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Record W2998233795

Sensory modality and body-location imagery among persons with somatosensory amplification

2019· article· en· W2998233795 on OpenAlexaboutno aff
Atsushi Okada, Jiro Gyoba

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2019
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSomatosensory systemSensory systemModality (human–computer interaction)PsychologyCommunicationComputer scienceArtificial intelligenceCognitive psychologyNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to assess whether somatosensory amplification influenced the relationship between basic emotions and sensory modality and body image by the modality differential (MD) method and the body-image location (BIL) scale.Approximately 130 undergraduate students were administered the Japanese Version of the Somatosensory Amplification Scale (SSAS) and were asked to rate the relevance of 10 sensory modalities (warm, cold, olfactory, gustatory, tactile, pain, equilibrium, kinesthetic, visual, and auditory) and 7 body parts (forehead, throat, chest, stomach, lower abdomen, internal organs, and whole body) to 6 basic emotions (happiness, sadness, fear, anger, surprise, and disgust).Participants also completed the Japanese version of the 20-item Toronto Alexithymia Scale.Each emotion was compared between the two groups (high and low SSAS) using the MD method and BIL scale.The high SSAS's relevance of sadness, fear, anger, and surprise to the proximal sensory image was stronger than that of the low SSAS.Moreover, the high SSAS's relevance of happiness, fear, anger, surprise, and disgust to body location image was stronger than that of the low SSAS.These results suggest that somatosensory amplification strongly influenced the combination of sensory modality and body image with emotion.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.746

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 studies0.0010.000
Scholarly communication0.0000.003
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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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