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Record W3215870652 · doi:10.1002/eat.23646

Explaining reinforcement and erroneous beliefs in pathological exercise: A commentary and expansion on Coniglio et al. (in press) using the pathways model of disordered gambling

2021· article· en· W3215870652 on OpenAlexaff
Nassim Tabri, Michael J. A. Wohl

Bibliographic record

VenueInternational Journal of Eating Disorders · 2021
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsCarleton University
Fundersnot available
KeywordsReinforcementPsychologyPathologicalReinforcement learningAnorexia nervosaCognitive psychologyMechanism (biology)Developmental psychologySocial psychologyClinical psychologyMedicineEating disorders

Abstract

fetched live from OpenAlex

Coniglio, Cooper, and Selby proposed that behavioral reinforcement may be critical for understanding the etiology and maintenance of pathological exercise among people living with anorexia nervosa. They presented three competing hypotheses about why exercise can become problematic: (a) positive reinforcement via biological and behavioral rewards, (b) negative reinforcement via avoidance of aversive states, or (c) a synergistic interplay between positive and negative reinforcement. Herein, we extend Coniglio and colleagues' framework by drawing on theory and research from the field of disordered gambling-a behavior in which reinforcement is an etiological and maintaining mechanism. We applied the pathways model of disordered gambling to the study of pathological exercise and made the following two proposals. First, pathological exercise may be driven by positive reinforcement, negative reinforcement, or both (they are not mutually exclusive), depending on the presence or absence of specific co-occurring psychopathologies. Second, erroneous beliefs about the safety and efficacy of maladaptive exercise for weight control may help maintain pathological exercise regardless of the type of reinforcement. We conclude by calling for research that assesses Coniglio and colleagues' novel hypotheses and our supposition that the pathways model can help provide a framework for those hypotheses.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.067
GPT teacher head0.356
Teacher spread0.289 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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