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Record W4414465542 · doi:10.1037/xhp0001373

Contributions of action representations to tool naming.

2025· article· en· W4414465542 on OpenAlexafffund
Daniel N. Bub, Noah Moise, Michael E. J. Masson

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2025
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAction (physics)Object (grammar)Task (project management)Orientation (vector space)Dimension (graph theory)Feature (linguistics)LateralitySequence (biology)Semantics (computer science)

Abstract

fetched live from OpenAlex

We present new evidence on the role that action representations play in the speeded naming of tools/utensils. In a series of experiments (minimum sample size = 30), participants held in working memory (WM) a sequence of two hand actions, both of which involved a particular hand (left or right) and orientation of the wrist (horizontal or vertical). While under this load, participants named objects that had a horizontal or vertical handle aligned to the left or right. Naming time was elevated when the WM load and the object's handle were congruent on one dimension (hand or orientation) but incongruent on the other, relative to when both dimensions were congruent or incongruent. We assumed that features of the action sequence in WM, including the laterality and wrist orientation of the hand postures, are bound together. If just one of these features (say, hand laterality) is recapitulated in the object presented for naming, a mismatching feature (in this instance, wrist orientation) would automatically be retrieved from WM. The resulting conflict induces a delay in the naming response (partial-repetition cost). No such effect was observed when the task required a decision about the upright/inverted status or the semantic category of an object (i.e., whether the tool/utensil is typically found in a kitchen or garage). Furthermore, no partial-repetition cost occurred on a speeded reach-and-grasp action afforded by the handle of a depicted object. We infer that the effect of action features in WM occurred because motor representations were directly consulted for name retrieval. (PsycInfo Database Record (c) 2026 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.421

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 communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.433
Teacher spread0.404 · 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 designBench or experimental
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
Published2025
Admission routes2
Has abstractyes

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