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Record W2759149419 · doi:10.1037/xlm0000453

Grasp representations depend on knowledge and attention.

2017· article· en· W2759149419 on OpenAlexafffund
Kao-Wei Chua, Daniel N. Bub, Michael E. J. Masson, L. Gauthier

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2017
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyObject (grammar)Action (physics)GRASPCognitive psychologyOrientation (vector space)EvocationCommunicationDual (grammatical number)CognitionCognitive scienceArtificial intelligenceComputer scienceLinguisticsNeuroscience

Abstract

fetched live from OpenAlex

Seeing pictures of objects activates the motor cortex and can have an influence on subsequent grasping actions. However, the exact nature of the motor representations evoked by these pictures is unclear. For example, action plans engaged by pictures could be most affected by direct visual input and computed online based on object shape. Alternatively, action plans could be influenced by experience seeing and grasping these objects. We provide evidence for a dual-route theory of action representations evoked by pictures of objects, suggesting that these representations are influenced by both direct visual input and stored knowledge. We find that that familiarity with objects has a facilitative effect on grasping actions, with knowledge about the object's canonical orientation or its name speeding grasping actions for familiar objects compared to novel objects. Furthermore, the strength of contributions from each route to action can be modulated by the manner in which the objects are attended. Thus, evocation of grasping representations depends on an interaction between one's familiarity with perceived objects and how those objects are attended while making grasp actions. (PsycINFO Database Record

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.698

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.423
Teacher spread0.354 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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