Why would being almost human feel wrong?
One influential account is that the uncanny valley comes from perceptual specialization. We are experts at processing human-related things — faces, voices, bodies, and biological motion — and that expertise makes us sensitive to anomalies or deviations in those things. When an artificial agent is close to human but not quite right, the deviation is especially noticeable, and the evaluation turns relatively negative [4]. In this view, the valley is not a fear of machines as such; it is the cost of being very good at noticing when something human-like is off.
A related explanation points to mismatches between different human-like features. In a study of 251 real-world robot images, negative responses were attributed to particular combinations of perceptual mismatches — for example, when surface and facial features matched each other but did not match the robot's body or manipulator [1]. That suggests the problem is not human-likeness in general but inconsistency among the cues that together make something read as human.
Does the valley always look like a single dip?
The classic picture is a curve: as a robot becomes more human-like, likability rises, then drops sharply near the human end before rising again. A meta-analysis of 49 studies found support for the first part of that pattern — more human-like robots at low to medium anthropomorphism were rated more positively — but it could not confirm the negative turn at high human-likeness because highly anthropomorphic robots were sparsely used in the studies [2]. So the familiar dip is plausible but not firmly established across the robot literature.
The shape may also be more complicated than one dip. The real-world robot study found evidence for two uncanny valleys: Mori's original valley for highly human-like robots, and a second valley for moderately low human-like robots [1]. The study also found that positive and negative emotional responses followed different patterns, and that the word 'uncanny' itself was an unreliable measure of the effect [1]. That matters for interpretation: what people say is uncanny and what they actually rate negatively may not be the same thing.
Is the uncanny valley only about robots?
Not necessarily. The perceptual-specialization account has been applied beyond robots to other categories, including written text and physical places, and the author argues it can explain a wider range of observations than the robot-focused version [4]. The music-classroom article describes a musical version of the same effect: when rhythm, tempo, and pitch are heavily processed by computers, music can lose human nuance and fall into an uncanny valley, shifting positive perception toward negative as it gets close to human without hitting the mark [3]. These are theoretical and applied arguments rather than direct tests of the mechanism, but they suggest the valley is about near-human patterns in general, not just android faces.
Could the valley depend on whose face the robot resembles?
One proposed extension is that self-resemblance might make the effect stronger. A planned study using face morphs built from participants' own faces and a real robot's image expects heightened negative outcomes for self-like stimuli compared with stranger-like stimuli at the apex of the valley, and suggests that social agents should not bear physical resemblance to their users [5]. Because this is a proposed expectation rather than a completed result, it remains a hypothesis about one possible moderator of the effect, not an established cause of the valley.
Sources used in this answer
Two uncanny valleys: Re-evaluating the uncanny valley across the full spectrum of real-world human-like robots
Across 251 real-world robot images, people showed two uncanny valleys — one for highly human-like robots and one for moderately low human-like robots — attributed to unique combinations of perceptual mismatches among human-like features.
Human-Like Robots and the Uncanny Valley
A meta-analysis of 49 studies found more positive responses for more human-like robots at low to medium anthropomorphism, but could not draw conclusions about adverse effects at high human-likeness because highly anthropomorphic robots were sparsely studied.
The uncanny valley in the music classroom: Perspectives on overcorrection
The uncanny valley can appear in music when heavy computer processing of rhythm, tempo, and pitch removes human nuance, making output close to human but not quite human.
At the fringes of normality – a neurocognitive model of the uncanny valley on the detection and negative evaluation of deviations
A neurocognitive model proposes that greater perceptual specialization increases sensitivity to anomalies or deviations in human-related stimuli, leading to relatively negative evaluation of near-human artificial agents.
The Mechanical Self: Examining the Explanations and Impact of the Self-Referencing Effect on the Uncanny Valley Response
A planned study using self- and stranger-like robot face morphs expects stronger negative uncanny valley outcomes for self-like stimuli at the valley's apex.