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Record W4297229778 · doi:10.1101/2022.09.23.509245

Neural population dynamics of human working memory

2022· preprint· en· W4297229778 on OpenAlexfundno aff
Hsin-Hung Li, Clayton E. Curtis

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsnot available
FundersNational Eye InstituteYork University
KeywordsReceptive fieldWorking memoryNeurosciencePopulationDynamics (music)SaccadeFovealVisual cortexSensory systemCortex (anatomy)PsychologyComputer scienceCognitive psychologyEye movementCognitionBiology

Abstract

fetched live from OpenAlex

Abstract Temporally evolving neural processes maintain working memory (WM) representations of information no longer available in the environment. Nonetheless, the dynamics of WM remain largely unexplored in the human cortex. With fMRI, we found evidence of both stable and dynamic WM representations in human cortex during a memory-guided saccade task. The stability of WM varied across brain regions with early visual cortex exhibiting the strongest dynamics. Leveraging population receptive field modeling, we visualized and made the neural dynamics interpretable. Early in the trial, neural responses in V1 were dominated by narrowly tuned activation at the location of the peripheral target. Over time, activity spread toward foveal locations and targets were represented by diffuse activation among voxels with receptive fields along a line between the fovea and the target. We suggest that the WM dynamics in early visual cortex reflects a transformation of sensory inputs into abstract task-related representations.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.243
Teacher spread0.215 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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