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Record W7115817180

Effect of prosody on word list recall

2023· dissertation· en· W7115817180 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsRecallProsodySerial position effectStress (linguistics)Recall testTask (project management)Free recall
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines the effects of prosody on serial recall. Serial recall is an experimental task commonly used to evaluate the capacity of short-term memory. The Working Memory model by Baddeley and Hitch is a theoretical framework that describes the inner operation of short-term memory. Its hierarchies are supported by empirical evidence, but details of the core mechanisms remain unclear. In an attempt to refine the framework, this thesis investigated prosody as a factor in serial recall accuracy. Two behavioural experiments were conducted on native speakers of Canadian English. In the first experiment, the explicit awareness of word stress was examined. Results showed a main effect of word stress type, where iambic words received higher stress identification scores compared to trochaic words. In the second experiment, an immediate serial recall task was used to examine serial recall of word lists. The lists consisted of disyllabic words from Canadian English sources. The lists had mixed or uniform stress patterns. A main effect of list stress patterns was found, where mixed lists elicited better recall of the order of list items compared to uniform lists. Overall, the present thesis offers a new interpretation on how word stress is represented in the short-term memory. It adds support to the proposed interaction between short-term and long-term memory.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.021
GPT teacher head0.254
Teacher spread0.233 · 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.

Study designOther design
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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