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Laterality Effect for Faces in Chimpanzees (<i>Pan troglodytes</i>)

2013· article· en· W2035921969 on OpenAlexaff
Christoph D. Dahl, Malte J. Rasch, Masaki Tomonaga, Ikuma Adachi

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersJapan Society for the Promotion of ScienceDeutscher Akademischer Austauschdienst
KeywordsLateralityPsychologyRight hemisphereLateralization of brain functionPrimateTroglodytesPerceptionCognitive psychologyDominance (genetics)Face perceptionLeft and rightNeuroscienceBiologyZoology

Abstract

fetched live from OpenAlex

Face perception in humans is governed more by right-hemispheric than left-hemispheric neural correlate. Some but not all neurophysiological studies depict a right-side dominance for face responsive neurons in the brains of macaques. Hence, it is an open question whether and to what extent a right-hemisphere preference of processing faces exists across primate brains. We investigated chimpanzees discriminating chimeric faces of chimpanzees and humans, i.e., the combination of either left or right sides of a face vertically flipped and merged into a whole face. We found an effect of choosing the left-chimeric face more often than the right-chimeric face as being the one of the two that is closer to the original face, reflecting an advantage for the right side of the brain to process faces, as reported in humans. Moreover, we found a modulation by age of the participants, suggesting that the exposure history with a particular category shapes the right-hemispheric neural correlate to a configural/holistic processing strategy. In other words, the findings in chimpanzee participants parallel those in human participants and are suggestive for similar neural machineries in the occipital-temporal cortices in both species.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.316
Teacher spread0.269 · 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 designBench or experimental
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

Citations18
Published2013
Admission routes1
Has abstractyes

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