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On the representation of words and nonwords in visual short‐term memory: Evidence from human electrophysiology

2008· article· en· W2157882503 on OpenAlexaff
David Predovan, David Prime, Martin Arguin, Frédéric Gosselin, Roberto Dell’Acqua, Pierre Jolicœur

Bibliographic record

VenuePsychophysiology · 2008
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyElectrophysiologyCognitive psychologyRepresentation (politics)Negativity effectVisual short-term memoryWord (group theory)Encoding (memory)CommunicationNeuroscienceVisual memoryLinguisticsCognition

Abstract

fetched live from OpenAlex

Electrophysiological measures were used to investigate the contribution of lexical status on the maintenance of letter strings in visual short-term memory (VSTM). The sustained posterior contralateral negativity (SPCN), an electrophysiological marker of storage in VSTM, was measured for words and nonwords as well as scrambled letters. A smaller SPCN was found for words than for nonwords (independently of their pronounceability), indicating that lexical status influences storage in VSTM. One possibility is that words produce a smaller SPCN because they can be recoded to a form that does not require a low-level representation in VSTM. For exploratory purpose, a comparison between the nonwords and the scrambled nonwords was also made. Based on previous research, the SPCN component should not be affected by the size of the region enclosing to-be-encoded objects. Surprisingly, significant differences between the SPCN for nonwords and scrambled letters conditions were found, suggesting that special encoding mechanisms may be recruited to encode word-like letter strings.

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 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 categoriesnone
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 score0.135
Threshold uncertainty score0.449

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.063
GPT teacher head0.357
Teacher spread0.294 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2008
Admission routes1
Has abstractyes

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