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Record W2044908436 · doi:10.1111/cogs.12115

The Effects of Prior Learned Strategies on Updating an Opponent's Strategy in the Rock, Paper, Scissors Game

2014· article· en· W2044908436 on OpenAlex
Elisabeth Stöttinger, Alex Filipowicz, James Danckert, Britt Anderson

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCognitive Science · 2014
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAdversaryComputer scienceCognitive psychologyHuman–computer interactionPsychologyArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

To explore how model building adapts to changing environments, we had participants play "rock-paper-scissors" against a computer that played a frequency bias or a player-dependent bias and then switched. Participants demonstrated their use of prior experience in how quickly they recognized and exploited changes in the computer's play strategy; in general, the more similar the strategies, the more efficient the updating. These findings inform our understanding of previously reported updating impairments in right-brain damaged patients.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
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 head0.043
GPT teacher head0.362
Teacher spread0.319 · 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