WPT.Analyses.08.06.2020
Load packages
Load Data
setwd("~/Desktop/R Projects/WPT")
WPT1 <- read.csv("wpt_study1_6.19.2020.csv", header=T, stringsAsFactors = FALSE, na.strings=c("","NA"))
WPT2 <- read.csv("Pred2Full.08.06.2020.csv", header=T, stringsAsFactors = FALSE, na.strings=c("","NA"))
WPT3 <- read.csv("Pred3Full.08.06.2020.csv", header=T, stringsAsFactors = FALSE, na.strings=c("","NA"))Show # of participants removed due to low accuracy for all studies
length(count_NAStudy1)## 20 in study 2## [1] 9length(count_NAStudy3)## 15 in study 3## [1] 10plot average accuracies and raw learning rate over time for each study
#Study 1
ggplot(WPT1Summary, aes(x=as.factor(Condition), y=mean_acc)) +
geom_bar(stat = "identity", alpha=0.5) +
geom_errorbar(aes(x=as.factor(Condition), ymin=mean_acc-SE_PL, ymax=mean_acc+SE_PL))+
coord_cartesian(ylim = c(0, 1))
#Study 1
ggplot(WPT1, aes(Trial, acc, color = Condition)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'
#Study 2
ggplot(WPT2Summary, aes(x=as.factor(Condition), y=mean_acc)) +
geom_bar(stat = "identity", alpha=0.5) +
geom_errorbar(aes(x=as.factor(Condition), ymin=mean_acc-SE_PL, ymax=mean_acc+SE_PL))+
coord_cartesian(ylim = c(0, 1))
#Study2
ggplot(WPT2, aes(Trial, acc, color = Condition)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'
#Study 3
ggplot(WPT3Summary, aes(x=as.factor(Condition), y=meanAcc)) +
geom_bar(stat = "identity", alpha=0.5) +
geom_errorbar(aes(x=as.factor(Condition), ymin=meanAcc-SE_PL, ymax=meanAcc+SE_PL))+
coord_cartesian(ylim = c(0, 1))
#Study3
ggplot(WPT3, aes(Trial, acc, color = Condition)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 798 rows containing non-finite values (stat_smooth).
########RL Results#######
Plot BIC Summed Difference From Baseline Model (i.e. model that does not take into account experimental design)
#Maximum Likelihood Study 2
ggplot(RL.ML.2, aes(x = Cond, y = SumBIC))+ facet_grid(~model) +geom_col(aes(fill = Cond), position = position_stack(reverse = TRUE))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
#MAP Likelihood Study 2
ggplot(RL.Map.2, aes(x = Cond, y = SumBIC))+ facet_grid(~model) +geom_col(aes(fill = Cond), position = position_stack(reverse = TRUE))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
#Maximum Likelihood Study 2
ggplot(RL.ML.3, aes(x = Cond, y = SumBIC))+ facet_grid(~model) +geom_col(aes(fill = Cond), position = position_stack(reverse = TRUE))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
#MAP Likelihood Study 3
ggplot(RL.Map.3, aes(x = Cond, y = SumBIC))+ facet_grid(~model) +geom_col(aes(fill = Cond), position = position_stack(reverse = TRUE))+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
Plotting and Testing for ET Decay BIC differences
#summarize study 2 BIC
BIC.Sum.2.ETDecay <- WPT2FullRL %>% # the names of the new data frame and the data frame to be summarised
group_by(Cond) %>% # the grouping variable
dplyr::summarise(SumBIC = sum(BIC, na.rm = T), # calculates the mean of each group
sd_PL = sd(BIC),
n_PL = n(), # calculates the sample size per group
SE_PL = sd(BIC)/sqrt(n())) # calculates the standard error of each group## `summarise()` ungrouping output (override with `.groups` argument)#summarize study 3 BIC
BIC.Sum.3.ETDecay <- WPT3FullRL %>% # the names of the new data frame and the data frame to be summarised
group_by(Cond) %>% # the grouping variable
dplyr::summarise(SumBIC = sum(BIC, na.rm = T), # calculates the mean of each group
sd_PL = sd(BIC),
n_PL = n(), # calculates the sample size per group
SE_PL = sd(BIC)/sqrt(n())) # calculates the standard error of each group## `summarise()` ungrouping output (override with `.groups` argument)#Run ANOVA, post hoc tests and for study 2 BIC
WPT2FullRL.AOV <- aov(WPT2FullRL$BIC~WPT2FullRL$Cond)
summary(WPT2FullRL.AOV)## Df Sum Sq Mean Sq F value Pr(>F)
