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Record W1601850165 · doi:10.1080/17470218.2015.1061029

The sound of you and me: Novices represent shared goals in joint action

2015· article· en· W1601850165 on OpenAlexaff
Janeen D. Loehr, Cordula Vesper

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMelodyAction (physics)PsychologyPerceptionCognitive psychologyContext (archaeology)Joint attentionMental representationMusicalCommunicationCognitionDevelopmental psychologyAutism

Abstract

fetched live from OpenAlex

People performing joint actions coordinate their individual actions with each other to achieve a shared goal. The current study investigated the mental representations that are formed when people learn a new skill as part of a joint action. In a musical transfer-of-learning paradigm, piano novices first learned to perform simple melodies in the joint action context of coordinating with an accompanist to produce musical duets. Participants then performed their previously learned actions with two types of auditory feedback: while hearing either their individual action goal (the melody) or the shared action goal (the duet). As predicted, participants made more performance errors in the individual goal condition than in the shared goal condition. Further experimental manipulations indicated that this difference was not due to different coordination requirements in the two conditions or perceptual dissimilarities between learning and test. Together, these findings indicate that people form representations of shared goals in contexts that promote minimal representations, such as when learning a new action together with another person.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.134
GPT teacher head0.428
Teacher spread0.294 · 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 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

Citations37
Published2015
Admission routes1
Has abstractyes

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