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Record W2038635697 · doi:10.3109/08990220.2010.505033

The effect of adapting speed, duration, and distance on the tactile motion aftereffect

2010· article· en· W2038635697 on OpenAlexafffund
Peggy J. Planetta, Philip Servos

Bibliographic record

VenueSomatosensory & Motor Research · 2010
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDuration (music)IllusionExposure durationAudiologyAcousticsMathematicsPsychologyBiologyCognitive psychologyPhysicsMedicine

Abstract

fetched live from OpenAlex

We investigated the effect of adapting speed, duration, and distance on the frequency of occurrence, duration, and vividness of the tactile motion aftereffect (tMAE). Using a cylindrical drum with a patterned surface we adapted the glabrous surface of the right hand at two speeds (14 and 28 cm/s) and three durations (60, 120, and 240 s). Distance was explored in the interaction of adapting speed and duration. The results showed that the frequency of occurrence, duration, and vividness of the tMAE increased with adapting speed. There was also a positive relationship between adapting duration and the frequency of occurrence, but not the duration or vividness, of the illusion. Distance was only a factor when it came to the duration of the tMAE. Taken together, these results show the importance of adapting parameters, particularly speed, on the tMAE.

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)0.004
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
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
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)0.0020.000

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 head0.050
GPT teacher head0.351
Teacher spread0.301 · 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

Citations3
Published2010
Admission routes2
Has abstractyes

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