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Record W4402776680 · doi:10.1027/1618-3169/a000620

Which Encoding Techniques Facilitate Comprehension?

2024· article· en· W4402776680 on OpenAlexaff
Sophia Tran, Myra A. Fernandes

Bibliographic record

VenueExperimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComprehensionEncoding (memory)Reading comprehensionRepetition (rhetorical device)Cognitive psychologyReading (process)ParaphrasePerceptionPsychologyCognitionComputer scienceContrast (vision)LinguisticsNatural language processingArtificial intelligence

Abstract

fetched live from OpenAlex

Previous work suggests that similar cognitive processes contribute to memory and comprehension. This is unsurprising as both begin with a common process: encoding. Despite this, the investigation of techniques that benefit memory and comprehension has proceeded separately. In the current study, we compared the robust memory techniques of production and drawing to a similarly effective comprehension strategy known as paraphrasing. Depending on the group, participants were asked to either engage in one of the encoding types (read aloud, draw, or paraphrase) or to silently read 20 term-definition pairs (randomly intermixed and counterbalanced). The encoding techniques of drawing and paraphrasing resulted in better performance on a multiple-choice test of concept comprehension, relative to silently reading. By contrast, reading aloud at encoding did not lead to any benefit relative to silently reading. The results suggest that techniques that invoke transformation of the to-be-remembered text into another format, be it into a picture (drawing) or personally relevant summary (paraphrasing), are particularly effective at improving comprehension. By contrast, encoding techniques that mainly provide a perceptual repetition (production and silent reading) are less effective.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.504
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.104
GPT teacher head0.422
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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