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Record W4243673740 · doi:10.31234/osf.io/n7bqs

Interpersonal touch enhances cognitive control: A neurophysiological investigation

2018· preprint· en· W4243673740 on OpenAlexaff
Blair Saunders, Anja Riesel, Julia Klawohn, Michael Inzlicht

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyInterpersonal communicationNeurocognitiveCognitionControl (management)ElectroencephalographyNeurophysiologyCognitive psychologySocial psychologyNeuroscienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Touch is central to mammalian communication, socialisation, and wellbeing. Despite this prominence, interpersonal touch is relatively understudied. In this preregistered investigation, we assessed the influence of interpersonal touch on the subjective, neural, and behavioural correlates of cognitive control. Forty-five romantic couples were recruited (N=90; dating>6 months), and one partner performed an inhibitory control task while electroencephalography was recorded to assess neural performance monitoring. Interpersonal touch was provided by the second partner, and was manipulated between experimental blocks. A within-subject repeated-measures design was used to maximise statistical power, with our sample size providing 80% power for even small effect sizes (ds > .25). Results indicated that participants were not only happier when receiving touch, but also showed increased neural processing of mistakes. Further exploratory cognitive modelling using indirect effects tests and drift diffusion models of decision making revealed that touch was indirectly associated with both improved inhibitory control and increased rates of evidence accumulation (drift rate) through its influence on neural monitoring. Thus, beyond regulating emotion and stress, interpersonal touch appears to enhance the neurocognitive processes underling flexible goal-directed behaviour.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.999

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)0.0170.002

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 head0.065
GPT teacher head0.341
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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