## WPT2FullRL$Cond 3 27858 9286 2.867 0.0365 *
## Residuals 369 1195136 3239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(WPT2FullRL.AOV)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = WPT2FullRL$BIC ~ WPT2FullRL$Cond)
##
## $`WPT2FullRL$Cond`
## diff lwr upr p adj
## StealCl-steal -16.166597 -37.65531 5.322113 0.2125431
## Weather-steal -22.874670 -44.41835 -1.330994 0.0325028
## WeatherFa-steal -18.948126 -40.72222 2.825971 0.1130017
## Weather-StealCl -6.708074 -27.96327 14.547122 0.8476182
## WeatherFa-StealCl -2.781529 -24.27024 18.707180 0.9871347
## WeatherFa-Weather 3.926544 -17.61713 25.470221 0.9655238ggplot(BIC.Sum.2.ETDecay, aes(x=as.factor(Cond), y=SumBIC)) +
geom_bar(stat = "identity", alpha=0.5) +
geom_errorbar(aes(x=as.factor(Cond), ymin=SumBIC-SE_PL, ymax=SumBIC+SE_PL))+
coord_cartesian(ylim = c(100, 25000))
#Run T.Test, post hoc tests and for study 3 BIC
t.test(WPT3FullRL$BIC~WPT3FullRL$Cond)##
## Welch Two Sample t-test
##
## data: WPT3FullRL$BIC by WPT3FullRL$Cond
## t = 0.40201, df = 203.12, p-value = 0.6881
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.91607 19.53181
## sample estimates:
## mean in group Negative mean in group Positive
## 191.3123 188.0044ggplot(BIC.Sum.3.ETDecay, aes(x=as.factor(Cond), y=SumBIC)) +
geom_bar(stat = "identity", alpha=0.5) +
geom_errorbar(aes(x=as.factor(Cond), ymin=SumBIC-SE_PL, ymax=SumBIC+SE_PL))+
coord_cartesian(ylim = c(100, 25000))
#Test for Differences Between Studies 2 and 3 steal conditions
Steal3 <- WPT3FullRL[which(WPT3FullRL$Cond == "Negative"),]
Steal2 <- WPT2FullRL[which(WPT2FullRL$Cond == "steal"),]
t.test(Steal3$BIC,Steal2$BIC, paired = F) # Not sig different##
## Welch Two Sample t-test
##
## data: Steal3$BIC and Steal2$BIC
## t = -0.81451, df = 192.97, p-value = 0.4164
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -23.925483 9.940126
## sample estimates:
## mean of x mean of y
## 191.3123 198.3050Plot histograms of individual differences
hist(stealStudy2$SDO)
hist(stealStudy3$intergroup_anx)
hist(stealStudy2$IMS)
hist(stealStudy3$EMS)
hist(stealStudy2$Anneal)
hist(stealStudy3$EX)
hist(stealStudy2$HH)
hist(stealStudy3$blk_contact)
hist(stealStudy3$blk_exp)
hist(stealStudy3$avg_acc)
Plot All Correlations in stereotype congruency conditions
#Plot steal 2 correlations
S2.corr.Acc %>%
mutate(rowname = factor(rowname, levels = rowname[order(avg_acc)])) %>% # Order by correlation strength
ggplot(aes(x = rowname, y = avg_acc)) +
geom_bar(stat = "identity") +
ylab("Correlation with Alpha") +
xlab("Variable") + theme_grey(base_size = 8)
#Plot steal 3 correlations
S.corr.Acc %>%
mutate(rowname = factor(rowname, levels = rowname[order(avg_acc)])) %>% # Order by correlation strength
ggplot(aes(x = rowname, y = avg_acc)) +
geom_bar(stat = "identity") +
ylab("Correlation with Alpha") +
xlab("Variable") + theme_grey(base_size = 8)
#Plot Pos 3 correlations
T.Corr.Acc %>%
mutate(rowname = factor(rowname, levels = rowname[order(avg_acc)])) %>% # Order by correlation strength
ggplot(aes(x = rowname, y = avg_acc)) +
geom_bar(stat = "identity") +
ylab("Correlation with Alpha") +
xlab("Variable") + theme_grey(base_size = 8)
Run logistic mixed models for all three studies
#Study1 logistic
Study1Logistic<- glmer(acc~scale(Trial)*Condition_eff+ (1|Participant), data = WPT1, family = "binomial")
summary(Study1Logistic)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * Condition_eff + (1 | Participant)
## Data: WPT1
##
## AIC BIC logLik deviance df.resid
## 15714.0 15752.2 -7852.0 15704.0 15364
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2249 0.1654 0.4100 0.5871 1.2260
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5528 0.7435
## Number of obs: 15369, groups: Participant, 52
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.32524 0.10961 12.090 <2e-16 ***
## scale(Trial) 0.31301 0.02134 14.669 <2e-16 ***
## Condition_effWeather -0.16532 0.10957 -1.509 0.1314
## scale(Trial):Condition_effWeather 0.04870 0.02134 2.282 0.0225 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) Cndt_W
## scale(Tril) 0.030
## Cndtn_ffWth -0.275 -0.008
## scl(Tr):C_W -0.008 -0.329 0.030#Study2 logistic
Study2Logistic<- glmer(acc~scale(Trial)*Condition_eff+ (1|Participant), data = WPT2, family = "binomial")
