library(psych)
library(lmSupport)
## Registered S3 methods overwritten by 'lme4':
##   method                          from
##   cooks.distance.influence.merMod car 
##   influence.merMod                car 
##   dfbeta.influence.merMod         car 
##   dfbetas.influence.merMod        car
library(ggplot2)
## 
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
## 
##     %+%, alpha
library(ggpubr)
library(lme4)
## Loading required package: Matrix
library(lmerTest) 
## 
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
## 
##     lmer
## The following object is masked from 'package:stats':
## 
##     step
library(lme4)
library(plyr)
## 
## Attaching package: 'plyr'
## The following object is masked from 'package:ggpubr':
## 
##     mutate
library(tidyr)
## 
## Attaching package: 'tidyr'
## The following objects are masked from 'package:Matrix':
## 
##     expand, pack, unpack
library(sjPlot)
library(splines)
source('http://psych.colorado.edu/~jclab/R/mcSummaryLm.R')
library(car)
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:psych':
## 
##     logit
library(dfoptim)
library(optimx)
## Warning: package 'optimx' was built under R version 4.0.4
d <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/attention trust/attTrust_full_dataset.csv", header = T, stringsAsFactors = F)

#data for face information
df <- read.csv("C:/Users/Dani Grant/Dropbox/graduate school records/research projects/attention trust/Study 2/face_measures_cleaned.csv", header = T, stringsAsFactors = F)

# contrast codes for phrasing condition
d$trust_.5 <- ifelse(d$condition == "trust", .5, -.5)

#dummy codes for phrasing condition
d$trustYes_0 <- ifelse(d$trust_.5 == .5, 0, 1)
d$trustNo_0 <- ifelse(d$trust_.5 == -.5, 0, 1)

#mean center response time around grand mean
d$respTime_grand_c <- d$respTime - mean(d$respTime) 

#objective face measures
dface <- d %>% pivot_longer(c(faceLeft,faceRight), names_to = "faceSide", values_to = "face")
dwidth <- d %>% pivot_longer(c(fWtH_Ratio_L,fWtH_Ratio_R), names_to = "side", values_to = "fWtH_Ratio")
dhead <- d %>% pivot_longer(c(Upper_Head_Length_L,Upper_Head_Length_R), names_to = "side", values_to = "Upper_Head_Length")
dround <- d %>% pivot_longer(c(Face_Roundedness_L,Face_Roundedness_R), names_to = "side", values_to = "Face_Roundedness")
dheart <- d %>% pivot_longer(c(Face_Heartshapeness_L,Face_Heartshapeness_R), names_to = "side", values_to = "Face_Heartshapeness")
dprom <- d %>% pivot_longer(c(Cheekbone_Prominance_L,Cheekbone_Prominance_R), names_to = "side", values_to = "Cheekbone_Prominance")
dheight <- d %>% pivot_longer(c(Cheekbone_Height_L,Cheekbone_Height_R), names_to = "side", values_to = "Cheekbone_Height")
deye <- d %>% pivot_longer(c(Eye_Shape_L,Eye_Shape_R), names_to = "side", values_to = "Eye_Shape")
dsize <- d %>% pivot_longer(c(Eye_Size_L,Eye_Size_R), names_to = "side", values_to = "Eye_Size")
dpupil <- d %>% pivot_longer(c(Pupil_Distance_L,Pupil_Distance_R), names_to = "side", values_to = "Pupil_Distance")
dfull <- d %>% pivot_longer(c(Lip_Fullness_L,Lip_Fullness_R), names_to = "side", values_to = "Lip_Fullness")
dnose <- d %>% pivot_longer(c(Nose_Shape_L,Nose_Shape_R), names_to = "side", values_to = "Nose_Shape")

#subjective face measures
dtrust <- d %>% pivot_longer(c(trustL,trustR), names_to = "ide", values_to = "trust")
dappr <- d %>% pivot_longer(c(apprL,apprR), names_to = "ide", values_to = "appr")
dattr <- d %>% pivot_longer(c(attrL,attrR), names_to = "ide", values_to = "attractive")
dethical <- d %>% pivot_longer(c(ethicL,ethicR), names_to = "ide", values_to = "ethical")
dfamiliar <- d %>% pivot_longer(c(famL,famR), names_to = "ide", values_to = "familiar")
dfem_masc <- d %>% pivot_longer(c(fem_mascL,fem_mascR), names_to = "ide", values_to = "fem_masc")
dfriendly <- d %>% pivot_longer(c(friendL,friendR), names_to = "ide", values_to = "friendly")
dhelp <- d %>% pivot_longer(c(helpL,helpR), names_to = "ide", values_to = "helpful")
dhonest <- d %>% pivot_longer(c(honestL,honestR), names_to = "ide", values_to = "honest")
dmoral <- d %>% pivot_longer(c(moralL,moralR), names_to = "ide", values_to = "moral")
dsincere <- d %>% pivot_longer(c(sincereL,sincereR), names_to = "ide", values_to = "sincere")


