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Record W2064152672 · doi:10.1068/i0697sas

Visual Attention at the Tip of the Tongue

2015· article· en· W2064152672 on OpenAlexaff
Michael Barnett‐Cowan, Matin Soeizi, Joseph F. X. DeSouza

Bibliographic record

Venuei-Perception · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork UniversityUniversity of Waterloo
Fundersnot available
KeywordsTongueVisual searchEmbodied cognitionTask (project management)Cognitive psychologyPsychologyTip of the tongueCognitionSensory systemCommunicationNeuroscienceComputer scienceArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The brain shifts attention by selectively modulating sensory information about relevant environmental features. It has been shown that eye, head, trunk and limb position can bias spatial attention. This leads to the interesting question: Does the brain only recruit bodily information that is explicitly related to orienting behaviour to direct attention, or more generally? We tested whether tongue position, which does not explicitly functionally relate to orienting behaviour, biases attention in a visual search task. Thirty-six participants completed three visual search trial blocks of increased difficulty each consisting of three tongue positions for 50 trials. Response times and error rates were used to assess whether tongue position modulates visual attention. Results show that sensorimotor information from the tongue modulates attention in a difficult visual search task: faster responses to visual search targets presented ipsilateral with the tongue; slower responses when contralateral. In line with cognition being generally embodied, the tongue plays a surprising role in directing 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)0.000
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 score0.003
Threshold uncertainty score0.012

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.091
GPT teacher head0.350
Teacher spread0.259 · 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

Citations20
Published2015
Admission routes1
Has abstractyes

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