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Learning to become an expert: reinforcement learning and the acquisition of perceptual expertise

2011· article· en· W938683852 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

VenueAnnals of Neurosciences · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceReinforcement learningPerceptionReinforcementArtificial intelligencePerceptual learningHuman–computer interactionPsychologyNeuroscienceSocial psychology

Abstract

fetched live from OpenAlex

sensitive to processing of feedback) elicited over the frontalcentral region.Only the high learners learned to identify the learnable blobs that resulted in increase in the amplitudes of N250.Most importantly, with more training, the high learners developed the ability to evaluate the correctness of their responses while being less dependent on the external feedback.This was reflected as increase in amplitude of response ERN that preceded the enhancement of N250.In addition, there was a corresponding decrease in feedback ERN.The results suggested that the improvement in the categorization task was preceded by enhancement in the ability to evaluate the correctness of one's responses which reduced the dependence on any external feedback.Interestingly, no such effect was observed for the morph blobs.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.342
GPT teacher head0.441
Teacher spread0.099 · 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