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Record W2339574575 · doi:10.1167/16.6.12

The brain frequency tuning function for facial emotion discrimination: An ssVEP study

2016· article· en· W2339574575 on OpenAlexafffund
Maria Zhu, Esther Alonso‐Prieto, Todd C. Handy, Jason J.S. Barton

Bibliographic record

VenueJournal of Vision · 2016
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsFusiform gyrusSuperior temporal sulcusFacial expressionFlickerPerceptionHuman brainTemporal cortexSulcusPsychologyComputer scienceSpeech recognitionPattern recognition (psychology)NeuroscienceArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

Steady-state visual evoked potentials have only been applied recently to the study of face perception. We used this method to study the spatial and temporal dynamics of expression perception in the human brain and test the prediction that, as in the case of identity perception, the optimal frequency for facial expression would also be in the range of 5-6 Hz. We presented facial expressions at different flickering frequencies (2-8 Hz) to human observers while recording their brain electrical activity. Our modified adaptation paradigm contrasted blocks with varying expressions versus blocks with a constant neutral expression, while facial identity was kept constant. The presentation of different expressions created a larger steady-state response only at 5 Hz, corresponding to a cycle of 200 ms, over right occipito-temporal electrodes. Source localization using a time-domain analysis showed that the effect localized to the right occipito-temporal cortex, including the superior temporal sulcus and fusiform gyrus.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.806
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.073
GPT teacher head0.358
Teacher spread0.285 · 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 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

Citations16
Published2016
Admission routes2
Has abstractyes

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