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Record W3147459165 · doi:10.1109/hri.2010.5453226

Do children perceive robots as alive? Children's attributions of human characteristics

2010· article· en· W3147459165 on OpenAlexaffabout
Tanya Beran, Alejandro Ramirez-Serrano

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAnimismRobotSet (abstract data type)AttributionPsychologyCognitionSoulHumanismTask (project management)Cognitive psychologyDevelopmental psychologyArtificial intelligenceComputer scienceSocial psychologySociologyEngineeringEpistemologyAnthropology

Abstract

fetched live from OpenAlex

Centuries ago, the existence of life was explained by the presence of a soul [1]. Known as animism, this term was re-defined in the 1970s by Piaget as young children's beliefs that inanimate objects are capable of actions and have lifelike qualities. With the development of robots in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, researchers have yet to examine whether animism is apparent in children's impressions of robots. The purpose of this study was to examine children's perspectives about the cognitive, affective, and behavioral attributes of a robot. Visitors to a science centre located in a major Western Canadian city were invited to participate in an experiment set up at the centre. A total of 198 children ages 5 to 16 years (M = 8.18 years) with an approximate even number of boys and girls participated. Children were interviewed after observing a robot, a small 5 degree of freedom robot arm, perform a block stacking task. Answers to the six questions about the robot were scored according to whether they referenced humanistic qualities. Frequency and content analysis results suggest that a significant proportion of children ascribe cognitive, affective, and behavioral characteristics to robots.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.012
GPT teacher head0.303
Teacher spread0.291 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations5
Published2010
Admission routes2
Has abstractyes

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