summary(Study2Logistic)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * Condition_eff + (1 | Participant)
## Data: WPT2
##
## AIC BIC logLik deviance df.resid
## 67997.7 68080.6 -33989.9 67979.7 73473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7111 0.2478 0.3796 0.5163 1.3856
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3989 0.6316
## Number of obs: 73482, groups: Participant, 373
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.551765 0.034402 45.106 < 2e-16
## scale(Trial) 0.476979 0.010061 47.408 < 2e-16
## Condition_effsteal -0.199452 0.059786 -3.336 0.00085
## Condition_effsteal_clouds 0.071534 0.060041 1.191 0.23349
## Condition_effweather_faces -0.022965 0.058906 -0.390 0.69664
## scale(Trial):Condition_effsteal -0.034462 0.016932 -2.035 0.04182
## scale(Trial):Condition_effsteal_clouds -0.005911 0.017734 -0.333 0.73889
## scale(Trial):Condition_effweather_faces -0.000869 0.017146 -0.051 0.95958
##
## (Intercept) ***
## scale(Trial) ***
## Condition_effsteal ***
## Condition_effsteal_clouds
## Condition_effweather_faces
## scale(Trial):Condition_effsteal *
## scale(Trial):Condition_effsteal_clouds
## scale(Trial):Condition_effweather_faces
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) Cndtn_ Cndtn_ffs_ Cndtn_ffw_ sc(T):C_
## scale(Tril) 0.082
## Cndtn_ffstl 0.008 -0.011
## Cndtn_ffst_ 0.015 0.003 -0.342
## Cndtn_ffwt_ -0.018 -0.003 -0.329 -0.332
## scl(Trl):C_ -0.011 -0.049 0.069 -0.023 -0.019
## scl(Trl):Cndtn_ffs_ 0.003 0.031 -0.022 0.082 -0.027 -0.328
## scl(Trl):Cndtn_ffw_ -0.003 -0.027 -0.019 -0.027 0.079 -0.303
## scl(Trl):Cndtn_ffs_
## scale(Tril)
## Cndtn_ffstl
## Cndtn_ffst_
## Cndtn_ffwt_
## scl(Trl):C_
## scl(Trl):Cndtn_ffs_
## scl(Trl):Cndtn_ffw_ -0.335#Study3 logistic
WPT3 <- WPT3[!is.na(WPT3$Pattern),]
Study3Logistic<- glmer(acc~scale(Trial)*Condition_eff+ (1|Participant), data = WPT3, family = "binomial")
summary(Study3Logistic)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * Condition_eff + (1 | Participant)
## Data: WPT3
##
## AIC BIC logLik deviance df.resid
## 34331.8 34374.4 -17160.9 34321.8 36977
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1169 0.2258 0.3701 0.5238 1.4513
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4955 0.7039
## Number of obs: 36982, groups: Participant, 178
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.502380 0.075624 19.867 <2e-16 ***
## scale(Trial) 0.542954 0.019219 28.250 <2e-16 ***
## Condition_effpos -0.024386 0.110008 -0.222 0.825
## scale(Trial):Condition_effpos -0.009686 0.028502 -0.340 0.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) Cndtn_
## scale(Tril) 0.076
## Condtn_ffps -0.687 -0.051
## scl(Trl):C_ -0.050 -0.674 0.075#individual differences mixed models
#individual differences for Study 2 steal
Steal2EMS <- glmer(acc~scale(Trial)*EMS_sca + (1|Participant), data = stealStudy2, family = "binomial")
summary(Steal2EMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * EMS_sca + (1 | Participant)
## Data: stealStudy2
##
## AIC BIC logLik deviance df.resid
## 16268.4 16306.9 -8129.2 16258.4 16336
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0829 0.2136 0.4028 0.5588 1.4717
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4664 0.683
## Number of obs: 16341, groups: Participant, 83
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.35662 0.07796 17.401 < 2e-16 ***
## scale(Trial) 0.46569 0.02028 22.958 < 2e-16 ***
## EMS_sca -0.12928 0.07754 -1.667 0.0955 .
## scale(Trial):EMS_sca -0.08406 0.02016 -4.170 3.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) EMS_sc
## scale(Tril) 0.064
## EMS_sca -0.010 -0.018
## scl(T):EMS_ -0.018 -0.078 0.065plot_model(Steal2EMS, type = "pred", terms = c("Trial","EMS_sca"))## Data were 'prettified'. Consider using `terms="Trial [all]"` to get smooth plots.
