In this report, we reproduce the sensitivity analyses testing H4-6 in Study 3 reported in Supplementary Material using alternatively defined ROIs.

Given the high correlation between the preregistered Neurosynth ROIs, we conducted sensitivity analyses using ROIs from Scholz et al. (2017) A neural model of valuation and information virality.

In order to maximize the differentiation between the self-referential and social cognitive ROIs, we removed the PCC/precuneus cluster from the social cognitive ROI as it overlapped with the self-referential ROI.

prep data

First, we load the relevant packages, define functions and plotting aesthetics, and load and tidy the data.

load packages

library(pacman)
pacman::p_load(tidyverse, purrr, fs, knitr, lmerTest, ggeffects, kableExtra, boot, devtools, install = TRUE)
devtools::install_github("hadley/emo")

define functions

source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")

# MLM results table function
table_model = function(model_data) {
  model_data %>%
  broom.mixed::tidy(conf.int = TRUE) %>%
  filter(effect == "fixed") %>%
  rename("SE" = std.error,
         "t" = statistic,
         "p" = p.value) %>%
  select(-group, -effect) %>%
  mutate_at(vars(-contains("term"), -contains("p")), round, 2) %>%
  mutate(term = gsub("article_cond", "", term),
         term = gsub("\\(Intercept\\)", "intercept", term),
         term = gsub("article_condother", "other", term),
         term = gsub("article_condself", "self", term),
         term = gsub("topichealth", "topic (health)", term),
         term = gsub("self_referential", "self-referential", term),
         term = gsub("msg_rel_self_z_z", "self-relevance", term),
         term = gsub("msg_rel_social_z_z", "social relevance", term),
         term = gsub("self_referential", "self-referential", term),
         term = gsub("social_cognitive", "social cognitive", term),
         term = gsub(":", " x ", term),
         p = ifelse(p < .001, "< .001",
             ifelse(p > .999, "1.000", gsub("0.(.*)", ".\\1", sprintf("%.3f", p)))),
         `b [95% CI]` = sprintf("%.2f [%0.2f, %.2f]", estimate, conf.low, conf.high)) %>%
  select(term, `b [95% CI]`, df, t, p)
}

simple_slopes = function(model, var, moderator, continuous = TRUE) {
  
  if (isTRUE(continuous)) {
    emmeans::emtrends(model, as.formula(paste("~", moderator)), var = var) %>%
      data.frame() %>%
      rename("trend" = 2) %>%
      mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", trend, asymp.LCL, asymp.UCL)) %>%
      select(!!moderator, `b [95% CI]`) %>%
      kable()  %>%
      kableExtra::kable_styling()
    
  } else {
    confint(emmeans::contrast(emmeans::emmeans(model, as.formula(paste("~", var, "|", moderator))), "revpairwise", by = moderator, adjust = "none")) %>%
      data.frame() %>%
      filter(grepl("control", contrast)) %>%
      mutate(`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, asymp.LCL, asymp.UCL)) %>%
      select(contrast, !!moderator, `b [95% CI]`) %>%
      arrange(contrast) %>%
      kable()  %>%
      kableExtra::kable_styling()
  }
}

define aesthetics

palette_cond = c("self" = "#ee9b00",
                      "control" = "#0a9396",
                      "other" = "#005f73")
palette_roi = c("self-referential" = "#ee9b00",
               "social cognitive" = "#005f73")
palette_dv = c("self-relevance" = "#ee9b00",
               "social relevance" = "#005f73",
               "narrowcast sharing" = "#D295BF")
palette_topic = c("climate" = "#519872",
                 "health" = "#3A3357")

plot_aes = theme_minimal() +
  theme(legend.position = "top",
        legend.text = element_text(size = 12),
        text = element_text(size = 16, family = "Futura Medium"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.text = element_text(color = "black"),
        axis.line = element_line(colour = "black"),
        axis.ticks.y = element_blank())

load and tidy data

merged_all = read.csv("../data/study3_data.csv")

ratings_z = merged_all %>%
  select(SID, trial, article_number, article_cond, msg_rel_self, msg_rel_social, msg_share) %>%
  unique() %>%
  mutate(msg_share_z = scale(msg_share, scale = TRUE, center = TRUE),
         msg_rel_self_z = scale(msg_rel_self, center = TRUE, scale = TRUE),
         msg_rel_social_z = scale(msg_rel_social, center = TRUE, scale = TRUE))