# create new long long dataset
d2 <- data.frame(dface$participant)
colnames(d2)[colnames(d2)=="dface.participant"] <- "pt"
d2$trial <- dface$trial
d2$face <- dface$face
d2$cuedFace <- dface$cuedFace
d2$trust_.5 <- dface$trust_.5
d2$trustYes_0 <- dface$trustYes_0
d2$trustNo_0 <- dface$trustNo_0
d2$cuedFaceTrusted_1 <- dface$cuedFaceTrusted_1
d2$stim_female_.5 <- dface$stim_female_.5
d2$pt_female_.5 <- dface$pt_female_.5
d2$sideCuedRight_.5 <- dface$sideCuedRight_.5

d2$fWtH_Ratio <- dwidth$fWtH_Ratio
d2$Upper_Head_Length <- dhead$Upper_Head_Length
d2$Face_Roundedness <- dround$Face_Roundedness
d2$Face_Heartshapeness <- dheart$Face_Heartshapeness
d2$Cheekbone_Prominance <- dprom$Cheekbone_Prominance
d2$Cheekbone_Height <- dheight$Cheekbone_Height
d2$Eye_Shape <- deye$Eye_Shape
d2$Eye_Size <- dsize$Eye_Size
d2$Pupil_Distance <- dpupil$Pupil_Distance
d2$Lip_Fullness <- dfull$Lip_Fullness
d2$Nose_Shape <- dnose$Nose_Shape

d2$trustworthy <- dtrust$trust
d2$ethical <- dethical$ethical
d2$helpful <- dhelp$helpful
d2$honest <- dhonest$honest
d2$moral <- dmoral$moral
d2$sincere <- dsincere$sincere

d2$appr <- dappr$appr #appreciative?
d2$familiar <- dfamiliar$familiar
d2$fem_masc <- dfem_masc$fem_masc
d2$friendly <- dfriendly$friendly
d2$attractive <- dattr$attractive

## average measures of trustworthiness
d2$overallTrust <- ((d2$trustworthy + d2$moral + d2$ethical + d2$helpful + d2$honest + d2$sincere)/6)

psych::alpha(d2[c(23:28)]) #alpha = .98
## 
## Reliability analysis   
## Call: psych::alpha(x = d2[c(23:28)])
## 
##   raw_alpha std.alpha G6(smc) average_r S/N     ase mean   sd median_r
##       0.98      0.98    0.98      0.89  49 0.00032    3 0.47     0.89
## 
##  lower alpha upper     95% confidence boundaries
## 0.98 0.98 0.98 
## 
##  Reliability if an item is dropped:
##             raw_alpha std.alpha G6(smc) average_r S/N alpha se   var.r med.r
## trustworthy      0.97      0.97    0.97      0.89  39  0.00041 0.00022  0.88
## ethical          0.98      0.98    0.97      0.89  40  0.00039 0.00018  0.89
## helpful          0.98      0.98    0.97      0.90  43  0.00037 0.00022  0.89
## honest           0.98      0.98    0.97      0.90  43  0.00037 0.00021  0.89
## moral            0.97      0.98    0.97      0.89  39  0.00040 0.00015  0.89
## sincere          0.98      0.98    0.97      0.89  41  0.00039 0.00030  0.89
## 
##  Item statistics 
##                n raw.r std.r r.cor r.drop mean   sd
## trustworthy 9578  0.96  0.96  0.95   0.95  3.1 0.52
## ethical     9578  0.95  0.96  0.95   0.94  2.9 0.46
## helpful     9578  0.95  0.94  0.93   0.92  2.9 0.51
## honest      9578  0.95  0.95  0.93   0.92  3.0 0.49
## moral       9578  0.96  0.96  0.95   0.94  2.9 0.47
## sincere     9578  0.95  0.95  0.94   0.93  3.0 0.49

1. cuedFaceTrusted ~ 1 + (1|pt)

m1 <- glmer(cuedFaceTrusted_1 ~ 1 +
              (1 | participant), family = binomial(), data = d)
summary(m1)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ 1 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6627.5   6640.4  -3311.7   6623.5     4787 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2110 -1.0128  0.8339  0.9540  1.1528 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06439  0.2537  
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06618    0.03379   1.958   0.0502 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
prop.table(table(d$cuedFaceTrusted_1))
## 
##         0         1 
## 0.4836083 0.5163917
OR1 = exp(0.06618)

Interpretation: \(\beta_0\): Cued face is trusted with marginal more significance compared with the uncued face (trusted = 51.6%, OR = 1.07, p = .050). ***

2. cuedFaceTrusted ~ sideCued + (1|pt)

m2 <- glmer(cuedFaceTrusted_1 ~ sideCuedRight_.5 + 
              (1 | participant), family = binomial("logit"), data = d)

summary(m2)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ sideCuedRight_.5 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6629.2   6648.6  -3311.6   6623.2     4786 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2207 -1.0178  0.8400  0.9582  1.1618 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06452  0.254   
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##                  Estimate Std. Error z value Pr(>|z|)  
## (Intercept)       0.06613    0.03380   1.957   0.0504 .
## sideCuedRight_.5 -0.02966    0.05864  -0.506   0.6130  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## sdCdRght_.5 0.003
ORB1 = exp(0.06613)

ORB2 = exp(-0.02966)

Interpretation: \(\beta_0\): Across side cued, on average, the odds of the cued face being chosen is marginally more significant than the uncued face (OR = 1.07, p = .050).

\(\beta_1\): As you move from left side cued to right side cued,on average there is significant difference in the odds for choosing the cued (OR = .97, p = .613). ***

3. cuedFaceTrusted ~ phrasing_condition + (1|pt)

m3 <- glmer(cuedFaceTrusted_1 ~ trust_.5 +
              (1 | participant), family = binomial, data = d)
summary(m3)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6626.1   6645.6  -3310.1   6620.1     4786 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2291 -1.0123  0.8211  0.9646  1.1350 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06075  0.2465  
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06196    0.03362   1.843   0.0653 .
## trust_.5     0.12278    0.06725   1.826   0.0679 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.067
exp(0.06196)
## [1] 1.06392
exp(0.12278)
## [1] 1.130636

Interpretation: \(\beta_0\): Across phrasing condition, on average, the odds of the cued face being chosen is marginally more significant than the uncued face (OR = 1.06, p = .065).

