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Record W3207783335 · doi:10.31234/osf.io/kd29q

Memoir Dataset: Quantifying Image Memorability in Adolescents

2021· preprint· en· W3207783335 on OpenAlexafffund
Gal Almog, Saeid Alavi Naeini, Emma G. Duerden, Yalda Mohsenzadeh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsWestern University
FundersCanada First Research Excellence Fund
KeywordsPsychologySet (abstract data type)Image (mathematics)Property (philosophy)Stimulus (psychology)MemoirCognitive psychologyComputer scienceArtificial intelligenceArt

Abstract

fetched live from OpenAlex

Recent work shows that images differ in terms of their memorability, some stick in one's mind while others are forgotten quickly, and this phenomenon is consistent across observers. Previous research agrees that memorability is an intrinsic, continuous property of a stimulus that can be both measured and manipulated, however; this work has been limited to adults.In this study, we attempted to quantify image memorability in adolescents (11-18 years old) and determine if it differs from image memorability in adults.We selected images from the MemCat data set that are annotated with adult memorability scores. By running a visual memory game online, we quantified these images on their memorability in adolescents, and compared them to the adult scores. Our study finds that memorability rankings in adolescents and adults are strongly and significantly correlated. Moreover, the rankings of memorability by image category were also identical in both age groups, indicating that certain image categories are more consistently memorable for both adults and adolescents.Collectively, our results support previous research that suggests memorability is an intrinsic property of images that is consistent across viewers even with different developmental stages.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.733
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.001
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 head0.073
GPT teacher head0.355
Teacher spread0.282 · 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 designSimulation or modeling
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

Citations5
Published2021
Admission routes2
Has abstractyes

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