merged = merged_all %>%
  mutate(atlas = gsub("mentalizing", "social_cognitive", atlas)) %>%
  filter(outlier == "no" | is.na(outlier)) %>%
  group_by(SID, atlas) %>%
  mutate(parameter_estimate_std = parameter_estimate / sd(parameter_estimate, na.rm = TRUE)) %>%
  left_join(., ratings_z)

merged_wide = merged %>%
  filter(atlas %in% c("self-referential", "social_cognitive")) %>%
  select(SID, site, trial, article_number, topic, article_cond, msg_share, msg_share_z,
         msg_rel_self, msg_rel_self_z, msg_rel_social, msg_rel_social_z, atlas, parameter_estimate_std) %>%
  spread(atlas, parameter_estimate_std) %>%
  rename("self_referential" = `self-referential`)

merged_wide_alt = merged %>%
  filter(atlas %in% c("pnas_self", "pnas_social_cognitive_nopc")) %>%
  select(SID, site, trial, article_number, topic, article_cond, msg_share, msg_share_z,
         msg_rel_self, msg_rel_self_z, msg_rel_social, msg_rel_social_z, atlas, parameter_estimate_std) %>%
  spread(atlas, parameter_estimate_std) %>%
  rename("self_referential" = pnas_self,
         "social_cognitive" = pnas_social_cognitive_nopc)

ROI correlations

Compared to the preregistered Neurosynth ROIs (r = .94, 95% CI [.94, .94]), the correlation between the alternative ROIs are substantially reduced.

merged_wide_alt %>%
  rmcorr::rmcorr(as.factor(SID), social_cognitive, self_referential, data = .)
## 
## Repeated measures correlation
## 
## r
## 0.5588849
## 
## degrees of freedom
## 5928
## 
## p-value
## 0
## 
## 95% confidence interval
## 0.5411297 0.576142

H4: relevance ~ ROI activity

Greater activity in the (a) self-referential region of interest (ROI) will be associated with higher self-relevance ratings, and (b) greater activity in the social cognitive ROI will be associated with higher social relevance ratings.

self-referential ROI

mod_h4a =  lmer(msg_rel_self_z ~ self_referential + (1 + self_referential | SID),
               data = merged_wide_alt,
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h4a = table_model(mod_h4a)

table_h4a %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept -0.01 [-0.09, 0.07] 84.62 -0.26 .794
self-referential 0.03 [0.01, 0.06] 83.66 2.40 .018

summary

summary(mod_h4a)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_rel_self_z ~ self_referential + (1 + self_referential | SID)
##    Data: merged_wide_alt
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 16580.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4707 -0.7086  0.1383  0.6783  2.4399 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr 
##  SID      (Intercept)      0.113396 0.33674       
##           self_referential 0.001559 0.03949  -0.79
##  Residual                  0.891348 0.94411       
## Number of obs: 6014, groups:  SID, 85
## 
## Fixed effects:
##                  Estimate Std. Error       df t value Pr(>|t|)  
## (Intercept)      -0.01014    0.03873 84.61522  -0.262   0.7941  
## self_referential  0.03086    0.01284 83.66477   2.403   0.0185 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## self_rfrntl -0.344

social cognitive ROI

mod_h4b = lmer(msg_rel_social_z ~ social_cognitive + (1 + social_cognitive | SID),
               data = merged_wide_alt,
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h4b = table_model(mod_h4b)

table_h4b %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept -0.01 [-0.09, 0.08] 84.07 -0.19 .847
social cognitive 0.04 [0.01, 0.06] 82.98 2.86 .005

summary

summary(mod_h4b)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_rel_social_z ~ social_cognitive + (1 + social_cognitive |  
##     SID)
##    Data: merged_wide_alt
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 16371
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8050 -0.7206  0.1710  0.6480  2.6691 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr 
##  SID      (Intercept)      0.147258 0.38374       
##           social_cognitive 0.001135 0.03369  -0.09
##  Residual                  0.856482 0.92546       
## Number of obs: 6014, groups:  SID, 85
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)      -0.008402   0.043470 84.072840  -0.193  0.84721   
## social_cognitive  0.035594   0.012461 82.976339   2.856  0.00541 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## socil_cgntv -0.100

