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Record W1987612265 · doi:10.1086/660853

The Dynamics of Goal Revision: A Cybernetic Multiperiod Test-Operate-Test-Adjust-Loop (TOTAL) Model of Self-Regulation

2011· article· en· W1987612265 on OpenAlex
Chen Wang, Anirban Mukhopadhyay

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

VenueJournal of Consumer Research · 2011
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSatisficingCyberneticsMaximizationFunction (biology)Computer scienceTest (biology)Monotonic functionDynamics (music)System dynamicsSensitivity (control systems)Management scienceArtificial intelligencePsychologyMathematical optimizationMathematicsEconomicsEcologyEngineering

Abstract

fetched live from OpenAlex

Abstract This research presents a comprehensive conceptual model of the dynamics of goal revision over multiple periods. First, based on an integrative literature review, we derive four principles that govern how individuals update their goals over time (monotonicity, diminishing sensitivity, aspiration maximization, and performance satisficing). We then integrate these principles logically as well as mathematically into a goal-discrepancy response function. Next, we advance existing cybernetic models of self-regulation by synthesizing the four principles and the response function into a Test-Operate-Test-Adjust-Loop (TOTAL) model, which captures the dynamics of goal revision in self-regulation. We report four laboratory experiments that demonstrate initial support for the postulates of our model and conclude with a discussion of limitations and future directions.

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)0.006
metaresearch head score (Gemma)0.002
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 score0.558
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.151
GPT teacher head0.438
Teacher spread0.286 · 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