\(\beta_1\): As you move from trust phrasing to no trust phrasing condition,on average there is a marginally significant difference in the odds for choosing the cued face (OR = 1.13, p = .068). ***

3a. Trust Condition

m3a <- glmer(cuedFaceTrusted_1 ~ trustYes_0 +
              (1 | participant), family = binomial, data = d)
summary(m3a)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6626.1   6645.6  -3310.1   6620.1     4786 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2291 -1.0123  0.8211  0.9646  1.1350 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06075  0.2465  
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12335    0.04592   2.686  0.00722 **
## trustYes_0  -0.12277    0.06724  -1.826  0.06788 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.683
ORB0 = exp(0.12335)

Interpretation: \(\beta_0\): Within the trust phrasing condition, on average, the odds of the cued face being chosen is significantly more likely than the uncued face being chosen (OR = 1.13, p = .007). ***

3b. Not Trust Condition

m3b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 +
              (1 | participant), family = binomial, data = d)
summary(m3b)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6626.1   6645.6  -3310.1   6620.1     4786 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2291 -1.0123  0.8211  0.9646  1.1350 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06075  0.2465  
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) 0.0005722  0.0491263   0.012   0.9907  
## trustNo_0   0.1227805  0.0672452   1.826   0.0679 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.731
exp(0.0005722)
## [1] 1.000572

Interpretation: \(\beta_0\): Within the not trust phrasing condition, on average, the odds of the cued face being chosen is not significantly more likely than the uncued face being chosen (OR = 1.00, p = .991). ***

3c. Graph

p <- plot_model(m3, type = "pred", terms = "trust_.5")
p + labs(title = "probability for cued face trusted given question phrasing",
         x = "Not Trust                                                                                                                                    Trust", 
         y = "probability of cued face chosen")

mean(d$cuedFaceTrusted_1[d$trust_.5 == .5])
## [1] 0.530445
mean(d$cuedFaceTrusted_1[d$trust_.5 == -.5])
## [1] 0.5002245

4. cuedFaceTrusted ~ trust_condition * participant gender + (1 | pt)

m6 <- glmer(cuedFaceTrusted_1 ~ trust_.5 * pt_female_.5 + 
              (1 | participant), family = binomial, data = d)
summary(m6)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * pt_female_.5 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6629.7   6662.1  -3309.8   6619.7     4784 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2341 -1.0148  0.8178  0.9596  1.1189 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06025  0.2454  
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##                       Estimate Std. Error z value Pr(>|z|)  
## (Intercept)            0.06306    0.03471   1.817   0.0693 .
## trust_.5               0.11121    0.06943   1.602   0.1092  
## pt_female_.5          -0.01090    0.06943  -0.157   0.8752  
## trust_.5:pt_female_.5  0.09219    0.13886   0.664   0.5067  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) trs_.5 pt__.5
## trust_.5    -0.061              
## pt_femal_.5 -0.252  0.004       
## trs_.5:__.5  0.004 -0.252 -0.061

Interpretation: \(\beta_1\): there is no main effect for participant gender, p = .875.

\(\beta_2\): there is no significant interaction between participant gender and phrasing condition, p = .507.

5. cuedFaceTrusted ~ condition * stimulus gender + (1 | pt)

m5 <- glmer(cuedFaceTrusted_1 ~ trust_.5 * stim_female_.5 + 
              (1 | participant), family = binomial, data = d)
summary(m5)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 * stim_female_.5 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6629.8   6662.2  -3309.9   6619.8     4784 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2352 -1.0176  0.8331  0.9604  1.1399 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06053  0.246   
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##                          Estimate Std. Error z value Pr(>|z|)  
## (Intercept)              0.062463   0.037101   1.684   0.0923 .
## trust_.5                 0.104302   0.074206   1.406   0.1598  
## stim_female_.5          -0.001444   0.066532  -0.022   0.9827  
## trust_.5:stim_female_.5  0.078217   0.133065   0.588   0.5567  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) trs_.5 st__.5
## trust_.5    -0.076              
## stim_fml_.5 -0.424  0.041       
## trs_.5:__.5  0.041 -0.424 -0.078
exp(0.05231) #beta0 
## [1] 1.053702
exp(0.14444) #beta1 
## [1] 1.155392
exp(-0.01418) #beta2 
## [1] 0.9859201
exp(0.03078) #beta3
## [1] 1.031259

Interpretation: For a model that includes stimulus gender, phrasing condition, and their interaction, there are no significant findings. ***

5a. Trust Condition

m5a <- glmer(cuedFaceTrusted_1 ~ trustYes_0*stim_female_.5 + 
              (1 | participant), family = binomial, data = d)
summary(m5a)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 * stim_female_.5 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6629.8   6662.2  -3309.9   6619.8     4784 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2352 -1.0176  0.8331  0.9604  1.1399 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06053  0.246   
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##                           Estimate Std. Error z value Pr(>|z|)  
## (Intercept)                0.11461    0.05044   2.272   0.0231 *
## trustYes_0                -0.10431    0.07421  -1.406   0.1598  
## stim_female_.5             0.03767    0.09033   0.417   0.6767  
## trustYes_0:stim_female_.5 -0.07822    0.13307  -0.588   0.5566  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) trsY_0 st__.5
## trustYes_0  -0.680              
## stim_fml_.5 -0.415  0.282       
## trsY_0:__.5  0.282 -0.424 -0.679
exp(0.11461)
## [1] 1.121436

