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Record W3177225544 · doi:10.1038/s41598-021-92192-1

Haptic awareness changes when lying down

2021· article· en· W3177225544 on OpenAlexafffund
Kaian Unwalla, Michelle L. Cadieux, David I. Shore

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReference frameLyingComputer scienceFrame of referenceVestibular systemWeightingTask (project management)Computer visionArtificial intelligencePerceptionFrame (networking)Haptic technologyPsychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

Accurate localization of touch requires the integration of two reference frames-an internal (e.g., anatomical) and an external (e.g., spatial). Using a tactile temporal order judgement task with the hands crossed over the midline, we investigated the integration of these two reference frames. We manipulated the reliability of the visual and vestibular information, both of which contribute to the external reference frame. Visual information was manipulated between experiments (Experiment 1 was done with full vision and Experiment 2 was done while wearing a blindfold). Vestibular information was manipulated in both experiments by having the two groups of participants complete the task in both an upright posture and one where they were lying down on their side. Using a Bayesian hierarchical model, we estimated the perceptual weight applied to these reference frames. Lying participants on their side reduced the weight applied to the external reference frame and produced a smaller deficit; blindfolding resulted in similar reductions. These findings reinforce the importance of the visual system when weighting tactile reference frames, and highlight the importance of the vestibular system in this integration.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.074
GPT teacher head0.309
Teacher spread0.235 · 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

Citations26
Published2021
Admission routes2
Has abstractyes

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