Examining the Effects of Human-Likeness of Avatars on Emotion Perception and Emotion Elicitation

Dennis K\"uster; Hui Liu; Omar Faruk; Robert Porzel; Shiyao Zhang; Tanja Schultz

REVIEW 4 major objections 4 minor 32 references

Examining the Effects of Human-Likeness of Avatars on Emotion Perception and Emotion Elicitation

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read An avatar's cuteness and resting facial expression shift both the emotions viewers see and the emotions viewers feel, with human-like avatars skewing negative.

desk verdict Small exploratory study with a plausible but unidentifiable central claim; the abstract overstates what the data can show, but the authors are upfront about their confounds. read the letter →

arxiv 2508.01743 v1 pith:EHCAXHA7 submitted 2025-08-03 cs.HC

Shiyao Zhang , Omar Faruk , Robert Porzel , Dennis Küster , Tanja Schultz , Hui Liu This is my paper

classification cs.HC

keywords avatarhuman-likenessemotionperceptionelicitationuncannyvalleycuteavatarssixbasicemotionsfacialexpressionanimationcomputer-mediatedcommunication

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether the look of an animated avatar changes not only which emotion a viewer thinks the avatar is showing, but also which emotion the viewer ends up feeling. The study converted videos of real people performing the six basic emotions into animations of three avatars (banana, raccoon, human) for a first group of four raters, then into five avatars (adding pig and shark) for a second group of eleven raters, who annotated both the perceived emotion and their own felt emotion with confidence ratings. The results show that the human avatar pushes perception and felt emotion toward the negative, consistent with the uncanny valley, while the 'cute' raccoon and shark avatars push both toward the positive. The follow-up adds that an avatar's natural resting expression, such as the pig's sad face, also steers emotional responses. If these findings hold, avatar choice is not a neutral privacy feature but an active channel that shifts emotional communication.

What carries the argument

The carrying mechanism is the automatic conversion of the same acted performances onto differently shaped avatar bodies: the study uses a commercial avatar-animation pipeline to transfer videos of real people expressing the six basic emotions onto each avatar, so the underlying expressive content is fixed while the visual surface varies. Raters then complete two tasks—saying which emotion the avatar displayed and which emotion they themselves felt—with confidence values, and the paper summarizes responses with confusion matrices (tables of which emotions were mistaken for which) and an inter-rater agreement statistic. The six emotions are grouped into positive versus negative classes, which is what makes the directional shifts visible: raccoon and shark move responses toward positive emotion, while human and pig move them toward negative emotion. The uncanny valley supplies the explanation for the human avatar's negative pull: near-human but imperfect faces evoke unease.

What would settle it

Run the same acted emotion videos through the same animation settings on human, raccoon, shark, pig, and banana models, with each model's neutral resting face matched as closely as possible; if the human model no longer shifts raters' perceived and felt emotions toward the negative relative to the raccoon and shark, the claimed causal roles of human-likeness and cuteness are refuted. A supporting check would be automated measurement of expression fidelity in the rendered videos to rule out the possibility that the human avatar's animations are simply less faithful than the others.

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Extended reading notes

Core claim

The paper's central claim is that human-likeness is a real but incomplete predictor of how avatars transmit emotion. In the annotation tasks, the human avatar elicited more negative emotional responses from raters and shifted perceived emotion from positive to negative, a pattern the paper attributes to the uncanny valley. The raccoon avatar, lower in human-likeness but high in cuteness, communicated the intended emotions most reliably and shifted both perception and elicitation toward positive emotion; the shark showed a similar positive pull in the follow-up study. The banana avatar, the least human-like and most abstract, was the hardest to recognize, while the pig's naturally sad resting face made raters read and feel more sadness. The paper concludes that avatar design drives emotion perception and elicitation through at least three intertwined attributes: degree of human-likeness, perceived cuteness, and natural facial status.

Load-bearing premise

The study assumes that the automatic animation pipeline turns the actors' filmed expressions into equally faithful avatar expressions across all five avatars, so the emotional differences it observes come from how the avatars look rather than from differences in animation quality or baseline facial morphology.

