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Record W2103257046 · doi:10.1109/tbme.2005.845147

Objective Measurement of Tactile Mislocalization

2005· article· en· W2103257046 on OpenAlexafffund
Christoph Braun, J. Ladda, Michaela Burkhardt, Katja Wiech, Hubert Preißl, Larry E. Roberts

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2005
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsSomatosensory systemReplicateStimulus (psychology)PerceptionComputer scienceTactile perceptionSensory thresholdSensory systemPsychophysicsArtificial intelligenceSensory stimulation therapyTactile sensorTactile stimuliSensitivity (control systems)Detection thresholdComputer visionPattern recognition (psychology)PsychologyNeuroscienceMathematicsReal-time computingEngineeringElectronic engineeringCognitive psychology

Abstract

fetched live from OpenAlex

Stimulating the skin with intensities close to the sensory threshold causes erroneous localization of the site of stimulation. Previous studies using manual methods for applying faint tactile stimuli have shown that localization errors obey a somatotopic principle in which tactile stimuli are preferentially mislocalized to sites adjacent to the stimulated skin region. However, manual testing of mislocalization is time consuming and only partially objective because results depend on the skills of the tester. To improve the testing procedure, an automated apparatus was developed. The procedure adjusted stimulus intensity adaptively during testing to remain near the individual subject's sensory threshold, so that mislocalizations occurred often enough to assess somatotopic organization. The new method was applied to 12 healthy subjects. In each subject, the five digits of the right hand were stimulated singly in random order. Localization errors were distributed preferentially to fingers close to the stimulated finger rather than to distant fingers. The profile of mislocalization differed significantly from that expected on the basis of response bias or guessing behavior. The present results replicate previous findings obtained for manual testing with improved sensitivity and indicate that the new technique is a useful tool for the study of somatosensory processing on a perceptual level.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.244
Teacher spread0.220 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2005
Admission routes2
Has abstractyes

Explore more

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