- plot

p <- plot_model(m5a, type = "pred", terms = "trustYes_0")
p + labs(title = "probability for cued face trusted given trust condition",
         subtitle = "controlling for stimulus gender", 
         x = "trust = 0", 
         y = "probability of cued face trusted", 
         tag = "A")

Interpretation: However, the simple effect of the trust phrasing condition remains significant, OR = 1.12, p = .023. ***

5b. Not Trust Condition

m5b <- glmer(cuedFaceTrusted_1 ~ trustNo_0*stim_female_.5 + 
              (1 | participant), family = binomial, data = d)
summary(m5b)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 * stim_female_.5 + (1 | participant)
##    Data: d
## 
##      AIC      BIC   logLik deviance df.resid 
##   6629.8   6662.2  -3309.9   6619.8     4784 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.2352 -1.0176  0.8331  0.9604  1.1399 
## 
## Random effects:
##  Groups      Name        Variance Std.Dev.
##  participant (Intercept) 0.06053  0.246   
## Number of obs: 4789, groups:  participant, 221
## 
## Fixed effects:
##                          Estimate Std. Error z value Pr(>|z|)
## (Intercept)               0.01031    0.05442   0.189    0.850
## trustNo_0                 0.10430    0.07420   1.406    0.160
## stim_female_.5           -0.04055    0.09771  -0.415    0.678
## trustNo_0:stim_female_.5  0.07822    0.13306   0.588    0.557
## 
## Correlation of Fixed Effects:
##             (Intr) trsN_0 st__.5
## trustNo_0   -0.733              
## stim_fml_.5 -0.431  0.316       
## trsN_0:__.5  0.317 -0.424 -0.734

Interpretation: The simple effect of the not trust phrasing condition remains non-significant, p = .850. ***

SUBJECTIVE FACE MEASURES

descriptives

describe(d2$trustworthy)

random intercepts

a. cuedFaceTrusted ~ phrasing_cc + (1|pt) + (1|face)

m6 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (1 | face), family = binomial("logit"), data = d2)
summary(m6)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (1 | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13109.5  13138.2  -6550.7  13101.5     9574 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6530 -0.9891  0.6654  0.9419  1.5202 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  pt     (Intercept) 0.17289  0.4158  
##  face   (Intercept) 0.01351  0.1162  
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06373    0.03649   1.747   0.0807 .
## trust_.5     0.12890    0.07006   1.840   0.0658 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063

b. cuedFaceTrusted ~ Trust + (1|pt) + (1|face)

m6a <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (1 | face), family = binomial("logit"), data = d2)
summary(m6a)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (1 | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13109.5  13138.2  -6550.7  13101.5     9574 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6530 -0.9891  0.6654  0.9419  1.5202 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  pt     (Intercept) 0.17289  0.4158  
##  face   (Intercept) 0.01351  0.1162  
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12818    0.04895   2.618  0.00884 **
## trustYes_0  -0.12889    0.07006  -1.840  0.06581 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.668

c. cuedFaceTrusted ~ Not Trust + (1|pt) + (1|face)

m6b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (1 | face), family = binomial("logit"), data = d2)
summary(m6b)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (1 | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13109.5  13138.2  -6550.7  13101.5     9574 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6530 -0.9891  0.6654  0.9419  1.5202 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  pt     (Intercept) 0.17289  0.4158  
##  face   (Intercept) 0.01351  0.1162  
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.0007167  0.0521536  -0.014   0.9890  
## trustNo_0    0.1288966  0.0700580   1.840   0.0658 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.716

trustworthy

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + (trustworthy || face)

m7 <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (trustworthy || face), family = binomial("logit"), data = d2)

summary(m7)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (trustworthy || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.4  13147.2  -6550.7  13101.4     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6733 -0.9899  0.6644  0.9421  1.5194 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev.
##  pt     (Intercept) 0.1728770 0.41578 
##  face   (Intercept) 0.0051840 0.07200 
##  face.1 trustworthy 0.0008371 0.02893 
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06278    0.03660   1.715   0.0863 .
## trust_.5     0.12881    0.07006   1.839   0.0660 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
exp(0.06278)
## [1] 1.064793
exp(0.12881)
## [1] 1.137474

Interpretation: \(\beta_0\): collapsing across phrasing condition, the cued face is trusted marginally more than the uncued face, OR = 1.06, p = .086.

\(\beta_1\): As you move from trust phrasing to no trust phrasing condition,on average there is a marginally significant difference in the odds for choosing the cued face (OR = 1.14, p = .066). ***

b. cuedFaceTrusted ~ Trust + (1 | pt) + (trustworthy || face) | = singular

m7a <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (trustworthy || face), family = binomial("logit"), data = d2)
summary(m7a)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (trustworthy || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.4  13147.2  -6550.7  13101.4     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6733 -0.9899  0.6644  0.9421  1.5194 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  pt     (Intercept) 0.172877 0.41578 
##  face   (Intercept) 0.005186 0.07201 
##  face.1 trustworthy 0.000837 0.02893 
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12719    0.04905   2.593  0.00951 **
## trustYes_0  -0.12881    0.07006  -1.839  0.06597 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.667
exp(0.12719)
## [1] 1.135633

Interpretation: \(\beta_0\): In the trust phrasing condition, the cued face is trusted significantly more than the uncued face, OR = 1.14, p = .0095. ***

