On the representation of words and nonwords in visual short‐term memory: Evidence from human electrophysiology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".