Editorial extensions

If this is right

Avatar choice becomes a design decision with emotional consequences: a cute raccoon or shark can nudge a negative message toward positive affect, while a human-like avatar can shift a positive message toward negative affect.
Emotion perception accuracy is not uniform across avatars; the raccoon was read most reliably and the banana least reliably, so avatar communication benchmarks should report per-avatar confusion patterns rather than a single accuracy score.
An avatar's resting facial state is a controllable variable; the pig's naturally sad face biased both interpretation and felt emotion, so designers should treat baseline expression as an affordance.
High human-likeness does not guarantee better communication; the paper's uncanny-valley reading implies a 'sweet spot' around moderate human-likeness with cute features, not maximum realism.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A practical extension the paper leaves implicit is that the same message can be valenced by avatar choice: a counselor or customer-service agent could deliberately use a cute avatar to soften bad news or a human-like avatar to signal seriousness.
Because all five avatars come from one conversion pipeline, the cleanest follow-up would vary cuteness and resting-face shape within a single species model (for example, two raccoons differing only in eye size or mouth curvature) to separate design variables from species-specific associations; the current design cannot fully separate these.
The combination of high self-reported confidence and low inter-rater agreement for the banana avatar suggests that abstract avatars could cause undetected communication failures in settings such as counseling, where users believe they have understood but have not; a test of real-task consequences would extend the paper's results.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

Quick read on 2508.01743: it's a small, exploratory HCI study with a clean dual-task design across five avatars, but the evidence is too thin to support the abstract's causal talk about human-likeness and cuteness. The genuinely new bit is measuring both what viewers recognize and what they feel, across a range that includes cute animal avatars (raccoon, shark). That's a legitimate extension of prior avatar-realism work, and the qualitative pattern—raccoon/shark shift responses positive, human avatar shifts negative—is worth knowing about.

What the paper does well: two annotation tasks, clear confusion matrices, and the authors are honest about their own limitations. They explicitly say four participants are not generalizable, they flag the pig's sad resting face as a confound, they concede the banana's reduced feature visibility may have mattered, and they note Animaze artifacts like eye-blinking misclassification. That self-awareness is better than most.

Where it falls short: the central attribution doesn't survive contact with the stimulus set. Human-likeness is not manipulated independently of cuteness, baseline facial morphology, or conversion fidelity. The pig's "natural facial expression conveying sadness" is doing work in the elicitation results, and that's not a human-likeness effect. The banana's missing features are a visibility confound. No manipulation check reports perceived human-likeness, cuteness, or neutrality of the neutral face, and there are no inferential statistics, confidence intervals, or effect sizes anywhere. With n=4 and then n=11 and low Fleiss' kappa for most emotions, the abstract's "tend to elicit" and "demonstrate a positive influence" are overstatements. The data show between-avatar differences; they do not identify the cause.

Also, no stimuli or data are shared, so none of this can be checked or re-analyzed, which matters when claims are this soft.

The stress-test note is right: the load-bearing step is not identifiable. That said, this is not a dishonest paper; it's an underpowered, confounded pilot that reads its own results too strongly in the abstract. The conclusion actually says human-likeness is "not the only determining factor," which is closer to the evidence than the abstract.

Who this is for: people working on avatar design or affective computing who want a quick map of possible effects. It deserves a serious referee because the question is real and the design is a legitimate starting point, but it needs major revision: report inferential stats with effect sizes, add a manipulation check, and release stimuli.

My take: send it out, but reviewers should push hard on the identification problem.

Best,

Referee Report

4 major / 4 minor

Summary. The paper investigates how the human-likeness of animated avatars affects both the perception of six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the emotions elicited in viewers. Two within-subjects studies are reported: a preliminary study with 4 participants viewing 18 emotion videos rendered on 3 avatars, and a follow-up study with 11 participants viewing 30 videos rendered on 5 avatars. Stimuli were produced by automatically converting human acted performances into avatar animations using Animaze. The main claims are that high-human-likeness avatars (human) elicit more negative emotional responses, consistent with an Uncanny Valley interpretation; that cuter, moderately human-like avatars (raccoon, shark) elicit positive emotions; and that the pig avatar's naturally sad resting expression influences negative elicitation. Results are presented mainly as confusion matrices, Fleiss' kappa values, and confidence-value violin plots, with no inferential statistics.

Significance. If the causal claims were established, the work would be practically relevant for avatar-based communication, helping designers choose avatars that convey intended emotions and avoid unintended negative reactions. The topic is timely and the use of a diverse avatar set (human, raccoon, shark, pig, banana) is a useful exploratory step. The manuscript also includes useful descriptive material, such as confusion matrices, inter-rater agreement coefficients, and follow-up on potential software artifacts. However, the current evidence is far too weak to support the paper's causal attributions: sample sizes are very small, no inferential statistics or effect sizes are reported, and the avatar set confounds human-likeness with baseline facial morphology, cuteness, feature visibility, and animation fidelity. The paper's own discussion acknowledges several of these confounds, which further underscores that the central claims are not yet identifiable from the data.

major comments (4)

