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Record W2159904468 · doi:10.1037/a0038514

Bodily illusions disrupt tactile sensations.

2014· article· en· W2159904468 on OpenAlexafffund
Sarah D’Amour, Lisa M. Pritchett, Laurence R. Harris

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2014
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIllusionPerceptionSomatosensory systemRepresentation (politics)Tactile perceptionPsychologySensationSensory systemAudiologyCognitive psychologyCommunicationComputer visionNeuroscienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

To accurately interpret tactile information, the brain needs to have an accurate representation of the body to which to refer the sensations. Despite this, body representation has only recently been incorporated into the study of tactile perception. Here, we investigate whether distortions of body representation affect tactile sensations. We perceptually altered the length of the arm and the width of the waist using a tendon vibration illusion and measured spatial acuity and sensitivity. Surprisingly, we found reduction in both tactile acuity and sensitivity thresholds when the arm or waist was perceptually altered, which indicates a general disruption of low-level tactile processing. We postulate that the disruptive changes correspond to the preliminary stage as the body representation starts to change and may give new insights into sensory processing in people with long-term or sudden abnormal body representation such as are found in eating disorders or following amputation.

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)0.002
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
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.357
Teacher spread0.326 · 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

Citations7
Published2014
Admission routes2
Has abstractyes

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