Steal2IMS <- glmer(acc~scale(Trial)*IMS_sca + (1|Participant), data = stealStudy2, family = "binomial")
summary(Steal2IMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * IMS_sca + (1 | Participant)
## Data: stealStudy2
##
## AIC BIC logLik deviance df.resid
## 16788.6 16827.2 -8389.3 16778.6 16733
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6188 0.2225 0.4044 0.5663 1.3487
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4387 0.6623
## Number of obs: 16738, groups: Participant, 85
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.337195 0.074836 17.868 <2e-16 ***
## scale(Trial) 0.450382 0.019905 22.627 <2e-16 ***
## IMS_sca -0.183959 0.074691 -2.463 0.0138 *
## scale(Trial):IMS_sca -0.007651 0.020300 -0.377 0.7062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) IMS_sc
## scale(Tril) 0.062
## IMS_sca -0.012 -0.012
## scl(T):IMS_ -0.012 -0.087 0.066Steal2IntAnx <- glmer(acc~scale(Trial)*intergroup_anx_sca + (1|Participant), data = stealStudy2, family = "binomial")
summary(Steal2IntAnx)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * intergroup_anx_sca + (1 | Participant)
## Data: stealStudy2
##
## AIC BIC logLik deviance df.resid
## 16903.5 16942.2 -8446.8 16893.5 16933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8243 0.2246 0.4019 0.5635 1.4151
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4739 0.6884
## Number of obs: 16938, groups: Participant, 86
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.35043 0.07713 17.508 <2e-16 ***
## scale(Trial) 0.45356 0.01981 22.893 <2e-16 ***
## intergroup_anx_sca -0.08519 0.07683 -1.109 0.268
## scale(Trial):intergroup_anx_sca -0.02814 0.01964 -1.433 0.152
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) intr__
## scale(Tril) 0.061
## intrgrp_nx_ -0.003 -0.009
## scl(Trl):__ -0.009 -0.053 0.059Steal2blkCon <- glmer(acc~scale(Trial)*blk_contact_sca + (1|Participant), data = stealStudy2, family = "binomial")
summary(Steal2blkCon)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_contact_sca + (1 | Participant)
## Data: stealStudy2
##
## AIC BIC logLik deviance df.resid
## 16905.4 16944.1 -8447.7 16895.4 16933
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6892 0.2266 0.4010 0.5633 1.3675
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4768 0.6905
## Number of obs: 16938, groups: Participant, 86
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.34992 0.07734 17.454 <2e-16 ***
## scale(Trial) 0.45175 0.01979 22.832 <2e-16 ***
## blk_contact_sca 0.05113 0.07766 0.658 0.510
## scale(Trial):blk_contact_sca -0.01698 0.02024 -0.839 0.401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_c_
## scale(Tril) 0.060
## blk_cntct_s 0.007 -0.001
## scl(Trl):__ -0.001 0.023 0.060Steal2blkExt <- glmer(acc~scale(Trial)*blk_exp_sca + (1|Participant), data = stealStudy2, family = "binomial")
summary(Steal2blkExt)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_exp_sca + (1 | Participant)
## Data: stealStudy2
##
## AIC BIC logLik deviance df.resid
## 15388.2 15426.5 -7689.1 15378.2 15568
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6752 0.2331 0.3976 0.5562 1.3524
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4694 0.6851
## Number of obs: 15573, groups: Participant, 79
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.37729 0.08015 17.183 <2e-16 ***
## scale(Trial) 0.45918 0.02077 22.105 <2e-16 ***
## blk_exp_sca -0.02403 0.08003 -0.300 0.764
## scale(Trial):blk_exp_sca -0.01728 0.02061 -0.839 0.402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_x_
## scale(Tril) 0.062
## blk_exp_sca 0.000 -0.004
## scl(Trl):__ -0.004 -0.015 0.060#individual differences for Study 3 steal
Steal3EMS <- glmer(acc~scale(Trial)*EMS_sca + (1|Participant), data = stealStudy3, family = "binomial")
summary(Steal3EMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * EMS_sca + (1 | Participant)
## Data: stealStudy3
##
## AIC BIC logLik deviance df.resid
## 18363.6 18403.0 -9176.8 18353.6 19783
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8585 0.2278 0.3686 0.5249 1.3614
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5153 0.7178
## Number of obs: 19788, groups: Participant, 93
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.475573 0.077392 19.066 <2e-16 ***
## scale(Trial) 0.531402 0.019562 27.166 <2e-16 ***
## EMS_sca 0.006076 0.054428 0.112 0.911
## scale(Trial):EMS_sca -0.024489 0.019651 -1.246 0.213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) EMS_sc
## scale(Tril) 0.068
## EMS_sca 0.020 -0.001
## scl(T):EMS_ 0.000 0.036 0.107Steal3IMS <- glmer(acc~scale(Trial)*IMS_sca + (1|Participant), data = stealStudy3, family = "binomial")
summary(Steal3IMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * IMS_sca + (1 | Participant)
