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Record W1993262000 · doi:10.1080/02702710601186407

Compensating for a Limited Working Memory Capacity During Reading: Evidence from Eye Movements

2007· article· en· W1993262000 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

VenueReading Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorking memoryPsychologyCognitive psychologyReading (process)ComprehensionEye movementTask (project management)Reading comprehensionShort-term memoryCognitive scienceCognitionLinguisticsNeuroscience

Abstract

fetched live from OpenAlex

Although working memory capacity is an important contributor to reading comprehension performance, it is not the only contributor. Studies have shown that epistemic knowledge (or knowledge about knowledge and learning) is related to comprehension success and may enable low-span readers to compensate for their limited resources. By comparing the eye movements of epistemically mature versus epistemically naïve low-span readers, this study provided evidence for how the compensation occurs. Metacognitively mature low-span readers spent more time engaged in selective backtracking to unfamiliar and task-relevant text information. These selective look-backs would have reinstated the difficult and important information into working memory, thereby allowing these readers to offset some of the disadvantages of a limited temporary storage capacity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.179
GPT teacher head0.418
Teacher spread0.239 · 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