2016
DOI: 10.1002/hbm.23454
|View full text |Cite
|
Sign up to set email alerts
|

Mortality salience reduces the discrimination between in‐group and out‐group interactions: A functional MRI investigation using multi‐voxel pattern analysis

Abstract: As a fundamental concern of human beings, mortality salience impacts various human social behaviors including intergroup interactions; however, the underlying neural signature remains obscure. Here, we examined the neural signatures underlying the impact of mortality reminders on in-group bias in costly punishment combining a second-party punishment task with multivariate pattern analysis of fMRI data. After mortality salience (MS) priming or general negative affect priming, participants received offers from r… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
12
0
2

Year Published

2017
2017
2019
2019

Publication Types

Select...
5

Relationship

3
2

Authors

Journals

citations
Cited by 22 publications
(14 citation statements)
references
References 108 publications
(173 reference statements)
0
12
0
2
Order By: Relevance
“…For example, previous evidence has that a multivariate predictive model combining the multi‐round TG with electroencephalography (EEG)‐based RSFC predicts only initial trust as measured during the first round (i.e., trust propensity) but not trust as measured across multiple rounds (i.e., trust dynamics; Hahn et al, ). Further, the machine learning approach allows for the prediction of unseen participants, offering information at the individual level rather than group level (Cui, Su, Li, Shu, & Gong, ; Cui, Xia, Su, Shu, & Gong, ; Dubois & Adolphs, ; Feng et al, ; Gabrieli, Ghosh, & Whitfield‐Gabrieli, ; Shen et al, ; Yarkoni & Westfall, ). A machine learning approach typically implements cross‐validation procedures to estimate the model with training samples and to test the performance of the model with independent samples (i.e., test samples).…”
Section: Introductionmentioning
confidence: 99%
“…For example, previous evidence has that a multivariate predictive model combining the multi‐round TG with electroencephalography (EEG)‐based RSFC predicts only initial trust as measured during the first round (i.e., trust propensity) but not trust as measured across multiple rounds (i.e., trust dynamics; Hahn et al, ). Further, the machine learning approach allows for the prediction of unseen participants, offering information at the individual level rather than group level (Cui, Su, Li, Shu, & Gong, ; Cui, Xia, Su, Shu, & Gong, ; Dubois & Adolphs, ; Feng et al, ; Gabrieli, Ghosh, & Whitfield‐Gabrieli, ; Shen et al, ; Yarkoni & Westfall, ). A machine learning approach typically implements cross‐validation procedures to estimate the model with training samples and to test the performance of the model with independent samples (i.e., test samples).…”
Section: Introductionmentioning
confidence: 99%
“…Previous studies utilizing the altruistic punishment task have revealed the consistent involvement of a salience network, anchored in the dorsal anterior cingulate cortex (dACC) and anterior insula (AI) [Feng et al, ; Gabay et al, ]. These brain regions have been implicated in detecting norm‐violating behaviors [Feng et al, ; Strobel et al, ] as well as predicting decisions to punish violators [Gabay et al, ]. Further, those punishment‐related neural responses are modulated according to social contexts and people [Baumgartner et al, ; Haruno and Frith, ; Haruno et al, ; Wright et al, ].…”
Section: Introductionmentioning
confidence: 99%
“…Reading death‐related relative to unpleasant, but death‐unrelated sentences also activated the ventral ACC [Quirin et al, ] and decreased AI activity [Klackl et al, ]. Mortality salience also modulates brain activity in response to others' pain and costly punishment in the ACC, MPFC, lateral prefrontal cortex (LPC) [Feng et al, ; Luo et al, ; Li et al, ], and to attractive opposite‐sex faces in the insular and LPC [Silveira et al, ].…”
Section: Introductionmentioning
confidence: 99%
“…This design allowed us to examine neural responses to death‐related (vs neutral) words and negative (vs neutral) words and to assess whether the moderation effects of the 5‐HTTLPR are specific to brain responses to mortality threats or are general to brain activities to any negative stimuli. More specifically, we examined whether and how the 5‐HTTLPR moderates the association between interdependence and neural activity to mortality threats in the brain regions involved in emotional responses (e.g., AI and putamen [Han et al, ; Shi and Han, ]), monitoring control processes (e.g., ACC [Feng et al, ; Han et al, ]), and emotional regulation (e.g., LPC [Feng et al, ; Silveira et al, ]).…”
Section: Introductionmentioning
confidence: 99%