## Data: stealStudy3
##
## AIC BIC logLik deviance df.resid
## 17789.0 17828.3 -8889.5 17779.0 19395
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8726 0.2157 0.3610 0.5288 1.5375
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5356 0.7318
## Number of obs: 19400, groups: Participant, 91
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.50793 0.08021 18.799 < 2e-16 ***
## scale(Trial) 0.55499 0.02013 27.564 < 2e-16 ***
## IMS_sca -0.06188 0.07473 -0.828 0.408
## scale(Trial):IMS_sca -0.14934 0.01987 -7.517 5.6e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) IMS_sc
## scale(Tril) 0.073
## IMS_sca -0.112 -0.030
## scl(T):IMS_ -0.026 -0.124 0.087Steal3IntAnx <- glmer(acc~scale(Trial)*intergroup_anx_sca + (1|Participant), data = stealStudy3, family = "binomial")
summary(Steal3IntAnx)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * intergroup_anx_sca + (1 | Participant)
## Data: stealStudy3
##
## AIC BIC logLik deviance df.resid
## 17526.9 17566.2 -8758.4 17516.9 19222
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0687 0.2260 0.3639 0.5144 1.3872
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5239 0.7238
## Number of obs: 19227, groups: Participant, 90
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.526226 0.079541 19.188 <2e-16 ***
## scale(Trial) 0.534083 0.020101 26.569 <2e-16 ***
## intergroup_anx_sca -0.025612 0.050557 -0.507 0.612
## scale(Trial):intergroup_anx_sca -0.006246 0.020761 -0.301 0.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) intr__
## scale(Tril) 0.071
## intrgrp_nx_ -0.066 -0.009
## scl(Trl):__ -0.008 -0.058 0.130Steal3blkCon <- glmer(acc~scale(Trial)*blk_contact_sca + (1|Participant), data = stealStudy3, family = "binomial")
summary(Steal3blkCon)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_contact_sca + (1 | Participant)
## Data: stealStudy3
##
## AIC BIC logLik deviance df.resid
## 18294.2 18333.7 -9142.1 18284.2 19781
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2679 0.2209 0.3680 0.5236 1.4229
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5314 0.729
## Number of obs: 19786, groups: Participant, 93
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48855 0.07856 18.948 <2e-16 ***
## scale(Trial) 0.53036 0.01963 27.019 <2e-16 ***
## blk_contact_sca -0.07254 0.07416 -0.978 0.3280
## scale(Trial):blk_contact_sca -0.03260 0.01858 -1.755 0.0793 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_c_
## scale(Tril) 0.068
## blk_cntct_s -0.027 -0.009
## scl(Trl):__ -0.009 -0.068 0.059Steal3blkExt <- glmer(acc~scale(Trial)*blk_exp_sca + (1|Participant), data = stealStudy3, family = "binomial")
summary(Steal3blkExt)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_exp_sca + (1 | Participant)
## Data: stealStudy3
##
## AIC BIC logLik deviance df.resid
## 17348.8 17388.0 -8669.4 17338.8 18631
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1521 0.2170 0.3722 0.5267 1.4946
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5085 0.7131
## Number of obs: 18636, groups: Participant, 87
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.46243 0.07951 18.394 < 2e-16 ***
## scale(Trial) 0.54010 0.02019 26.748 < 2e-16 ***
## blk_exp_sca -0.10206 0.08016 -1.273 0.20294
## scale(Trial):blk_exp_sca -0.06303 0.01996 -3.158 0.00159 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_x_
## scale(Tril) 0.070
## blk_exp_sca -0.016 -0.015
## scl(Trl):__ -0.015 -0.083 0.067#individual differences for Study 3 Touchdown
Steal3EMS <- glmer(acc~scale(Trial)*EMS_sca + (1|Participant), data = posStudy3, family = "binomial")
summary(Steal3EMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * EMS_sca + (1 | Participant)
## Data: posStudy3
##
## AIC BIC logLik deviance df.resid
## 15180.1 15218.5 -7585.0 15170.1 16011
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1131 0.2326 0.3806 0.5343 1.4956
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.445 0.6671
## Number of obs: 16016, groups: Participant, 83
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48000 0.07659 19.325 < 2e-16 ***
## scale(Trial) 0.53390 0.02142 24.931 < 2e-16 ***
## EMS_sca 0.08321 0.07632 1.090 0.27559
## scale(Trial):EMS_sca 0.05584 0.02142 2.607 0.00914 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) EMS_sc
## scale(Tril) 0.078
## EMS_sca 0.005 0.016
## scl(T):EMS_ 0.016 0.058 0.079Steal3IMS <- glmer(acc~scale(Trial)*IMS_sca + (1|Participant), data = posStudy3, family = "binomial")
summary(Steal3IMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * IMS_sca + (1 | Participant)