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + (trustworthy || face) | = singular

m7b <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (trustworthy || face), family = binomial("logit"), data = d2)
summary(m7b)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (trustworthy || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.4  13147.2  -6550.7  13101.4     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6733 -0.9899  0.6644  0.9421  1.5194 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev.
##  pt     (Intercept) 0.1728765 0.41578 
##  face   (Intercept) 0.0051842 0.07200 
##  face.1 trustworthy 0.0008371 0.02893 
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.001622   0.052227  -0.031    0.975  
## trustNo_0    0.128812   0.070056   1.839    0.066 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.715
exp(-0.001622)
## [1] 0.9983793

Interpretation: \(\beta_0\): In the not trust phrasing condition, there is no difference for cued of uncued face being trusted, OR = 1.00, p = .975. ***

honest

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( honest | face)

honest.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (honest | face), family = binomial("logit"), data = d2)
summary(honest.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (honest | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.6  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6755 -0.9892  0.6641  0.9414  1.5198 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 1.729e-01 0.415812     
##  face   (Intercept) 9.056e-05 0.009516     
##         honest      1.234e-03 0.035130 0.99
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06174    0.03672   1.681   0.0927 .
## trust_.5     0.12876    0.07006   1.838   0.0661 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.062

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( honest | face)

honest.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (honest || face), family = binomial("logit"), data = d2)
summary(honest.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (honest || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.3  13147.1  -6550.6  13101.3     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6760 -0.9892  0.6640  0.9414  1.5199 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev.
##  pt     (Intercept) 0.1728990 0.41581 
##  face   (Intercept) 0.0007383 0.02717 
##  face.1 honest      0.0013766 0.03710 
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.12607    0.04914   2.566   0.0103 *
## trustYes_0  -0.12875    0.07006  -1.838   0.0661 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.666

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( honest | face)

honest.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (honest | face), family = binomial("logit"), data = d2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
summary(honest.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (honest | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.6  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6755 -0.9892  0.6641  0.9414  1.5198 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1728997 0.415812     
##  face   (Intercept) 0.0000906 0.009519     
##         honest      0.0012336 0.035122 0.99
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.002634   0.052305  -0.050   0.9598  
## trustNo_0    0.128755   0.070059   1.838   0.0661 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.714
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate  Hessian with 1 negative eigenvalues

sincere

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( sincere | face)

sincere.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (sincere | face), family = binomial("logit"), data = d2)
summary(sincere.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (sincere | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.7  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6700 -0.9897  0.6658  0.9415  1.5202 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1729147 0.41583      
##  face   (Intercept) 0.0007318 0.02705      
##         sincere     0.0008863 0.02977  1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06219    0.03669   1.695   0.0901 .
## trust_.5     0.12865    0.07006   1.836   0.0663 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.062

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( sincere | face)

sincere.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (sincere | face), family = binomial("logit"), data = d2)
summary(sincere.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (sincere | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.7  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6701 -0.9897  0.6658  0.9415  1.5202 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1729155 0.41583      
##  face   (Intercept) 0.0007312 0.02704      
##         sincere     0.0008865 0.02977  1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.12651    0.04913   2.575   0.0100 *
## trustYes_0  -0.12865    0.07007  -1.836   0.0663 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.667

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( sincere | face)

sincere.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (sincere | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(sincere.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (sincere | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.7  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6700 -0.9897  0.6658  0.9415  1.5202 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1729146 0.41583      
##  face   (Intercept) 0.0007317 0.02705      
##         sincere     0.0008863 0.02977  1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.002141   0.052275  -0.041   0.9673  
## trustNo_0    0.128652   0.070064   1.836   0.0663 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.714
## convergence code: 0
## boundary (singular) fit: see ?isSingular

helpful

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( helpful | face)

helpful.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (helpful | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(helpful.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (helpful | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.5  13156.5  -6550.7  13101.5     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6599 -0.9895  0.6654  0.9418  1.5183 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 1.729e-01 0.415788     
##  face   (Intercept) 8.301e-03 0.091109     
##         helpful     7.406e-05 0.008606 1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06353    0.03653   1.739   0.0820 .
## trust_.5     0.12885    0.07006   1.839   0.0659 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( helpful | face)

helpful.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (helpful | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(helpful.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (helpful | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.5  13156.5  -6550.7  13101.5     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6599 -0.9895  0.6654  0.9418  1.5183 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 1.729e-01 0.415786     
##  face   (Intercept) 8.301e-03 0.091108     
##         helpful     7.408e-05 0.008607 1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12795    0.04899   2.612  0.00901 **
## trustYes_0  -0.12885    0.07006  -1.839  0.06590 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.668
## convergence code: 0
## boundary (singular) fit: see ?isSingular

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( helpful | face)

helpful.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (helpful | face), family = binomial("logit"), data = d2)
summary(helpful.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (helpful | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.5  13156.5  -6550.7  13101.5     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6599 -0.9895  0.6654  0.9418  1.5183 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 1.729e-01 0.415787     
##  face   (Intercept) 8.299e-03 0.091096     
##         helpful     7.414e-05 0.008611 1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.0008974  0.0521745  -0.017   0.9863  
## trustNo_0    0.1288455  0.0700575   1.839   0.0659 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.715

moral

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( moral | face)

moral.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (moral | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(moral.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (moral | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13112.9  13156.0  -6550.5  13100.9     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6998 -0.9890  0.6594  0.9409  1.5219 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172954 0.41588       
##  face   (Intercept) 0.003756 0.06129       
##         moral       0.003600 0.06000  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06108    0.03661   1.668   0.0952 .
## trust_.5     0.12863    0.07007   1.836   0.0664 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( moral | face)

moral.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (moral | face), family = binomial("logit"), data = d2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00326138 (tol = 0.002, component 1)
summary(moral.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (moral | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13112.9  13156.0  -6550.5  13100.9     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6997 -0.9890  0.6594  0.9409  1.5219 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172958 0.41588       
##  face   (Intercept) 0.003716 0.06096       
##         moral       0.003586 0.05989  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.12539    0.04906   2.556   0.0106 *
## trustYes_0  -0.12862    0.07007  -1.836   0.0664 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.667
## convergence code: 0
## Model failed to converge with max|grad| = 0.00326138 (tol = 0.002, component 1)

