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Record W4393054287 · doi:10.1080/13506285.2024.2315803

Target recognition and lure rejection: Two sides of the same memorability coin?

2023· article· en· W4393054287 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 · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersDivision of Behavioral and Cognitive SciencesNational Science Foundation of Sri LankaNational Institutes of Health
KeywordsPsychologyCommunicationCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

The human brain has a massive storage capacity for remembering visual information, but certain objects appear to be more likely to be remembered than the others across observers. Here, we tested a new possible explanation for the differential memorability of objects. The explanation states that certain objects are more memorable due to sheer frequency of encounter. We had a group of observers provide subjective frequency estimates for the objects in our stimulus set. We found that items that observers judged as less frequently seen were easier to reject as new items, but did not correlate with which items were more likely to garner hit responses when they were old. In summary, our findings suggest that memorability may be a multifaceted construct, with different aspects of the memoranda driving different component judgements that observers make.

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: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.211

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 head0.061
GPT teacher head0.366
Teacher spread0.305 · 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