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Record W2967776347 · doi:10.3389/fnhum.2019.00292

Social Media Approval Reduces Emotional Arousal for People High in Narcissism: Electrophysiological Evidence

2019· article· en· W2967776347 on OpenAlexaff
Kyle Nash, Andre Johansson, Kumar Yogeeswaran

Bibliographic record

VenueFrontiers in Human Neuroscience · 2019
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSelfiePsychologyNarcissismSocial mediaDistressSocial psychologyArousalDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

We used event-related potentials (ERPs) to examine if posting a ‘selfie’ and receiving validation from others in the form of ‘likes’ on social media can help narcissists reduce psychological distress. After all participants completed the narcissistic personality inventory (NPI) and experienced social exclusion, participants completed an auditory startle task that elicits the P3 to white noise—an ERP component that reflects emotional arousal and is sensitive to psychological distress. Participants were then randomly assigned to either view a personal ‘selfie’ that quickly received a significant number of ostensibly real ‘likes’ (selfie with likes condition), view a ‘selfie’ with no feedback (selfie only condition), or view a neutral picture before (neutral picture condition) completing the auditory startle task again. Results revealed that participants high on the leadership/authority subscale of the NPI in the ‘selfie’ with ‘likes’ condition demonstrated a pre-post manipulation decrease in P3 mean amplitude, relative to participants in the other two conditions. These results suggest that approval via social media can help certain kinds of narcissists alleviate distress from social exclusion.

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 score0.728
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.043
GPT teacher head0.329
Teacher spread0.285 · 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

Citations27
Published2019
Admission routes1
Has abstractyes

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