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Record W4236777071 · doi:10.2174/187423000701011000

Evidence for a Generic Process Underlying Multisensory Integration

2007· article· en· W4236777071 on OpenAlexafffund
Corinne Tremblay, François Champoux, Benoît A. Bacon, Hugo Théoret

Bibliographic record

VenueThe Open Behavioral Science Journal · 2007
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversité de MontréalBishop's UniversityCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsProcess (computing)Multisensory integrationComputer scienceProcess managementProcess engineeringPsychologyBusinessNeuroscienceProgramming languageEngineeringPerception

Abstract

fetched live from OpenAlex

It has been shown repeatedly that the various sensory modalities interact with each other and that the integration of incongruent percepts across two modalities, such as vision and audition, can lead to illusions.Different individual cognitive features (i.e., attention, linguistic experience, etc.) have been shown to modulate the level of multisensory integration.As such, it may be hypothesized that an intra-individual generic process underlies parts of illusory perception, irrespective of illusory material.One simple way to address this issue is to assess whether observers experience multisensory integration to a similar degree when the illusory material varies with respect to its sensory features.Here, performance on two distinct audio-visual illusions (McGurk effect, illusory flash effect) was tested in a group of adult observers.Results show a positive within-subject correlation between both illusions indirectly supporting the existence of a generic process for multisensory integration that could include individual differences in attention.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.650
GPT teacher head0.588
Teacher spread0.062 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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