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( moral | face)

moral.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (moral | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(moral.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (moral | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13112.9  13156.0  -6550.5  13100.9     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6998 -0.9890  0.6594  0.9409  1.5219 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172953 0.41588       
##  face   (Intercept) 0.003756 0.06129       
##         moral       0.003600 0.06000  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.003239   0.052234  -0.062   0.9506  
## trustNo_0    0.128630   0.070067   1.836   0.0664 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.715
## convergence code: 0
## boundary (singular) fit: see ?isSingular

ethical

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( ethical | face)

ethical.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (ethical | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(ethical.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (ethical | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13112.6  13155.6  -6550.3  13100.6     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7285 -0.9891  0.6607  0.9405  1.5348 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172998 0.41593       
##  face   (Intercept) 0.011453 0.10702       
##         ethical     0.005594 0.07479  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06123    0.03653   1.676   0.0937 .
## trust_.5     0.12864    0.07007   1.836   0.0664 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( ethical | face)

ethical.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (ethical | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(ethical.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (ethical | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13112.6  13155.6  -6550.3  13100.6     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7285 -0.9891  0.6607  0.9405  1.5349 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172996 0.41593       
##  face   (Intercept) 0.011462 0.10706       
##         ethical     0.005596 0.07481  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.12556    0.04899   2.563   0.0104 *
## trustYes_0  -0.12866    0.07007  -1.836   0.0663 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.668
## convergence code: 0
## boundary (singular) fit: see ?isSingular

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( ethical | face)

ethical.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (ethical | face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(ethical.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (ethical | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13112.6  13155.6  -6550.3  13100.6     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7285 -0.9891  0.6607  0.9405  1.5348 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.173001 0.41593       
##  face   (Intercept) 0.011453 0.10702       
##         ethical     0.005594 0.07479  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.003086   0.052189  -0.059   0.9528  
## trustNo_0    0.128637   0.070074   1.836   0.0664 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.716
## convergence code: 0
## boundary (singular) fit: see ?isSingular

attractive

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( attractive | face)

attractive.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (attractive | face), family = binomial("logit"), data = d2)
summary(attractive.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (attractive | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.1  13156.1  -6550.5  13101.1     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6807 -0.9892  0.6609  0.9413  1.5171 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.17290  0.41581       
##  face   (Intercept) 0.09908  0.31477       
##         attractive  0.00593  0.07701  -0.96
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06161    0.03657   1.685   0.0920 .
## trust_.5     0.12892    0.07006   1.840   0.0657 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( attractive | face)

attractive.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (attractive | face), family = binomial("logit"), data = d2)
summary(attractive.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (attractive | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.1  13156.1  -6550.5  13101.1     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6807 -0.9892  0.6609  0.9413  1.5171 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.17290  0.41581       
##  face   (Intercept) 0.09908  0.31477       
##         attractive  0.00593  0.07701  -0.96
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.12607    0.04901   2.572   0.0101 *
## trustYes_0  -0.12892    0.07006  -1.840   0.0657 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.668

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( attractive | face)

attractive.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (attractive | face), family = binomial("logit"), data = d2)
summary(attractive.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (attractive | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.1  13156.1  -6550.5  13101.1     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6807 -0.9892  0.6609  0.9413  1.5171 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.17290  0.4158        
##  face   (Intercept) 0.09907  0.3148        
##         attractive  0.00593  0.0770   -0.96
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.002858   0.052212  -0.055   0.9563  
## trustNo_0    0.128924   0.070060   1.840   0.0657 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.715

fem_masc

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( fem_masc | face)

fem_masc.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (fem_masc | face), family = binomial("logit"), data = d2)
summary(fem_masc.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (fem_masc | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.2  13156.2  -6550.6  13101.2     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6897 -0.9897  0.6656  0.9415  1.5236 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172797 0.41569       
##  face   (Intercept) 0.035732 0.18903       
##         fem_masc    0.002566 0.05066  -0.89
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06314    0.03658   1.726   0.0843 .
## trust_.5     0.12870    0.07005   1.837   0.0662 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( fem_masc | face)

fem_masc.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (fem_masc | face), family = binomial("logit"), data = d2)
summary(fem_masc.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (fem_masc | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.2  13156.2  -6550.6  13101.2     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6897 -0.9897  0.6656  0.9415  1.5236 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172796 0.41569       
##  face   (Intercept) 0.035735 0.18904       
##         fem_masc    0.002566 0.05066  -0.89
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12749    0.04902   2.601   0.0093 **
## trustYes_0  -0.12870    0.07005  -1.837   0.0662 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.667

