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Record W2088935518 · doi:10.1016/j.pnsc.2007.06.006

A study on neural mechanism of face processing based on fMRI

2008· article· en· W2088935518 on OpenAlexaff
Jiangang Liu, Jie Tian, Kang Lee, Jun Li

Bibliographic record

VenueProgress in Natural Science Materials International · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCategorizationFusiform face areaFace (sociological concept)Chinese charactersPsychologyFusiform gyrusVisual processingFace perceptionCognitive psychologyMechanism (biology)Computer sciencePerceptionCognitionArtificial intelligenceNeuroscienceLinguistics

Abstract

fetched live from OpenAlex

Recently, there were debates about the specificity of lateral middle fusiform in face processing. The debates focused on whether these areas were specialized in face processing or involved in processing of visual expertise and categorization at individual level. The present study aims to investigate the neural mechanism of face processing, using Chinese characters as comparison stimuli. Chinese characters are greatly similar to faces on a variety of dimensions, among which the most significant one is that both faces and Chinese characters not only are extremely familiar to literate Chinese adults but also are processed at individual level. In the present study, faces and Chinese characters activated bilateral middle fusiform with great correlation. Greater activities were observed in the right fusiform face area (FFA) for faces than for Chinese characters. These results demonstrate that FFA is specialized in face processing per se rather than the processing of visual expertise and categorization at individual level.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.438

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 science0.0010.000
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 head0.067
GPT teacher head0.357
Teacher spread0.290 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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