## Data: posStudy3
##
## AIC BIC logLik deviance df.resid
## 14799.0 14837.3 -7394.5 14789.0 15635
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0777 0.2326 0.3793 0.5318 1.4461
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4585 0.6772
## Number of obs: 15640, groups: Participant, 81
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.4824451 0.0785987 18.861 <2e-16 ***
## scale(Trial) 0.5342336 0.0216454 24.681 <2e-16 ***
## IMS_sca 0.0377341 0.0789442 0.478 0.633
## scale(Trial):IMS_sca -0.0005933 0.0227181 -0.026 0.979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) IMS_sc
## scale(Tril) 0.076
## IMS_sca 0.001 0.004
## scl(T):IMS_ 0.003 0.022 0.085Steal3IntAnx <- glmer(acc~scale(Trial)*intergroup_anx_sca + (1|Participant), data = posStudy3, family = "binomial")
summary(Steal3IntAnx)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * intergroup_anx_sca + (1 | Participant)
## Data: posStudy3
##
## AIC BIC logLik deviance df.resid
## 15002.5 15040.8 -7496.2 14992.5 15824
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0182 0.2305 0.3799 0.5331 1.4931
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4556 0.675
## Number of obs: 15829, groups: Participant, 82
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.478017 0.077881 18.978 <2e-16 ***
## scale(Trial) 0.535403 0.021511 24.889 <2e-16 ***
## intergroup_anx_sca -0.008934 0.077624 -0.115 0.9084
## scale(Trial):intergroup_anx_sca -0.044878 0.020894 -2.148 0.0317 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) intr__
## scale(Tril) 0.076
## intrgrp_nx_ 0.000 -0.008
## scl(Trl):__ -0.008 -0.025 0.070Steal3blkCon <- glmer(acc~scale(Trial)*blk_contact_sca + (1|Participant), data = posStudy3, family = "binomial")
summary(Steal3blkCon)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_contact_sca + (1 | Participant)
## Data: posStudy3
##
## AIC BIC logLik deviance df.resid
## 15172.9 15211.3 -7581.4 15162.9 16011
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1930 0.2284 0.3832 0.5350 1.5449
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4381 0.6619
## Number of obs: 16016, groups: Participant, 83
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.48202 0.07606 19.486 < 2e-16 ***
## scale(Trial) 0.53738 0.02149 25.009 < 2e-16 ***
## blk_contact_sca -0.13546 0.07538 -1.797 0.072321 .
## scale(Trial):blk_contact_sca -0.07157 0.02030 -3.526 0.000422 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_c_
## scale(Tril) 0.080
## blk_cntct_s -0.010 -0.020
## scl(Trl):__ -0.021 -0.095 0.062Steal3blkExt <- glmer(acc~scale(Trial)*blk_exp_sca + (1|Participant), data = posStudy3, family = "binomial")
summary(Steal3blkExt)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_exp_sca + (1 | Participant)
## Data: posStudy3
##
## AIC BIC logLik deviance df.resid
## 14987.2 15025.5 -7488.6 14977.2 15834
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0455 0.2342 0.3803 0.5317 1.4574
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4533 0.6733
## Number of obs: 15839, groups: Participant, 82
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.483065 0.077704 19.086 <2e-16 ***
## scale(Trial) 0.530639 0.021493 24.689 <2e-16 ***
## blk_exp_sca 0.009793 0.077166 0.127 0.899
## scale(Trial):blk_exp_sca -0.011141 0.020472 -0.544 0.586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_x_
## scale(Tril) 0.076
## blk_exp_sca 0.001 -0.002
## scl(Trl):__ -0.002 -0.007 0.064#steal full
StealFullEMS <- glmer(acc~scale(Trial)*EMS_sca + (1|Participant), data = stealFull, family = "binomial")
summary(StealFullEMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * EMS_sca + (1 | Participant)
## Data: stealFull
##
## AIC BIC logLik deviance df.resid
## 34632.7 34675.2 -17311.4 34622.7 36124
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4553 0.2289 0.3819 0.5418 1.4511
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4972 0.7052
## Number of obs: 36129, groups: Participant, 176
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.41799 0.05524 25.670 < 2e-16 ***
## scale(Trial) 0.49893 0.01406 35.493 < 2e-16 ***
## EMS_sca -0.04466 0.04448 -1.004 0.315
## scale(Trial):EMS_sca -0.05841 0.01398 -4.177 2.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) EMS_sc
## scale(Tril) 0.065
## EMS_sca 0.003 -0.013
## scl(T):EMS_ -0.010 -0.030 0.088StealFullIMS <- glmer(acc~scale(Trial)*IMS_sca + (1|Participant), data = stealFull, family = "binomial")
summary(StealFullIMS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * IMS_sca + (1 | Participant)
