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Record W2532087418 · doi:10.1017/cbo9781107279353.006

Cognizance of the Neuroimaging Methods for Studying the Social Brain

2016· book-chapter· en· W2532087418 on OpenAlexaff
Stephanie Cacioppo, John T. Cacioppo

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNeuroimagingEmbodied cognitionSet (abstract data type)PsychologyInterpretation (philosophy)Social cognitionCognitive scienceCognitionBrain activity and meditationCognitive psychologyNeuroscienceComputer scienceArtificial intelligenceElectroencephalography

Abstract

fetched live from OpenAlex

A key challenge in the study of the social brain resides not only in determining how psychological states and processes map onto patterns of brain activity but also how this activity is modulated by shared representations, social compositions and social behaviors. The past 20 years have seen the growth of neuroimaging methods for studying neural aspects of shared representations, embodied cognition and the social brain in normal, waking humans. We discuss the intimate relationship between theory and methods; we discuss a set of considerations to guide the interpretation or understanding of data from neuroimaging studies; and we discuss the importance of using converging methods to dissect the social brain.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.953
Threshold uncertainty score0.515

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 science0.0000.000
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 head0.090
GPT teacher head0.339
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2016
Admission routes1
Has abstractyes

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