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Examining the Specificity of Practice Hypothesis: Is Learning Modality Specific?

2001· article· en· W1987064587 on OpenAlex
Jamie Coull, Luc Tremblay, Digby Elliott

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

VenueResearch Quarterly for Exercise and Sport · 2001
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsModality (human–computer interaction)Task (project management)Cognitive psychologyPsychologyDreyfus model of skill acquisitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The specificity of practice hypothesis was examined using a tracking task. In Experiment 1, visual or auditory feedback about performance was provided. Vision was more useful than audition early in acquisition. Performance gains found in acquisition were maintained during retention, but learning was specific only if the acquisition modality was visual. Specificity did not increase with the amount of practice. In Experiment 2, visual and auditory information were combined. Again, the specificity of practice hypothesis was supported. Also, instructing participants to attend to one information source allowed us to demonstrate that information can be explicitly or implicitly processed. Further, specificity effects may occur because of different rates of development for error detection and correction processes.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.161
GPT teacher head0.350
Teacher spread0.189 · 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