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Record W2093773434 · doi:10.1080/13506285.2014.887042

Slow categorization but fast naming for photographs of manipulable objects

2014· article· en· W2093773434 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVisual Cognition · 2014
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCategorizationObject (grammar)PsychologyTask (project management)Set (abstract data type)Action (physics)Identification (biology)Cognitive psychologyDuration (music)CommunicationCognitive neuroscience of visual object recognitionArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Previous research investigating the influence of object manipulability (the properties of objects that make them appropriate for manual action) on object identification has not tightly controlled for effects of both object familiarity and age of acquisition of objects. The current research carefully controlled these two variables on a balanced set of 120 photographs and showed significant effects of object manipulability during object categorization (Experiment 1) and object naming (Experiment 2). Critically, the effects showed a manipulability-effect reversal, with faster categorization of non-manipulable objects, but faster naming of manipulable objects, suggesting that task moderates the direction of the manipulability effect. Exposure duration (the amount of time the object was visible to participants) was also investigated, but no interactions between exposure duration and manipulability were found. These results indicate that not only can manipulability influence object identification, but the way in which it does depends on the task.

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.

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: none
Teacher disagreement score0.622
Threshold uncertainty score0.593

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)0.0010.000

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 head0.034
GPT teacher head0.325
Teacher spread0.291 · 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