[Sections III, IV, and V] The central quantitative claims rest on only 4 participants in the preliminary study and 11 in the follow-up, yet no inferential statistics, confidence intervals, or effect sizes are reported for any comparison. For example, Section IV-C reports Fleiss' kappa values below 0.4 for most emotions and interprets them as 'confusion,' but no test assesses whether perception differs significantly across avatars or emotions. Similarly, the directional conclusions in Section V about human avatars eliciting negative emotions and cute avatars eliciting positive emotions are drawn directly from descriptive confusion matrices without quantifying uncertainty. The authors should either provide appropriate statistical analyses (e.g., mixed-effects models with participant random effects, or exact tests) or explicitly and prominently label the results as descriptive pilot observations that do not support generalization.
[Sections V and VI] The avatar conditions are confounded with baseline morphology and natural facial expression, so differences cannot be attributed to human-likeness. Section V states that the pig avatar's 'natural facial expression conveying sadness' made it 'more likely to evoke negative emotions,' and Section VI concedes that 'the reduced visibility of facial features for the banana might still have had an effect on emotion perception and elicitation.' These are properties of specific avatar designs, not manipulations of human-likeness. Without manipulation checks for perceived human-likeness, cuteness, and natural facial status, the abstract's causal claim that human-likeness drives the observed effects is not identifiable. Furthermore, the constructs 'cuteness' and 'natural facial status' are not measured independently but are inferred from the same participant responses they are used to explain, as when the pig's sad resting face is identified from its negative elicitation results.
[Section III] The automated conversion pipeline (Animaze) is assumed to preserve the emotional content of the original human performances equally across all avatars, but no validation of this assumption is provided. Per-avatar differences in animation fidelity, facial-feature visibility, or tracking quality could fully account for the observed differences in emotion perception and elicitation. Section VI itself reports an Animaze artifact where users with smaller eyes are misidentified as blinking, indicating that conversion fidelity is not uniform. The authors should validate the conversion by comparing avatar emotion recognition to human-video recognition, or at minimum report per-avatar rendering-fidelity metrics and discuss how they might affect the results.
[Abstract and Section VII] The abstract overstates what the data can show by stating that 'High human-likeness avatars ... tend to elicit more negative emotional responses from users' and that cute avatars 'demonstrate a positive influence on emotion perception.' The design only demonstrates between-avatar differences in descriptive outcomes; it does not identify human-likeness or cuteness as the cause, as the Discussion itself acknowledges through alternative explanations such as natural facial status and feature visibility. Moreover, Section VII states that future work should 'more systematically manipulate dimensions of human-likeness as well as other notable differences between avatars,' which implicitly concedes that the current manipulation is not systematic. The central claims should be reworded as exploratory hypotheses, not conclusions.

minor comments (4)

[Section II] There is an incomplete sentence: 'Since voice and facial emotions are significant means of expressing emotion [10],.' The sentence should be completed or the fragment removed.
[Figure 8 caption] The caption contains a typo: 'basix emotions' should be 'basic emotions.'
[Section III] The method description does not specify how the 18 and 30 videos were distributed across emotions and avatars, whether each emotion was presented more than once per avatar, or whether trial order was randomized and counterbalanced. Adding this information would clarify the stimulus design and aid reproducibility.
[Figures 2 and 3] The confusion-matrix captions do not explain the color scale, the meaning of row/column sums, or whether values are counts or percentages. Please define these in the captions or in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is an empirical comparison with acknowledged confounds, not a fitted-parameter prediction or a self-citation chain.

full rationale

The paper contains no formal derivation, fitted parameter, or mathematical model whose output is built back into its input. Its central claims about human-likeness, cuteness, and natural facial status are empirical interpretations of annotation data (confusion matrices, Fleiss' kappa, confidence values), not results forced by definition. The explanatory labels 'cuteness' and 'natural facial expression conveying sadness' are not independently measured with rating scales, and the study has genuine confounds such as Animaze conversion fidelity, banana feature visibility, and the pig's baseline morphology; these are threats to internal validity and generalizability, which the authors themselves acknowledge in Sections VI and VII ('the reduced visibility of facial features for the banana might still have had an effect', 'future work should aim to more systematically manipulate dimensions of human-likeness'). But a confound is not circularity: the observed emotion-perception and emotion-elicitation differences are not equivalent by construction to the avatars' human-likeness or cuteness labels. The few self-citations are background references in related work and are not load-bearing for the paper's conclusions. Therefore no specific circular step can be exhibited under the required evidentiary standard.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters or invented entities appear. The central assumptions are the validity of the emotion taxonomy, the faithfulness of the avatar conversion, the validity of self-report, and the post-hoc identification of cuteness and natural facial status. The conversion-faithfulness assumption is the most load-bearing because it is untested and plausibly false given that the pig avatar's resting face is said to convey sadness.

assumptions (4)

domain assumption Ekman's six basic emotions are a valid and sufficient taxonomy for labeling perceived and elicited emotions.
The study relies on this taxonomy throughout Section III and IV without testing whether participants would use other emotion labels.
ad hoc to paper The automated avatar conversion pipeline preserves the emotional content of the original human performance across all avatars.
Invoked in Section III ('automatically converted and displayed by avatars'); this is the central premise that allows attribution of effects to avatar design rather than animation fidelity.
domain assumption Self-reported emotion labels reflect participants' genuinely perceived and elicited emotional states.
The two annotation tasks treat a button press as a valid measure of perception and of actual felt emotion, a standard but unvalidated assumption in this study.
ad hoc to paper The traits 'cuteness' and 'natural facial status' are stable avatar properties that can be identified from the design without separate measurement.
These constructs are introduced in Section V to explain the raccoon, shark, and pig results, but no manipulation check or independent rating is provided.