combined plot

vals = seq(-4.5, 4.5, .1)
predicted_h4 = ggeffects::ggpredict(mod_h4a, c("self_referential [vals]")) %>%
  data.frame() %>%
  mutate(roi = "self-referential",
         variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h4b, c("social_cognitive [vals]")) %>%
              data.frame() %>%
              mutate(roi = "social cognitive",
                     variable = "social relevance"))

predicted_sub_h4 = ggeffects::ggpredict(mod_h4a, terms = c("self_referential [vals]", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(roi = "self-referential",
         variable = "self-relevance") %>%
  bind_rows(ggeffects::ggpredict(mod_h4b, c("social_cognitive [vals]", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(roi = "social cognitive",
                     variable = "social relevance"))

predicted_h4 %>%
  ggplot(aes(x, predicted)) +
  stat_smooth(data = predicted_sub_h4, aes(group = group, color = roi), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high, fill = roi), alpha = .5, color = NA) +
  geom_line(aes(color = roi), size = 2) +
  facet_grid(~variable) +
  scale_color_manual(name = "", values = palette_roi, guide = FALSE) +
  scale_fill_manual(name = "", values = palette_roi, guide = FALSE) +
  labs(x = "\nROI activity (SD)", y = "predicted rating\n") +
  plot_aes

H5: sharing ~ ROI activity

Greater activity in the (a) self-referential and (b) social cognitive ROIs will be associated with stronger news sharing intentions.

self-referential ROI

mod_h5a = lmer(msg_share_z ~ self_referential + (1 + self_referential | SID),
              data = merged_wide_alt,
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h5a = table_model(mod_h5a)

table_h5a %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept -0.02 [-0.09, 0.06] 85.08 -0.45 .651
self-referential 0.06 [0.03, 0.09] 83.56 4.40 < .001

summary

summary(mod_h5a)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_share_z ~ self_referential + (1 + self_referential | SID)
##    Data: merged_wide_alt
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 16380.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5502 -0.7218  0.1157  0.7344  2.1951 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr 
##  SID      (Intercept)      0.104470 0.32322       
##           self_referential 0.003838 0.06195  -0.36
##  Residual                  0.892105 0.94451       
## Number of obs: 5935, groups:  SID, 85
## 
## Fixed effects:
##                  Estimate Std. Error       df t value  Pr(>|t|)    
## (Intercept)      -0.01701    0.03745 85.07735  -0.454     0.651    
## self_referential  0.06136    0.01396 83.55508   4.396 0.0000323 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## self_rfrntl -0.251

social cognitive ROI

mod_h5b = lmer(msg_share_z ~ social_cognitive + (1 + social_cognitive | SID),
              data = merged_wide_alt,
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h5b = table_model(mod_h5b)

table_h5b %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept -0.01 [-0.08, 0.06] 84.71 -0.26 .797
social cognitive 0.04 [0.02, 0.07] 83.49 3.41 .001

summary

summary(mod_h5b)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: msg_share_z ~ social_cognitive + (1 + social_cognitive | SID)
##    Data: merged_wide_alt
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 16397.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6298 -0.7135  0.1138  0.7344  2.0984 
## 
## Random effects:
##  Groups   Name             Variance Std.Dev. Corr
##  SID      (Intercept)      0.103051 0.32102      
##           social_cognitive 0.001125 0.03354  0.08
##  Residual                  0.896387 0.94678      
## Number of obs: 5935, groups:  SID, 85
## 
## Fixed effects:
##                   Estimate Std. Error        df t value Pr(>|t|)   
## (Intercept)      -0.009573   0.037135 84.707328  -0.258  0.79720   
## social_cognitive  0.043529   0.012774 83.485143   3.407  0.00101 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## socil_cgntv -0.070

combined plot

vals = seq(-4.5, 4.5, .1)

predicted_h5 = ggeffects::ggpredict(mod_h5a, c("self_referential [vals]")) %>%
  data.frame() %>%
  mutate(roi = "self-referential") %>%
  bind_rows(ggeffects::ggpredict(mod_h5b, c("social_cognitive [vals]")) %>%
              data.frame() %>%
              mutate(roi = "social cognitive")) %>%
  mutate(roi = factor(roi, levels = c("self-referential", "social cognitive")))

