playful, cartoonish one suits a playful one.
The “right” degree of human-likeness, on this view, is not fixed; it depends entirely on what the robot is actually for and who it needs to work with.
Fixing Appearance Alone
Sometimes appearance alone causes the problem, and appearance alone is the only fix (MacDorman, Green, Ho, & Koch, 2009; Saygin, Chaminade, Ishiguro, Driver, & Frith, 2012).
Artists sometimes use anomalous facial proportions to make computer-generated characters more attractive.
Flawless features can backfire, inducing eeriness rather than appeal.
Pushing for more realism, rather than more polish, is what removes the effect here.
The lesson generalises: a figure does not have to be ugly to fall into the valley. Sometimes it is the artificial pursuit of perfection itself that tips it in.
Prosthetics face the same dilemma.
A highly realistic limb aids social passing but risks the valley up close.
Some designers now prefer openly stylised, expressive prostheses that sidestep the problem entirely, rather than chasing photorealism.
The same trade-off shapes digital avatars and virtual assistants, which increasingly face the same choice between realism and safety.
Critical Evaluation
Several objections challenge the scientific standing of the uncanny valley theory. Four are worth taking seriously:
- Too Many Explanations: the valley may be a mix of separate, overlapping phenomena rather than one single mechanism.
- Individual and Cultural Differences: the reaction is not uniform; background, familiarity, and age all shift how strongly someone feels it.
- Is the Valley Real, or an Artefact?: the “dip” may just reflect ordinary familiarity or frequency effects, not a distinct uncanny mechanism.
- The Capgras Edge Case: a rare clinical condition suggests eeriness can arise even when the face in front of you is genuinely human.
Too Many Explanations
MacDorman, Green, Ho, and Koch (2009) counted the candidate mechanisms on offer: norm violation, mortality salience, pathogen avoidance, identity threat, and perceptual discord, among several others.
No single cause need be doing all the work.
MacDorman and Ishiguro (2006) made a related point: distinct sense modalities and psychological constructs, not just appearance, matter too.
An observer’s cultural background can shape how an android is perceived, and how strongly the valley bites in a given situation.
Context, in other words, modulates every account on this list.
Adjudicating between these accounts, rather than adding new ones, is the real task facing the field.
MacDorman and Ishiguro’s (2006) own review set out that menu of mechanisms first, which is partly why later work weighs them against each other.
Individual and Cultural Differences
Younger people who are more familiar with robots and androids tend to be less affected by the hypothesized uncanny effect.
This points to exposure, not a fixed response.
MacDorman and Entezari (2015) found that individual differences predict who experiences the valley most strongly.
Sensitivity to threat, and personality traits linked to discomfort with ambiguity, both play a role. Some people are simply more bothered by an ambiguous, near-human face than others are.
Cultural background matters too. Kaplan (2004) documented cross-cultural variation in the acceptance of humanoid robots, with greater acceptance often reported in Japan than in other countries.
This variability sits awkwardly with any single, universal mechanism. A disposition-and-exposure-modulated reaction, rather than a reflex everyone experiences equally, better fits what the evidence shows.
Is the Valley Real, or an Artefact?
Cheetham, Suter, and Jäncke (2011) pointed to a confound running through many demonstrations: familiarity.
We simply encounter real human faces far more often than near-human artefacts. That alone could produce the dip researchers call “uncanny.”
Burleigh and Schoenherr (2014) pushed this further: the effect may split between frequency-based information processing and category-boundary effects.
Altering how often people practice categorizing items changes the picture. It shows a dissociation between category-boundary uncertainty and frequency-based uncertainty.
Hanson, Olney, Pereira, and Zielke (2005) added a design-based challenge.
With careful, attractive design, a figure can be taken through the near-human region without falling into any valley at all.
If the dip is avoidable, it may not be an inevitable law of perception, but rather an artifact of poor design choices.
The Capgras Edge Case
Capgras delusion, a rare misidentification syndrome, is sometimes read as an inverse of the valley.
Here, a genuinely human face feels eerily like an impostor.
Sufferers can rationally acknowledge that the person in front of them looks identical to someone they know.
Yet the belief in replacement persists. In some cases, the duplicate is even perceived as a robot.
Michael Lewis and Hadyn Ellis explained Capgras as a split between two systems (Ellis & Lewis, 2001).
One is an intact mechanism for overt face recognition. The other is a damaged mechanism for covert, affective recognition.
The face is identified, but the usual emotional “glow” of recognition is missing.
This complicates any account that locates the uncanny valley purely in an artificial stimulus’s features.
Eeriness, Capgras suggests, can also arise from a mismatch between recognition and feeling, even when the stimulus is a real human being.
Contemporary Research
That debate is now largely settled. Diel, Weigelt, and MacDorman (2021) ran the field’s first quantitative meta-analysis.
Aim: to establish, by meta-analysis, how reliable and how large the uncanny valley effect is, and whether the method used to create stimuli changes the result.
Method: a systematic search screened 468 studies. Of these, 72 met inclusion criteria, yielding 247 effect sizes, combined using a three-level meta-analysis that accounted for multiple effects per study.
Results: the effect was large, Hedges’ g = 1.01. Face-distortion stimuli produced the biggest effect (g = 1.46), ahead of distinct entities, realism rendering, and morphing.
Conclusion: the uncanny valley is genuine and substantial. Its size depends heavily on how researchers build their stimuli, a caution that past inconsistent results reflect method, not an unreliable phenomenon.
Where the evidence adjudicates cause, it favours perceptual mismatch over the more speculative existential accounts.
Kätsyri, Förger, Mäkäräinen, and Takala (2015) concluded that perceptual mismatch, atypical or inconsistent features within an otherwise human face, is the best-supported single route to eeriness.
Wang, Lilienfeld, and Rochat (2015) reviewed the field more broadly.
They found the evidence for mortality-salience and other existential accounts weak and often untestable, cautioning that the phenomenon has been over-theorised relative to its data.
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