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Record W2059708726 · doi:10.1037/h0087450

Acquiring a Cognitive Skill With a New Repeating Version of the Tower of London Task.

2004· article· en· W2059708726 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

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2004
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsPsychologyTask (project management)CognitionSequence learningCognitive psychologySequence (biology)Tower

Abstract

fetched live from OpenAlex

A computerized version of the Tower of London task was used to investigate cognitive skill learning. Thirty-six healthy volunteers were assigned to either a random condition (nonrecurring problems), or to a sequence condition in which, unbeknownst to the subjects, a repeating sequence of three problems was presented. Indices of execution, planning, and total time, as well as number of moves performed, were used to measure behavioural change. Subjects' performance improved in both conditions across blocks of practice. A distinct learning effect related to the repeating sequence was also observed. This suggests that a specific skill that reflects procedural learning of the strategies, rules, and procedures pertaining to repeating problems can develop over and above a more general skill at solving cognitive planning problems with practice.

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)0.001
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.304
Teacher spread0.274 · 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