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Record W7025209853

"This is driving me dotty": a new experimental method for studying sequential statistical learning

2021· dissertation· en· W7025209853 on OpenAlexaboutno aff

Bibliographic record

VenueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2021
Typedissertation
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsSequence learningEncoding (memory)Sequence (biology)Task (project management)Process (computing)Statistical learningMechanism (biology)CognitionStatistical modelPattern recognition (psychology)
DOInot available

Abstract

fetched live from OpenAlex

This thesis introduces a new experimental paradigm for exploring the cognitive mechanism of statistical learning (SL). SL refers to the ability of extracting statistical regularities and patterns implicitly from the sensory input and forming them into units of knowledge. Most of the methodologies used in the literature to investigate or assess the mechanism of SL, use tasks and measurements that assess the outcome of the learning process, rather than the process itself. Therefore, the new experimental paradigm introduced in this thesis, provides a new way of observing the SL mechanism while it operates, with the usage of a gaze contingent/time-displayed eye-tracking sequential SL task (on the visual domain). Once the new methodology is introduced and assessed across two different eye-trackers (Gazepoint GP3; EyeLink 1000 (SR Research Ltd., Mississauga, Canada)), it is applied to specific research contexts about the encoding process of sequences in SL and the effects of sequence length and mixtures of sequences lengths in sequential SL. The findings of this thesis support evidence for a hierarchical structure in the SL encoding process, where the last item of a sequence is better learned than the previous and so on. Additionally, it is suggested that sequences with shorter lengths (2 items) are learned faster than longer length sequences (4 items) in same length tasks. However, when the sequential SL occurs within mixed length sequences, longer sequences are facilitated from their coexistence with shorter sequences in the task, resulting in faster learning, while learning of shorter sequences is impeded by their coexistence with longer sequences in the task. A contextual evaluation of the two eye-tracking systems used in this thesis is described to justify the usage of low-cost equipment in experimental research. Finally, a critical discussion of the findings and its applications on educational and clinical areas is given.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.032
GPT teacher head0.348
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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