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Record W4407118526 · doi:10.1177/20416695251315382

Sound effects have only minor contribution to perceptions of anthropomorphism and animacy of simple animated shapes

2025· article· en· W4407118526 on OpenAlexafffund
Karen Collins, Adel Manji

Bibliographic record

Venuei-Perception · 2025
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAnimacyPerceptionPsychologyAnimationAnimeSound (geography)Cognitive psychologyMovement (music)Minor (academic)Computer scienceAcousticsArtificial intelligenceAestheticsHumanitiesArt

Abstract

fetched live from OpenAlex

While studies of anthropomorphism have spanned many decades, there is little evidence of the role that sound effects may play. We present two studies into sound's influence on perceptions of anthropomorphism and animacy using simple geometric animated shapes. For the first study, conducted on 149 participants, we simplified the animation to just two "bumping" squares. Study Two recreated the Heider-Simmel study of 1944, and was conducted on 250 participants under five conditions: without sound, and with one of two different sound types (interface sounds and "anthropomorphic" robot sounds) with two stereo modes (fixed in stereo position, or binaurally panned with the movement). We had participants answer both the Individual Differences in Anthropomorphism Questionnaire and the Godspeed Questionnaire, with three additional questions added. Results showed that the sound had a minor impact on anthropomorphism and potency in Study One, but did not impact animacy. Study Two showed no significant effect on anthropomorphism or animacy, but did show an impact on perceived intelligence and perceptions of activity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.386
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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