## Data: stealFull
##
## AIC BIC logLik deviance df.resid
## 34609.2 34651.6 -17299.6 34599.2 36133
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.7310 0.2217 0.3808 0.5451 1.5848
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.497 0.705
## Number of obs: 36138, groups: Participant, 176
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.42903 0.05536 25.815 < 2e-16 ***
## scale(Trial) 0.50349 0.01414 35.608 < 2e-16 ***
## IMS_sca -0.12961 0.05374 -2.412 0.0159 *
## scale(Trial):IMS_sca -0.07731 0.01396 -5.536 3.09e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) IMS_sc
## scale(Tril) 0.068
## IMS_sca -0.064 -0.020
## scl(T):IMS_ -0.020 -0.109 0.073StealFullIntAnx <- glmer(acc~scale(Trial)*intergroup_anx_sca + (1|Participant), data = stealFull, family = "binomial")
summary(StealFullIntAnx)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * intergroup_anx_sca + (1 | Participant)
## Data: stealFull
##
## AIC BIC logLik deviance df.resid
## 34430.9 34473.4 -17210.5 34420.9 36160
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8278 0.2275 0.3806 0.5380 1.4414
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5043 0.7102
## Number of obs: 36165, groups: Participant, 176
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.44091 0.05569 25.873 <2e-16 ***
## scale(Trial) 0.49411 0.01412 35.003 <2e-16 ***
## intergroup_anx_sca -0.04617 0.04137 -1.116 0.264
## scale(Trial):intergroup_anx_sca -0.02212 0.01444 -1.532 0.126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) intr__
## scale(Tril) 0.066
## intrgrp_nx_ -0.048 -0.012
## scl(Trl):__ -0.010 -0.067 0.102StealFullblkCon <- glmer(acc~scale(Trial)*blk_contact_sca + (1|Participant), data = stealFull, family = "binomial")
summary(StealFullblkCon)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_contact_sca + (1 | Participant)
## Data: stealFull
##
## AIC BIC logLik deviance df.resid
## 35199.5 35242.0 -17594.7 35189.5 36719
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9400 0.2260 0.3822 0.5416 1.4230
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.5105 0.7145
## Number of obs: 36724, groups: Participant, 179
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.42042 0.05546 25.612 <2e-16 ***
## scale(Trial) 0.49162 0.01392 35.319 <2e-16 ***
## blk_contact_sca -0.01818 0.05399 -0.337 0.7363
## scale(Trial):blk_contact_sca -0.02712 0.01372 -1.976 0.0481 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_c_
## scale(Tril) 0.063
## blk_cntct_s -0.015 -0.005
## scl(Trl):__ -0.006 -0.030 0.059StealFullblkExt <- glmer(acc~scale(Trial)*blk_exp_sca + (1|Participant), data = stealFull, family = "binomial")
summary(StealFullblkExt)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scale(Trial) * blk_exp_sca + (1 | Participant)
## Data: stealFull
##
## AIC BIC logLik deviance df.resid
## 32735.9 32778.1 -16363.0 32725.9 34204
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8929 0.2254 0.3828 0.5400 1.4153
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4908 0.7006
## Number of obs: 34209, groups: Participant, 166
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.42158 0.05654 25.145 < 2e-16 ***
## scale(Trial) 0.50105 0.01447 34.625 < 2e-16 ***
## blk_exp_sca -0.06776 0.05674 -1.194 0.23238
## scale(Trial):blk_exp_sca -0.04433 0.01432 -3.096 0.00196 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scl(T) blk_x_
## scale(Tril) 0.066
## blk_exp_sca -0.015 -0.010
## scl(Trl):__ -0.010 -0.056 0.062RL differences by study & condition
#Study 2 Anneal by condition
Study2RLLog <- glmer(acc~scDecay*Condition_eff + (1|Participant), data = WPT2, family = "binomial")
summary(Study2RLLog)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scDecay * Condition_eff + (1 | Participant)
## Data: WPT2
##
## AIC BIC logLik deviance df.resid
## 70297.8 70380.6 -35139.9 70279.8 73473
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5356 0.3072 0.4099 0.5191 0.9213
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3085 0.5554
## Number of obs: 73482, groups: Participant, 373
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.47492 0.03095 47.650 <2e-16 ***
