MétaCan
Menu
Back to cohort
Record W2033715141 · doi:10.1080/09658210344000413

Forgetting and redintegration of consonants and vowels in pseudoword lists

2004· article· en· W2033715141 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

VenueMemory · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsDalhousie UniversityQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsPseudowordVowelConsonantRedundancy (engineering)RecallPsychologySpeech recognitionMotivated forgettingCognitive psychologyComputer scienceCognition

Abstract

fetched live from OpenAlex

Immediate recall of phonemes was studied in a pseudoword span task. Finnish participants recalled lists of increasing length, consisting of C(consonant)V(vowel)CVCV pseudowords. The lists were made up from pools of 12 pseudowords. There were three types of lists. In the non-redundant lists the items were unpredictable combinations of consonants and vowels. In consonant-redundant lists, all items had the consonant frame /t/_/s/_/l/. In vowel-redundant lists, all items had the vowel frame _/u/_/e/_/o/. Unlike redundant last syllables in a previous experiment, neither consonant nor vowel redundancy helped list recall. Instead, a harmful phonological similarity effect was apparent in the vowel-redundant case but not the consonant-redundant case. A phoneme-level analysis of recall showed that consonants were recalled better in consonant-redundant lists and vowels were recalled better in vowel-redundant lists compared to non-redundant lists. Vowels appeared to be more important for discrimination between items, with redundancy resulting in confusions. The consequences of phoneme-level forgetting and redintegration for item- and list-level recall are discussed.

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)0.000
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 score0.008
Threshold uncertainty score0.186

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 head0.020
GPT teacher head0.269
Teacher spread0.249 · 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