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( fem_masc | face)

fem_masc.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (fem_masc | face), family = binomial("logit"), data = d2)
summary(fem_masc.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (fem_masc | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.2  13156.2  -6550.6  13101.2     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6897 -0.9897  0.6656  0.9415  1.5236 
## 
## Random effects:
##  Groups Name        Variance Std.Dev. Corr 
##  pt     (Intercept) 0.172797 0.41569       
##  face   (Intercept) 0.035739 0.18905       
##         fem_masc    0.002566 0.05066  -0.89
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.001206   0.052210  -0.023   0.9816  
## trustNo_0    0.128700   0.070047   1.837   0.0662 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.715

friendly

a. cuedFaceTrusted ~ phrasing_cc + (1 | pt) + ( friendly | face)

friendly.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (friendly | face), family = binomial("logit"), data = d2)
summary(friendly.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (friendly | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.6  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6687 -0.9896  0.6667  0.9419  1.5199 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1728887 0.41580      
##  face   (Intercept) 0.0004587 0.02142      
##         friendly    0.0012295 0.03506  1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06340    0.03648   1.738   0.0822 .
## trust_.5     0.12858    0.07006   1.835   0.0665 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063

b. cuedFaceTrusted ~ Trust + (1 | pt) + ( friendly | face)

friendly.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (friendly | face), family = binomial("logit"), data = d2)
summary(friendly.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (friendly | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.6  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6687 -0.9896  0.6667  0.9419  1.5199 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1728891 0.41580      
##  face   (Intercept) 0.0004579 0.02140      
##         friendly    0.0012300 0.03507  1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12769    0.04895   2.609  0.00909 **
## trustYes_0  -0.12858    0.07006  -1.835  0.06647 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.669

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + ( friendly | face)

friendly.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (friendly | face), family = binomial("logit"), data = d2)
summary(friendly.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (friendly | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.3  13156.3  -6550.6  13101.3     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6687 -0.9896  0.6667  0.9419  1.5199 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. Corr
##  pt     (Intercept) 0.1728879 0.41580      
##  face   (Intercept) 0.0004581 0.02140      
##         friendly    0.0012299 0.03507  1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##               Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.0008858  0.0521472  -0.017   0.9864  
## trustNo_0    0.1285750  0.0700617   1.835   0.0665 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.716

overallTrust

a. cuedFaceTrusted ~ phrasing condition + (1 | pt) + (overallTrust | face) | & || = singular

overall.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (overallTrust | face), family = binomial("logit"), data = d2)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
summary(overall.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (overallTrust | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.2  13156.2  -6550.6  13101.2     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6854 -0.9895  0.6620  0.9415  1.5204 
## 
## Random effects:
##  Groups Name         Variance  Std.Dev. Corr 
##  pt     (Intercept)  1.729e-01 0.41582       
##  face   (Intercept)  7.327e-05 0.00856       
##         overallTrust 1.718e-03 0.04145  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06198    0.03663   1.692   0.0906 .
## trust_.5     0.12869    0.07006   1.837   0.0662 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.062
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

b. cuedFaceTrusted ~ Trust + (1 | pt) + (overallTrust | face) | & || = singular

overall.m.trust <- glmer(cuedFaceTrusted_1 ~ trustYes_0 + 
              (1 | pt) + 
              (overallTrust || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(overall.m.trust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustYes_0 + (1 | pt) + (overallTrust ||  
##     face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.2  13147.1  -6550.6  13101.2     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6835 -0.9895  0.6627  0.9416  1.5203 
## 
## Random effects:
##  Groups Name         Variance Std.Dev.
##  pt     (Intercept)  0.172905 0.41582 
##  face   (Intercept)  0.000000 0.00000 
##  face.1 overallTrust 0.001499 0.03872 
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.12644    0.04895   2.583  0.00979 **
## trustYes_0  -0.12870    0.07006  -1.837  0.06621 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## trustYes_0 -0.668
## convergence code: 0
## boundary (singular) fit: see ?isSingular

c. cuedFaceTrusted ~ Not Trust + (1 | pt) + (overallTrust | face) | & || = singular

overall.m.notrust <- glmer(cuedFaceTrusted_1 ~ trustNo_0 + 
              (1 | pt) + 
              (overallTrust || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(overall.m.notrust)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trustNo_0 + (1 | pt) + (overallTrust || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.2  13147.1  -6550.6  13101.2     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6835 -0.9895  0.6627  0.9416  1.5203 
## 
## Random effects:
##  Groups Name         Variance  Std.Dev. 
##  pt     (Intercept)  1.729e-01 4.158e-01
##  face   (Intercept)  4.003e-09 6.327e-05
##  face.1 overallTrust 1.499e-03 3.872e-02
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##              Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.002268   0.052152  -0.043   0.9653  
## trustNo_0    0.128710   0.070058   1.837   0.0662 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## trustNo_0 -0.716
## convergence code: 0
## boundary (singular) fit: see ?isSingular
trustworthy.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
           (1 | pt) +
           (overallTrust |  face), family = binomial("logit"), verbose = T, data = d2)
## start par. =  1 1 0 1 fn =  13736.35 
## At return
## eval: 2911 fn:      13101.231 par: 0.415815 0.00854046 -0.0412693 0.00380563
## (NM) 20: f = 13101.2 at   0.415815 0.00854046 -0.0412693 0.00380563  0.0608942   0.126491
## (NM) 40: f = 13101.2 at   0.415815 0.00854046 -0.0412693 0.00380563  0.0608942   0.126491
## (NM) 60: f = 13101.2 at   0.415824 0.00851983 -0.0413349 0.00471163  0.0616698   0.128017
## (NM) 80: f = 13101.2 at   0.415727 0.00928089 -0.0414669 0.00394368  0.0621024   0.129027
## (NM) 100: f = 13101.2 at   0.415769 0.00901103 -0.0413881 0.00398998  0.0620506    0.12879
## (NM) 120: f = 13101.2 at   0.415819 0.00864451 -0.0412825 0.00406955  0.0619618   0.128603
## (NM) 140: f = 13101.2 at   0.415819 0.00860051 -0.0412716 0.00405697  0.0620051   0.128669
## (NM) 160: f = 13101.2 at   0.415809 0.00857972 -0.0412561 0.00406034  0.0619942   0.128675
## (NM) 180: f = 13101.2 at   0.415815 0.00856227  -0.041251 0.00406071  0.0619851   0.128703
## (NM) 200: f = 13101.2 at   0.415816 0.00855361 -0.0412497 0.00406138  0.0619812   0.128694
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?
summary(trustworthy.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (overallTrust | face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13113.2  13156.2  -6550.6  13101.2     9572 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6854 -0.9895  0.6620  0.9415  1.5204 
## 
## Random effects:
##  Groups Name         Variance  Std.Dev. Corr 
##  pt     (Intercept)  1.729e-01 0.41582       
##  face   (Intercept)  7.327e-05 0.00856       
##         overallTrust 1.718e-03 0.04145  -1.00
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06198    0.03663   1.692   0.0906 .
## trust_.5     0.12869    0.07006   1.837   0.0662 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.062
## convergence code: 0
## Model is nearly unidentifiable: large eigenvalue ratio
##  - Rescale variables?