predicted_sub_h5 = ggeffects::ggpredict(mod_h5a, terms = c("self_referential [vals]", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(roi = "self-referential") %>%
  bind_rows(ggeffects::ggpredict(mod_h5b, c("social_cognitive [vals]", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(roi = "social cognitive")) %>%
  mutate(roi = factor(roi, levels = c("self-referential", "social cognitive")))

predicted_h5 %>%
  ggplot(aes(x = x, y = predicted, color = roi, fill = roi)) +
  stat_smooth(data = predicted_sub_h5, aes(group = group), geom ='line', method = "lm", alpha = .1, size = 1, se = FALSE) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .2, color = NA) +
  geom_line(size = 2) +
  facet_grid(~roi) +
  scale_color_manual(name = "", values = palette_roi) +
  scale_fill_manual(name = "", values = palette_roi) +
  labs(y = "predicted sharing intention\n", x = "\nROI activity (SD)") +
  plot_aes +
  theme(legend.position = "none")

H6 ROI activity ~ intervention condition

Compared to the control condition, the (a) self-focused condition will increase activity in the self-referential ROI, and the (b) other-focused condition will increase activity in the social cognitive ROI.

self-referential ROI

mod_h6a = lmer(self_referential ~ article_cond + (1 + article_cond | SID),
              data = merged_wide_alt,
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h6a = table_model(mod_h6a)

table_h6a %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 0.23 [0.12, 0.33] 84.12 4.38 < .001
other 0.11 [0.03, 0.19] 84.42 2.86 .005
self 0.13 [0.04, 0.21] 83.69 2.84 .006

summary

summary(mod_h6a)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: self_referential ~ article_cond + (1 + article_cond | SID)
##    Data: merged_wide_alt
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 17254.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4678 -0.6543 -0.0102  0.6474  3.5891 
## 
## Random effects:
##  Groups   Name              Variance Std.Dev. Corr       
##  SID      (Intercept)       0.18879  0.4345              
##           article_condother 0.04997  0.2235   -0.05      
##           article_condself  0.08492  0.2914    0.05  0.49
##  Residual                   0.97421  0.9870              
## Number of obs: 6014, groups:  SID, 85
## 
## Fixed effects:
##                   Estimate Std. Error       df t value  Pr(>|t|)    
## (Intercept)        0.22817    0.05204 84.12046   4.385 0.0000334 ***
## article_condother  0.11309    0.03950 84.41682   2.863   0.00529 ** 
## article_condself   0.12606    0.04441 83.69461   2.838   0.00569 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) artcl_cndt
## artcl_cndth -0.267           
## artcl_cndsl -0.176  0.493

social cognitive ROI

mod_h6b = lmer(social_cognitive ~ article_cond + (1 + article_cond | SID),
              data = merged_wide_alt,
              control = lmerControl(optimizer = "bobyqa"))

model table

table_h6b = table_model(mod_h6b)

table_h6b %>%
    kable()  %>%
    kableExtra::kable_styling()
term b [95% CI] df t p
intercept 0.27 [0.15, 0.39] 84.19 4.58 < .001
other 0.01 [-0.07, 0.08] 83.16 0.21 .837
self 0.05 [-0.04, 0.13] 84.03 1.11 .271

summary

summary(mod_h6b)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: social_cognitive ~ article_cond + (1 + article_cond | SID)
##    Data: merged_wide_alt
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 17292.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4694 -0.6574 -0.0019  0.6645  4.0424 
## 
## Random effects:
##  Groups   Name              Variance Std.Dev. Corr       
##  SID      (Intercept)       0.25179  0.5018              
##           article_condother 0.04215  0.2053   -0.45      
##           article_condself  0.07355  0.2712   -0.25  0.87
##  Residual                   0.98487  0.9924              
## Number of obs: 6014, groups:  SID, 85
## 
## Fixed effects:
##                    Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)        0.269101   0.058773 84.192870   4.579 0.000016 ***
## article_condother  0.007961   0.038457 83.160051   0.207    0.837    
## article_condself   0.047633   0.043001 84.025798   1.108    0.271    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) artcl_cndt
## artcl_cndth -0.461           
## artcl_cndsl -0.354  0.641

