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Record W3021468410 · doi:10.1371/journal.pone.0232409

Similar social presence effects when reaching for real and digital objects

2020· article· en· W3021468410 on OpenAlexafffund
Jill A. Dosso, Katherine Nga Tsit Chow, Jane J. Kim, Natalie T. W. Wong, Alan Kingstone

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsObject (grammar)Observer (physics)Computer visionArtificial intelligenceComputer scienceSpace (punctuation)Matching (statistics)Image (mathematics)PsychologyCognitive psychologyCommunicationMathematicsPhysicsStatistics

Abstract

fetched live from OpenAlex

Though digital images and real objects are represented differently at a neural level and can evoke different behaviours, little work has directly compared the magnitude of social effects on real and digitally represented stimuli. Object-directed reaches are modified in the near space of others, while image-directed reaches are not, but the exact role of the presence versus location of the other person is unknown (Dosso and Kingstone, 2018). The present work probed the unique contribution of social presence (a passive observer) in shaping object- and image-directed reaching behaviour. In a shape-matching game, movements were performed more slowly and less efficiently when participants were observed by the experimenter, regardless of whether participants handled real objects or digital images. Our finding that social presence affects real- and image-directed reaches similarly supports the continued use of computer-generated objects to approximate human behaviour towards real objects when social effects on object-directed actions are studied.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.293

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.289
Teacher spread0.181 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2020
Admission routes2
Has abstractyes

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