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Record W3007426555 · doi:10.1002/jeab.584

Preference for free or forced choice in Sumatran orangutans (<i>Pongo abelii</i>)

2020· article· en· W3007426555 on OpenAlexaff
Sarah Ritvo, Suzanne E. MacDonald

Bibliographic record

VenueJournal of the Experimental Analysis of Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicPrimate Behavior and Ecology
Canadian institutionsYork University
Fundersnot available
KeywordsPreferenceTwo-alternative forced choicePsychologySocial psychologyCognitive psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Empirical investigations of humans, pigeons, rats, and monkeys have indicated that these species will select free over forced choice, even when faced with identical outcomes. However, the same has yet to be quantitatively confirmed in nonhuman great apes. This experiment is the first systematic investigation of preference for free or forced choice in great apes using a paradigm in which extraneous variables are highly controlled. Three orangutans were given a choice of one of two virtual routes, one that provided a choice and one that did not via a touchscreen computer program. Choice of either route was rewarded with the same type and quantity of food. Initial results indicated a preference for free choice across all three participants. However, in two control conditions, orangutans' preferences varied, suggesting a weaker tendency to exercise choice than species previously tested. We suggest further investigation of preference for free and forced choice in orangutans and other great apes through alternative experimental paradigms that focus on increasing the fidelity of free and forced choice options.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.381
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2020
Admission routes1
Has abstractyes

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