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Cite this review

Pith. "Pith review of Examining the Effects of Human-Likeness of Avatars on Emotion Perception and Emotion Elicitation." pith.science (2026). https://pith.science/paper/EHCAXHA7

@misc{pith2026250801743,
  author       = {Pith},
  title        = {Pith review of: Examining the Effects of Human-Likeness of Avatars on Emotion Perception and Emotion Elicitation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EHCAXHA7}},
  note         = {Machine review of arXiv:2508.01743}
}

read the original abstract

An increasing number of online interaction settings now provide the possibility to visually represent oneself via an animated avatar instead of a video stream. Benefits include protecting the communicator's privacy while still providing a means to express their individuality. In consequence, there has been a surge in means for avatar-based personalization, ranging from classic human representations to animals, food items, and more. However, using avatars also has drawbacks. Depending on the human-likeness of the avatar and the corresponding disparities between the avatar and the original expresser, avatars may elicit discomfort or even hinder effective nonverbal communication by distorting emotion perception. This study examines the relationship between the human-likeness of virtual avatars and emotion perception for Ekman's six "basic emotions". Research reveals that avatars with varying degrees of human-likeness have distinct effects on emotion perception. High human-likeness avatars, such as human avatars, tend to elicit more negative emotional responses from users, a phenomenon that is consistent with the concept of Uncanny Valley in aesthetics, which suggests that closely resembling humans can provoke negative emotional responses. Conversely, a raccoon avatar and a shark avatar, known as cuteness, which exhibit moderate human similarity in this study, demonstrate a positive influence on emotion perception. Our initial results suggest that the human-likeness of avatars is an important factor for emotion perception. The results from the follow-up study further suggest that the cuteness of avatars and their natural facial status may also play a significant role in emotion perception and elicitation. We discuss practical implications for strategically conveying specific human behavioral messages through avatars in multiple applications, such as business and counseling.

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Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Digital body, identity and privacy in social virtual reality: A systematic review,

J. Lin and M. E. Latoschik, “Digital body, identity and privacy in social virtual reality: A systematic review,” Frontiers in Virtual Reality , vol. 3, 2022

work page 2022

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D. K ¨uster, E. Krumhuber, and A. Kappas, “Nonverbal Behavior Online: A Focus on Interactions with and via Artificial Agents and Avatars,” in The Social Psychology of Nonverbal Communication , A. Kosti ´c and D. Chadee, Eds. London: Palgrave Macmillan UK, 2015, pp. 272–302. [Online]. Available: http://link.springer.com/10.1057/9781137345868 13

work page doi:10.1057/9781137345868 2015

[3] [3]

Kappas, E

A. Kappas, E. Krumhuber, and D. K ¨uster, Facial behavior . De Gruyter Mouton: Berlin, 2013, vol. 2. [Online]. Available: https://books.google.com/books?hl=en&lr=&id=scbmBQAAQBAJ& oi=fnd&pg=PA131&dq=info:M70qiwZyorEJ:scholar.google.com&ots= B7UPhTCjuC&sig=zdF51pzNiTwpGmapeJY7ecfmcSk

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A brief review of facial emotion recognition based on visual information,

B. Ko, “A brief review of facial emotion recognition based on visual information,” Sensors, vol. 18, no. 2, p. 401, 2018

work page 2018

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Avatars as transitional objects: The impact of avatars and digital objects on adolescent gamers,

B. Koles and P. Nagy, “Avatars as transitional objects: The impact of avatars and digital objects on adolescent gamers,” Journal of Gaming & Virtual Worlds , vol. 8, no. 3, p. 279–296, 2016

work page 2016

[6] [6]

Expressive avatars: Vitality in virtual worlds,

D. Ekdahl and L. Osler, “Expressive avatars: Vitality in virtual worlds,” Philosophy & Technology , vol. 36, no. 2, 2023

work page 2023

[7] [7]

The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction,

K. L. Nowak and C. Rauh, “The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction,” Journal of Computer-Mediated Communication , vol. 11, no. 1, p. 153–178, Nov 2005

work page 2005

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Can a retail web site be social?

L. C. Wang, J. Baker, J. A. Wagner, and K. Wakefield, “Can a retail web site be social?” Journal of Marketing , vol. 71, no. 3, p. 143–157, Jul 2007

work page 2007