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Record W2094717165 · doi:10.1097/wnr.0b013e32832e0c8d

Representations of physical plausibility revealed by event-related potentials

2009· article· en· W2094717165 on OpenAlexaff
Matthew E. Roser, Jonathan A. Fugelsang, Todd C. Handy, Kevin Dunbar, Michael S. Gazzaniga

Bibliographic record

VenueNeuroreport · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsThe Scarborough HospitalUniversity of British ColumbiaUniversity of TorontoUniversity of Waterloo
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsEvent-related potentialNeuroscienceEvent (particle physics)PsychologyCognitive psychologyElectroencephalographyPhysics

Abstract

fetched live from OpenAlex

Maintaining an accurate mental representation of the current environment is crucial to detecting change in that environment and ensuring behavioral coherence. Past experience with interactions between objects, such as collisions, has been shown to influence the perception of object interactions. To assess whether mental representations of object interactions derived from experience influence the maintenance of a mental model of the current stimulus environment, we presented physically plausible and implausible collision events while recording brain electrical activity. The parietal P300 response to 'oddball' events was found to be modulated by the physical plausibility of the stimuli, suggesting that past experience of object interactions can influence working memory processes involved in monitoring ongoing changes to the environment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.089
GPT teacher head0.416
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2009
Admission routes1
Has abstractyes

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