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Record W2016199997 · doi:10.2466/pms.104.3.758-762

Recognition Memory for Concrete, Regular Abstract, and Diverse Abstract Pictures

2007· article· en· W2016199997 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

VenuePerceptual and Motor Skills · 2007
Typearticle
Languageen
FieldEngineering
TopicRobotics and Automated Systems
Canadian institutionsBishop's University
Fundersnot available
KeywordsCognitive psychologyPsychologyCognitive scienceComputer science

Abstract

fetched live from OpenAlex

Based on previous research by Goldstein and Chance in which poor recognition memory for abstract visual patterns was reported, this study compared recognition memory for pictures of everyday concrete objects, regular abstract stimuli as employed by Goldstein and Chance, and diverse abstract stimuli. A (3) x 2 design (stimulus type x test order) analysis of variance design was used. The subjects (N = 31) first viewed 30 target stimuli, followed by an immediate recognition test in which for 30 paired target and distractor stimuli shown they indicated which one they had seen previously. Concrete pictures were recognized with near perfect accuracy, and above the level for diverse abstract pictures; these in turn were better identified than regular abstract items, on which performance resembled that found by Goldstein and Chance. It is concluded that stimulus discriminability, rather than representational meaningfulness, may be crucial in picture recognition.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score0.519

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)0.0000.000

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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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