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Record W4213373562 · doi:10.1038/s41598-022-07053-2

Learning from communication versus observation in great apes

2022· article· en· W4213373562 on OpenAlexaff
Hanna Marno, Christoph J. Völter, Brandon Tinklenberg, Dan Sperber, Josep Call

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsYork University
FundersFP7 Ideas: European Research CouncilAgence Nationale de la RechercheEuropean Commission
KeywordsHominidaeComputer scienceBiological evolutionBiologyGenetics

Abstract

fetched live from OpenAlex

When human infants are intentionally addressed by others, they tend to interpret the information communicated as being relevant to them and worth acquiring. For humans, this attribution of relevance leads to a preference to learn from communication, making it possible to accumulate knowledge over generations. Great apes are sensitive to communicative cues, but do these cues also activate an expectation of relevance? In an observational learning paradigm, we demonstrated to a sample of nonhuman great apes (bonobos, chimpanzees, orangutans; N = 24) how to operate on a food dispenser device. When apes had the opportunity to choose between an effective and an ineffective method in the baseline conditions, the majority of them chose the effective method. However, when the ineffective method was demonstrated in a communicative way, they failed to prioritize efficiency, even though they were equally attentive in both conditions. This suggests that the ostensive demonstration elicited an expectation of relevance that modified apes' interpretation of the situation, potentially leading to a preference to learn from communication, as human children do.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.052
GPT teacher head0.293
Teacher spread0.241 · 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.

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

Citations21
Published2022
Admission routes1
Has abstractyes

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