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Record W4296780668 · doi:10.1098/rstb.2021.0185

The human tickle response and mechanisms of self-tickle suppression

2022· article· en· W4296780668 on OpenAlexaff
Sandra Proelss, Shimpei Ishiyama, Eduard Maier, Matthias Schultze-Kraft, Michael Brecht

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2022
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsInnovation Cluster (Canada)
FundersH2020 European Research CouncilDeutsche Forschungsgemeinschaft
KeywordsLaughterPsychologySensory systemNeuroscienceCognitive psychologyAudiologyMedicine

Abstract

fetched live from OpenAlex

A tickle is a complex sensation: it occurs in response to touch but not unequivocally so, and makes us laugh albeit not when we self-tickle. We quantified human ticklishness by means of physiological, visual and acoustic measures alongside subjective reports, and assessed mechanisms of self-tickle suppression. Tickle responses arose faster than previously reported as changes in thoracic circumference and joyous facial expressions co-emerge approximately 300 ms after tickle onset and are followed by vocalizations starting after an additional 200 ms. The timing and acoustic properties of vocalizations tightly correlated with subjective reports: the faster, louder and higher-pitched participants laughed, the stronger they rated the experienced ticklishness. Externally evoked ticklishness is reduced by simultaneous self-tickling, whereby self-touch evokes stronger suppression than sole self-tickle movement without touch. We suggest that self-tickle suppression can be understood as broad attenuation of sensory temporally coincident inputs. Our study provides new insight on the nature of human ticklishness and the attenuating effects of self-tickling. This article is part of the theme issue 'Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience'.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.325
Teacher spread0.273 · 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 designTheoretical or conceptual
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

Citations11
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuePhilosophical Transactions of the Royal Society B Biological SciencesSame topicHumor Studies and ApplicationsFrench-language works237,207