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Record W4402946680 · doi:10.1167/jov.24.10.1197

Flexible memory interplays: selective reactivation of long-term memories in working memory

2024· article· en· W4402946680 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

VenueJournal of Vision · 2024
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTerm (time)Working memoryLong-term memoryComputer scienceCognitive psychologyPsychologyNeurosciencePhysicsCognition

Abstract

fetched live from OpenAlex

Working memory is defined as the online storage space for ongoing tasks. It stores both newly encoded information and retrieved long-term memories. However, there is a growing amount of work to suggest that long-term memories can also guide behavior. This raises the question: Why do humans invest metabolic resources in reactivating long-term memories in working memory instead of guiding behavior directly via long-term memory? We conducted six experiments examining working memory reactivation of long-term memories in anticipation of task demands encompassing protection against interference, behavioral guidance, and adaptation to novel settings. Using behavioral and electrophysiological indices, we measured the extent to which long-term memories are reactivated in working memory in anticipation of these task demands relative to the anticipation of a recognition task, which constituted a baseline. Compared to this baseline, we found equal memory reactivation when anticipating perceptual interference and dual-task interference, and less memory reactivation when anticipating attentional guidance. On the other hand, reactivation was stronger for task switching, contextual changes, and performing mental operations. These results suggest that the reactivation of long-term memories in working memory is not primarily for protection against interference or behavioral guidance. Instead, stronger reactivation occurs when there is a need to update the memories themselves (i.e., perform a mental operation) or the settings in which they are used (i.e., the task rules and the context). This insight implies that the goal of reactivating long-term memories in working memory may be to facilitate adaptation to novel situations. Our research challenges influential memory models and recent empirical work that consider working memory as the default buffer for retrieved long-term memories and instead highlights a flexible and dynamic interplay between long-term memories and working 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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.360
Teacher spread0.329 · 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