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Record W2153711092 · doi:10.3389/fnhum.2012.00045

Investigating the Features of the M170 in Congenital Prosopagnosia

2012· article· en· W2153711092 on OpenAlexfundno aff
Davide Rivolta, Romina Palermo, Laura Schmalzl, Mark Williams

Bibliographic record

VenueFrontiers in Human Neuroscience · 2012
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsnot available
FundersAustralian Research CouncilMax-Planck-GesellschaftMacquarie UniversityMcMaster University
KeywordsArtificial intelligencePsychologyComputer scienceNeuroscienceCognitive psychology

Abstract

fetched live from OpenAlex

Face perception generates specific neural activity as early as 170 ms post-stimulus onset, termed the M170 when measured with Magnetoencephalography (MEG). We examined the M170 in six people with congenital prosopagnosia (CP) and 11 typical controls. Previous research indicates that there are two neural generators for the M170 (one within the right lateral occipital area - rLO and one within the right fusiform gyrus - rFG), and in the current study we explored whether these sources reflect the processing of different types of information. Individuals with CP showed face-selective M170 responses within the rLO and right rFG, which did not differ in magnitude to those of the controls. To examine possible links between neural activity and behavior we correlated the CPs' MEG activity generated within rLO and rFG with their face perception skills. The rLO-M170 correlated with holistic/configural face processing, whereas the rFG-M170 correlated with featural processing. Hence, the results of our study demonstrate that individuals with CP can show an M170 that is within the normal range, and that the M170 in the rLO and rFG are involved in different aspects of face processing.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.053
GPT teacher head0.290
Teacher spread0.237 · 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

Citations39
Published2012
Admission routes1
Has abstractyes

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