OBJECTIVE FACE MEASURES

eye size

a. cuedFaceTrusted ~ phrasing condition + (1 | pt) + ( eye_size | face) | = singular

eyesize.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (Eye_Size || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(eyesize.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (Eye_Size || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.5  13147.3  -6550.7  13101.5     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6530 -0.9891  0.6654  0.9419  1.5202 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. 
##  pt     (Intercept) 1.729e-01 4.158e-01
##  face   (Intercept) 1.351e-02 1.162e-01
##  face.1 Eye_Size    4.823e-10 2.196e-05
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06373    0.03649   1.747   0.0807 .
## trust_.5     0.12890    0.07006   1.840   0.0658 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

eye shape

a. cuedFaceTrusted ~ phrasing condition + (1 | pt) + ( eye shape | face) |& || = singular

eyeshape.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
              (1 | pt) + 
              (Eye_Shape || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(eyeshape.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (Eye_Shape || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.5  13147.3  -6550.7  13101.5     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6530 -0.9891  0.6654  0.9419  1.5202 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. 
##  pt     (Intercept) 1.729e-01 4.158e-01
##  face   (Intercept) 1.351e-02 1.162e-01
##  face.1 Eye_Shape   3.702e-10 1.924e-05
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06373    0.03649   1.747   0.0807 .
## trust_.5     0.12889    0.07006   1.840   0.0658 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

face roundedness

a. cuedFaceTrusted ~ phrasing condition + (1 | pt) + ( round | face) | & || = singular

round.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
           (1 | pt) +
           (Face_Roundedness || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(round.m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (Face_Roundedness ||  
##     face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.2  13147.1  -6550.6  13101.2     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6673 -0.9890  0.6670  0.9425  1.5286 
## 
## Random effects:
##  Groups Name             Variance Std.Dev.
##  pt     (Intercept)      0.17294  0.4159  
##  face   (Intercept)      0.00000  0.0000  
##  face.1 Face_Roundedness 0.03747  0.1936  
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06394    0.03654   1.750   0.0801 .
## trust_.5     0.12882    0.07007   1.839   0.0660 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

face width to height ratio

a. cuedFaceTrusted ~ phrasing condition + (1 | pt) + ( wth_ratio | face) |& || = singular

fWtH_Ratio.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
           (1 | pt) +
           (fWtH_Ratio || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(fWtH_Ratio.m )
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (fWtH_Ratio || face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.3  13147.1  -6550.6  13101.3     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6595 -0.9892  0.6656  0.9424  1.5180 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. 
##  pt     (Intercept) 1.729e-01 4.159e-01
##  face   (Intercept) 1.793e-09 4.235e-05
##  face.1 fWtH_Ratio  2.880e-02 1.697e-01
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06399    0.03653   1.752   0.0798 .
## trust_.5     0.12886    0.07007   1.839   0.0659 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular

pupil distance

a. cuedFaceTrusted ~ phrasing condition + (1 | pt) + ( pupil | face) |& || = singular

pupil.m <- glmer(cuedFaceTrusted_1 ~ trust_.5 + 
           (1 | pt) +
           (Pupil_Distance || face), family = binomial("logit"), data = d2)
## boundary (singular) fit: see ?isSingular
summary(pupil.m )
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: cuedFaceTrusted_1 ~ trust_.5 + (1 | pt) + (Pupil_Distance ||  
##     face)
##    Data: d2
## 
##      AIC      BIC   logLik deviance df.resid 
##  13111.5  13147.3  -6550.7  13101.5     9573 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.6530 -0.9891  0.6654  0.9419  1.5202 
## 
## Random effects:
##  Groups Name           Variance Std.Dev.
##  pt     (Intercept)    0.17289  0.4158  
##  face   (Intercept)    0.01351  0.1162  
##  face.1 Pupil_Distance 0.00000  0.0000  
## Number of obs: 9578, groups:  pt, 221; face, 132
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.06373    0.03649   1.747   0.0807 .
## trust_.5     0.12890    0.07006   1.840   0.0658 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##          (Intr)
## trust_.5 -0.063
## convergence code: 0
## boundary (singular) fit: see ?isSingular