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Record W2789925454 · doi:10.1037/xge0000412

Interpersonal touch enhances cognitive control: A neurophysiological investigation.

2018· article· en· W2789925454 on OpenAlexaff
Blair Saunders, Anja Riesel, Julia Klawohn, Michael Inzlicht

Bibliographic record

VenueJournal of Experimental Psychology General · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyInterpersonal communicationPsycINFOCognitionNeurocognitiveElectroencephalographyCognitive psychologySocial psychologyNeuroscienceMEDLINE

Abstract

fetched live from OpenAlex

Touch is central to mammalian communication, socialization, 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 behavioral 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 maximize 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 modeling 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 behavior. (PsycINFO Database Record

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: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.643

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 studies0.0000.002
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 head0.158
GPT teacher head0.440
Teacher spread0.282 · 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

Citations27
Published2018
Admission routes1
Has abstractyes

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