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Record W4297887674 · doi:10.31234/osf.io/9b2fj

Sensory delay activity: more than an electrophysiological index of working memory load

2022· preprint· en· W4297887674 on OpenAlexaff
Stephen M. Emrich, Christine Salahub, Tobias Katus

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity Health NetworkBrock University
Fundersnot available
KeywordsWorking memoryPsychologySensory systemCognitionElectrophysiologyPerspective (graphical)Cognitive psychologyPerceptionCognitive resource theoryCognitive loadSensory memoryElectroencephalographyNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Sustained contralateral delay activity (CDA) emerges in the retention period of working memory (WM) tasks and has been commonly interpreted as an electrophysiological index of the number of items held in a discrete-capacity working memory resource. More recent findings indicate that these visual and tactile components are sensitive to various cognitive operations beyond the storage of discrete items in WM. In this Perspective, we present recent evidence from unisensory and multisensory visual and tactile WM tasks which suggest that, in addition to memory load, sensory delay activity may also be indicative of attentional and executive processes, as well as reflecting the flexible, rather than discrete, allocation of a continuous working memory resources. Together, these findings challenge the traditional model of the functional significance of the CDA as a pure measure of item load, and suggest that it may also reflect executive, attentional, and perceptual mechanisms operating in hierarchically organized WM systems.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
GPT teacher head0.394
Teacher spread0.137 · 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
Published2022
Admission routes1
Has abstractyes

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