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Influence of object size on baseline identification, priming, and explicit memory

2007· article· en· W2032033371 on OpenAlexaff
Bob Uttl, Peter Graf, Amy L. Siegenthaler

Bibliographic record

VenueScandinavian Journal of Psychology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsImplicit memoryPsychologyObject (grammar)Priming (agriculture)PerceptionRecognition memoryCognitive psychologyIdentification (biology)Line drawingsContrast (vision)Test (biology)Explicit memoryCognitive neuroscience of visual object recognitionArtificial intelligenceCognitionComputer scienceEpisodic memory

Abstract

fetched live from OpenAlex

We investigated the influence of size on identification, priming, and explicit memory for color photos of common objects. Participants studied objects displayed in small, medium, and large sizes and memory was assessed with both implicit identification and explicit recognition tests. Overall, large objects were easier to identify than small objects and study-to-test changes in object size impeded performance on explicit but not implicit memory tests. In contrast to previous findings with line-drawings of objects but consistent with predictions from the distance-as-filtering hypothesis, we found that study-test size manipulations had large effects on old/new recognition memory test for objects displayed in large size at test but not for objects displayed small or medium at test. Our findings add to the growing body of literature showing that the findings obtained using line-drawings of objects do not necessarily generalize to color photos of common objects. We discuss implications of our findings for theories of object perception, memory, and eyewitness identification accuracy for objects.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.039
GPT teacher head0.357
Teacher spread0.318 · 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

Citations16
Published2007
Admission routes1
Has abstractyes

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