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Record W3198374043 · doi:10.1167/jov.21.9.2446

Tracing the emergence of stimulus memorability

2021· article· en· W3198374043 on OpenAlexaff
Greer Gillies, Hyun Park, Dirk B. Walther, Jonathan S. Cant, Keisuke Fukuda

Bibliographic record

VenueJournal of Vision · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsStimulus (psychology)Working memoryPsychologyCognitive psychologySet (abstract data type)CognitionTask (project management)Computer scienceNeuroscience

Abstract

fetched live from OpenAlex

Some visual stimuli are consistently better remembered than others across individuals. This is due to variations in stimulus-intrinsic properties that determine ease of access into visual long-term memory (VLTM). Though memorability has been demonstrated in multiple stimulus domains, it remains an open question where memorability emerges or what cognitive processes give rise to it. As memorability cannot be attributed to low-level visual features, attentional saliency, or voluntary memory control (Bainbridge, 2020), we tested the hypothesis that memorability emerges within visual working memory (VWM). Specifically, do memorable stimuli require fewer resources to be maintained in VWM? If so, more memorable faces should be retained in VWM than forgettable faces. To test this, we had participants perform a standard VWM task with arrays of 3 or 6 face stimuli that were previously classified as “memorable” and “forgettable” (Bainbridge et al., 2013). VWM performance was better in the set size 3 condition compared to set size 6, with an additional benefit for memorable faces, supporting our hypothesis that memorable faces require fewer resources to be maintained in VWM. Interestingly, when memorable faces were presented alongside forgettable ones (e.g., 3 memorable and 3 forgettable faces), memory for the memorable items improved, suggesting that memorable items pull resources away from other items competing for representation. Next, to examine whether memorability can be fully captured within VWM, we had participants perform a VLTM recognition task after completing the VWM task. Here, we found that the memorability effect overrode the array size effect such that memorable faces encoded in 6-face arrays were better recognized than forgettable faces encoded in 3-face arrays. This suggests that not only are memorable stimuli treated differently within VWM, they are also “stickier” than forgettable stimuli, showing less memory decay. Together, our results demonstrate that stimulus memorability emerges over multiple stages of memory.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.148
GPT teacher head0.427
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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