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Record W155991542 · doi:10.12794/metadc271888

Using Progressive Ratio Schedules to Evaluate Edible, Leisure, and Token Reinforcement

2013· dissertation· en· W155991542 on OpenAlexaff
Danielle M. Russell

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsReinforcementSecurity tokenPsychologyComputer scienceSocial psychologyComputer network

Abstract

fetched live from OpenAlex

The general purpose of the current study was to evaluate the potency of different categories of reinforcers with young children diagnosed with developmental delays. The participants were two boys and one girl who were between the ages of seven and eight. In Phase 1, we evaluated the reinforcing potency of tokens, edible items, and leisure items by using a progressive ratio (PR) schedule. For two participants, we found that tokens resulted in the highest PR break points. For one participant, edibles resulted in the highest break points (tokens were found to have the lowest break points). In Phase 2, we evaluated the effects of presession access on the break points of edibles and tokens. This manipulation served as a preliminary analysis of the extent to which tokens might function as generalized conditioned reinforcers. During Phase 2, presession access altered the break points of edibles, but not tokens. The findings of the current study suggest that PR schedules may be useful as a means to better assess certain dimensions of tasks and how they affect reinforcer effectiveness (e.g., amount of effort the client is willing to exert, the duration at which the client willing to work, how many responses the client will emit, etc.), and to evaluate to what extent tokens actually function as generalized conditioned reinforcers.

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)0.000
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0100.001

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 head0.281
GPT teacher head0.445
Teacher spread0.164 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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