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Record W2902559113 · doi:10.1037/cep0000156

Intentions and actions.

2018· review· en· W2902559113 on OpenAlexafffund
Michael E. J. Masson

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2018
Typereview
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAction (physics)Mental representationTask (project management)Observer (physics)GRASPContext (archaeology)Object (grammar)PsychologyPsycINFORepresentation (politics)Orientation (vector space)Selection (genetic algorithm)Cognitive psychologyHierarchyComputer scienceProcess (computing)Cognitive scienceCommunicationSocial psychologyArtificial intelligenceCognitionMathematicsPoliticsNeuroscience

Abstract

fetched live from OpenAlex

Manipulable objects have the potential to evoke mental representations of hand actions. Behavioural evidence favouring the view that this process happens automatically while passively viewing objects is critically examined. A case is made for the alternative proposal that objects may evoke action representations when observers concurrently operate with an intention to engage in a reach-and-grasp action. In addition, the nature of hand action representations was examined by considering two components of actions, hand selection and wrist orientation, and it is shown that the relationship between these dimensions is modulated by task context. When an action representation is evoked by a task-irrelevant object, these two dimensions are to a large extent independent of one another, but when an observer prepares an action for immediate production, these two action features are hierarchically integrated, with hand selection dominating the hierarchy. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.006
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.004

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 head0.191
GPT teacher head0.424
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2018
Admission routes2
Has abstractyes

Explore more

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