## scDecay -0.26715 0.03106 -8.601 <2e-16 ***
## Condition_effsteal -0.13496 0.05391 -2.504 0.0123 *
## Condition_effsteal_clouds 0.01936 0.05406 0.358 0.7202
## Condition_effweather_faces -0.05997 0.05283 -1.135 0.2563
## scDecay:Condition_effsteal 0.04365 0.05261 0.830 0.4067
## scDecay:Condition_effsteal_clouds -0.07012 0.05685 -1.233 0.2174
## scDecay:Condition_effweather_faces -0.03666 0.05494 -0.667 0.5046
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scDecy Cndtn_ Cndtn_ffs_ Cndtn_ffw_ scD:C_
## scDecay 0.001
## Cndtn_ffstl 0.012 -0.109
## Cndtn_ffst_ 0.016 0.114 -0.344
## Cndtn_ffwt_ -0.023 0.088 -0.329 -0.331
## scDcy:Cndt_ -0.112 -0.038 -0.127 -0.003 0.013
## scDcy:Cndtn_ffs_ 0.108 0.097 -0.003 0.125 -0.112 -0.358
## scDcy:Cndtn_ffw_ 0.085 0.037 0.012 -0.113 0.101 -0.334
## scDcy:Cndtn_ffs_
## scDecay
## Cndtn_ffstl
## Cndtn_ffst_
## Cndtn_ffwt_
## scDcy:Cndt_
## scDcy:Cndtn_ffs_
## scDcy:Cndtn_ffw_ -0.383#Study 2 predicting RT by Anneal by condition
Study2RLRT <- lmer(RT~scDecay*Condition_eff +(1|Participant), data = WPT2)
summary(Study2RLRT)## Linear mixed model fit by REML ['lmerMod']
## Formula: RT ~ scDecay * Condition_eff + (1 | Participant)
## Data: WPT2
##
## REML criterion at convergence: 323716.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.380 -0.293 -0.120 0.151 78.768
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.1327 0.3643
## Residual 4.7471 2.1788
## Number of obs: 73482, groups: Participant, 373
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.93603 0.02085 92.849
## scDecay 0.02192 0.02102 1.043
## Condition_effsteal 0.04931 0.03642 1.354
## Condition_effsteal_clouds 0.07717 0.03651 2.113
## Condition_effweather_faces -0.06511 0.03571 -1.823
## scDecay:Condition_effsteal -0.06178 0.03565 -1.733
## scDecay:Condition_effsteal_clouds 0.03091 0.03851 0.803
## scDecay:Condition_effweather_faces -0.02623 0.03721 -0.705
##
## Correlation of Fixed Effects:
## (Intr) scDecy Cndtn_ Cndtn_ffs_ Cndtn_ffw_ scD:C_
## scDecay 0.017
## Cndtn_ffstl 0.015 -0.113
## Cndtn_ffst_ 0.019 0.118 -0.346
## Cndtn_ffwt_ -0.020 0.088 -0.332 -0.333
## scDcy:Cndt_ -0.116 -0.036 -0.115 -0.009 0.010
## scDcy:Cndtn_ffs_ 0.113 0.098 -0.008 0.145 -0.120 -0.359
## scDcy:Cndtn_ffw_ 0.085 0.038 0.009 -0.121 0.117 -0.335
## scDcy:Cndtn_ffs_
## scDecay
## Cndtn_ffstl
## Cndtn_ffst_
## Cndtn_ffwt_
## scDcy:Cndt_
## scDcy:Cndtn_ffs_
## scDcy:Cndtn_ffw_ -0.384#Study 3 Anneal by condition
Study3RLLog <- glmer(acc~scDecay*Condition_eff+ (1|Participant), data = WPT3, family = "binomial")
summary(Study3RLLog)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: acc ~ scDecay * Condition_eff + (1 | Participant)
## Data: WPT3
##
## AIC BIC logLik deviance df.resid
## 35844.9 35887.5 -17917.5 35834.9 36977
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8776 0.2954 0.4049 0.5409 0.9231
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.4017 0.6338
## Number of obs: 36982, groups: Participant, 178
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.421974 0.068203 20.849 < 2e-16 ***
## scDecay -0.230426 0.066718 -3.454 0.000553 ***
## Condition_effpos 0.001927 0.099686 0.019 0.984574
## scDecay:Condition_effpos 0.010864 0.098484 0.110 0.912160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) scDecy Cndtn_
## scDecay -0.028
## Condtn_ffps -0.683 0.019
## scDcy:Cndt_ 0.019 -0.678 -0.080Plot individual differences
#study 2 intergroup anxiety
stealStudy2 <- stealStudy2[!is.na(stealStudy2$IntAnxDich),]
ggplot(stealStudy2, aes(Trial, acc, color = IntAnxDich)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'
#study 3 intergroup anxiety
stealStudy3 <- stealStudy3[!is.na(stealStudy3$IntAnxDich),]
ggplot(stealStudy3, aes(Trial, acc, color = IntAnxDich)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 414 rows containing non-finite values (stat_smooth).
#study 2 EMS
stealStudy2plot <- stealStudy2[!is.na(stealStudy2$EMSDich),]
ggplot(stealStudy2plot, aes(Trial, acc, color = EMSDich)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'
#study 3 EMS
stealStudy3plot <- stealStudy3[!is.na(stealStudy3$EMSDich),]
ggplot(stealStudy3plot, aes(Trial, acc, color = EMSDich)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 380 rows containing non-finite values (stat_smooth).
#study 2 IMS
stealStudy2plot <- stealStudy2[!is.na(stealStudy2$IMSDich),]
ggplot(stealStudy2plot, aes(Trial, acc, color = IMSDich)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'
#study 3 IMS
stealStudy3plot <- stealStudy3[!is.na(stealStudy3$IMSDich),]
ggplot(stealStudy3plot, aes(Trial, acc, color = IMSDich)) +
geom_smooth(method = "loess")+
scale_y_continuous(name = "Accuracy") ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 336 rows containing non-finite values (stat_smooth).