combined plot

predicted_h6 = ggeffects::ggpredict(mod_h6a, c("article_cond")) %>%
  data.frame() %>%
  mutate(atlas = "self-referential") %>%
  bind_rows(ggeffects::ggpredict(mod_h6b, c("article_cond")) %>%
              data.frame() %>%
              mutate(atlas = "social cognitive")) %>%
  mutate(x = factor(x, levels = c("self", "control", "other")),
         atlas = factor(atlas, levels = c("self-referential", "social cognitive")))

predicted_sub_h6 = ggeffects::ggpredict(mod_h6a, terms = c("article_cond", "SID"), type = "random") %>%
  data.frame() %>%
  mutate(atlas = "self-referential") %>%
  bind_rows(ggeffects::ggpredict(mod_h6b, c("article_cond", "SID"), type = "random") %>%
              data.frame() %>%
              mutate(atlas = "social cognitive")) %>%
  mutate(x = factor(x, levels = c("self", "control", "other")),
         atlas = factor(atlas, levels = c("self-referential", "social cognitive")))

predicted_h6 %>%
  ggplot(aes(x = x, y = predicted)) +
  stat_summary(data = predicted_sub_h6, aes(group = group), fun = "mean", geom = "line",
               size = .1, color = "grey50") +
  stat_summary(aes(group = group), fun = "mean", geom = "line", size = 1) +
  geom_pointrange(aes(color = x, ymin = conf.low, ymax = conf.high), size = .75) +
  facet_grid(~atlas) +
  scale_color_manual(name = "", values = palette_cond, guide = "none") +
  scale_alpha_manual(name = "", values = c(1, .5)) +
  labs(x = "", y = "ROI activity (SD)\n") +
  plot_aes

combined table

table_h4a %>% mutate(DV = "H4a: Self-relevance") %>%
  bind_rows(table_h4b %>% mutate(DV = "H4b: Social relevance")) %>%
  bind_rows(table_h5a %>% mutate(DV = "H5a: Sharing intention")) %>%
  bind_rows(table_h5b %>% mutate(DV = "H5b: Sharing intention")) %>%
  bind_rows(table_h6a %>% mutate(DV = "H6a: Self-referential ROI")) %>%
  bind_rows(table_h6b %>% mutate(DV = "H6b: Socia cognitive ROI")) %>%
  select(DV, everything()) %>%
  kable() %>%
  kable_styling()
DV term b [95% CI] df t p
H4a: Self-relevance intercept -0.01 [-0.09, 0.07] 84.62 -0.26 .794
H4a: Self-relevance self-referential 0.03 [0.01, 0.06] 83.66 2.40 .018
H4b: Social relevance intercept -0.01 [-0.09, 0.08] 84.07 -0.19 .847
H4b: Social relevance social cognitive 0.04 [0.01, 0.06] 82.98 2.86 .005
H5a: Sharing intention intercept -0.02 [-0.09, 0.06] 85.08 -0.45 .651
H5a: Sharing intention self-referential 0.06 [0.03, 0.09] 83.56 4.40 < .001
H5b: Sharing intention intercept -0.01 [-0.08, 0.06] 84.71 -0.26 .797
H5b: Sharing intention social cognitive 0.04 [0.02, 0.07] 83.49 3.41 .001
H6a: Self-referential ROI intercept 0.23 [0.12, 0.33] 84.12 4.38 < .001
H6a: Self-referential ROI other 0.11 [0.03, 0.19] 84.42 2.86 .005
H6a: Self-referential ROI self 0.13 [0.04, 0.21] 83.69 2.84 .006
H6b: Socia cognitive ROI intercept 0.27 [0.15, 0.39] 84.19 4.58 < .001
H6b: Socia cognitive ROI other 0.01 [-0.07, 0.08] 83.16 0.21 .837
H6b: Socia cognitive ROI self 0.05 [-0.04, 0.13] 84.03 1.11 .271

