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Record W2950211537 · doi:10.3758/s13421-019-00954-0

Visual short-term memory capacity predicts the “bandwidth” of visual long-term memory encoding

2019· article· en· W2950211537 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

VenueMemory & Cognition · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of Toronto
FundersNational Institute of Mental HealthOffice of Naval Research
KeywordsVisual short-term memoryEncoding (memory)Visual memoryIconic memoryPsychologyShort-term memoryENCODESensory memoryLong-term memoryWorking memoryCognitive psychologyComputer scienceCommunicationCognitionNeuroscience

Abstract

fetched live from OpenAlex

We are capable of storing a virtually infinite amount of visual information in visual long-term memory (VLTM) storage. At the same time, the amount of visual information we can encode and maintain in visual short-term memory (VSTM) at a given time is severely limited. How do these two memory systems interact to accumulate vast amount of VLTM? In this series of experiments, we exploited interindividual and intraindividual differences VSTM capacity to examine the direct involvement of VSTM in determining the encoding rate (or "bandwidth") of VLTM. Here, we found that the amount of visual information encoded into VSTM at a given moment (i.e., VSTM capacity), but neither the maintenance duration nor the test process, predicts the effective encoding "bandwidth" of VLTM.

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 categoriesMeta-epidemiology (narrow)
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 score0.041
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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