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Record W2048300983 · doi:10.1162/jocn.2011.21616

Task-invariant Brain Responses to the Social Value of Faces

2011· article· en· W2048300983 on OpenAlexaff
Alexander Todorov, Christopher P. Said, Nikolaas N. Oosterhof, Andrew D. Engell

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2011
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsYork University
FundersRussell Sage FoundationNational Science Foundation
KeywordsPsychologyValence (chemistry)Fusiform face areaCognitive psychologyAmygdalaFace perceptionSocial psychologyNeurosciencePerception

Abstract

fetched live from OpenAlex

In two fMRI experiments (n = 44) using tasks with different demands-approach-avoidance versus one-back recognition decisions-we measured the responses to the social value of faces. The face stimuli were produced by a parametric model of face evaluation that reduces multiple social evaluations to two orthogonal dimensions of valence and power [Oosterhof, N. N., & Todorov, A. The functional basis of face evaluation. Proceedings of the National Academy of Sciences, U.S.A., 105, 11087-11092, 2008]. Independent of the task, the response within regions of the occipital, fusiform, and lateral prefrontal cortices was sensitive to the valence dimension, with larger responses to low-valence faces. Additionally, there were extensive quadratic responses in the fusiform gyri and dorsal amygdala, with larger responses to faces at the extremes of the face valence continuum than faces in the middle. In all these regions, participants' avoidance decisions correlated with brain responses, with faces more likely to be avoided evoking stronger responses. The findings suggest that both explicit and implicit face evaluation engage multiple brain regions involved in attention, affect, and decision making.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.265
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.134
GPT teacher head0.353
Teacher spread0.219 · 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 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

Citations57
Published2011
Admission routes1
Has abstractyes

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