cite packages

report::cite_packages()
##   - Angelo Canty, B. D. Ripley (2024). _boot: Bootstrap R (S-Plus) Functions_. R package version 1.3-30. A. C. Davison, D. V. Hinkley (1997). _Bootstrap Methods and Their Applications_. Cambridge University Press, Cambridge. ISBN 0-521-57391-2, <doi:10.1017/CBO9780511802843>.
##   - Bates D, Mächler M, Bolker B, Walker S (2015). "Fitting Linear Mixed-Effects Models Using lme4." _Journal of Statistical Software_, *67*(1), 1-48. doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
##   - Bates D, Maechler M, Jagan M (2024). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.7-0, <https://CRAN.R-project.org/package=Matrix>.
##   - Grolemund G, Wickham H (2011). "Dates and Times Made Easy with lubridate." _Journal of Statistical Software_, *40*(3), 1-25. <https://www.jstatsoft.org/v40/i03/>.
##   - Hester J, Wickham H, Csárdi G (2024). _fs: Cross-Platform File System Operations Based on 'libuv'_. R package version 1.6.4, <https://CRAN.R-project.org/package=fs>.
##   - Kuznetsova A, Brockhoff PB, Christensen RHB (2017). "lmerTest Package: Tests in Linear Mixed Effects Models." _Journal of Statistical Software_, *82*(13), 1-26. doi:10.18637/jss.v082.i13 <https://doi.org/10.18637/jss.v082.i13>.
##   - Lüdecke D (2018). "ggeffects: Tidy Data Frames of Marginal Effects from Regression Models." _Journal of Open Source Software_, *3*(26), 772. doi:10.21105/joss.00772 <https://doi.org/10.21105/joss.00772>.
##   - Müller K, Wickham H (2023). _tibble: Simple Data Frames_. R package version 3.2.1, <https://CRAN.R-project.org/package=tibble>.
##   - R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
##   - Rinker TW, Kurkiewicz D (2018). _pacman: Package Management for R_. version 0.5.0, <http://github.com/trinker/pacman>.
##   - Wickham H (2016). _ggplot2: Elegant Graphics for Data Analysis_. Springer-Verlag New York. ISBN 978-3-319-24277-4, <https://ggplot2.tidyverse.org>.
##   - Wickham H (2023). _forcats: Tools for Working with Categorical Variables (Factors)_. R package version 1.0.0, <https://CRAN.R-project.org/package=forcats>.
##   - Wickham H (2023). _stringr: Simple, Consistent Wrappers for Common String Operations_. R package version 1.5.1, <https://CRAN.R-project.org/package=stringr>.
##   - Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to the tidyverse." _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
##   - Wickham H, Bryan J, Barrett M, Teucher A (2024). _usethis: Automate Package and Project Setup_. R package version 2.2.3, <https://CRAN.R-project.org/package=usethis>.
##   - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.4, <https://CRAN.R-project.org/package=dplyr>.
##   - Wickham H, Henry L (2023). _purrr: Functional Programming Tools_. R package version 1.0.2, <https://CRAN.R-project.org/package=purrr>.
##   - Wickham H, Hester J, Bryan J (2024). _readr: Read Rectangular Text Data_. R package version 2.1.5, <https://CRAN.R-project.org/package=readr>.
##   - Wickham H, Hester J, Chang W, Bryan J (2022). _devtools: Tools to Make Developing R Packages Easier_. R package version 2.4.5, <https://CRAN.R-project.org/package=devtools>.
##   - Wickham H, Vaughan D, Girlich M (2024). _tidyr: Tidy Messy Data_. R package version 1.3.1, <https://CRAN.R-project.org/package=tidyr>.
##   - Xie Y (2024). _knitr: A General-Purpose Package for Dynamic Report Generation in R_. R package version 1.47, <https://yihui.org/knitr/>. Xie Y (2015). _Dynamic Documents with R and knitr_, 2nd edition. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963, <https://yihui.org/knitr/>. Xie Y (2014). "knitr: A Comprehensive Tool for Reproducible Research in R." In Stodden V, Leisch F, Peng RD (eds.), _Implementing Reproducible Computational Research_. Chapman and Hall/CRC. ISBN 978-1466561595.
##   - Zhu H (2024). _kableExtra: Construct Complex Table with 'kable' and Pipe Syntax_. R package version 1.4.0, <https://CRAN.R-project.org/package=kableExtra>.