# Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017).
#   Reward draws the eye, uncertainty holds the eye: Associative learning
#   modulates distracter interference in visual search.  Frontiers in
#   Behavioral Neuroscience. doi: 10.3389/fnbeh.2017.00128.


# Learning Task: Accuracy
load("LearnAccuracy.RData")
AOV[,Acc.t:=asin(sqrt(Acc))]
m <- aov_4(Acc.t ~ (CUE*B6|FILE), data=AOV)
anova(m)

M <- lsmeans(m,~CUE)
k <- list(qdr=scale(c(.5,-1,.5)))
K <- contrast(M, k)
summary(K)

M <- lsmeans(m,~CUE|B6)
lsmip(M, CUE~B6, type="response", aspect=1)

# Learning Task: Reward
m <- lmer(Exp.c~CUE+(1|FILE)+(0+CUE|FILE), data=AOV[B6>2])
M <- lsmeans(m,~CUE)
summary(contrast(M,"poly"))
lsmip(M, ~CUE, type="response", aspect=1.5)

# Learning Task: Uncertainty
m <- lmer(Unc.c~CUE+(1|FILE)+(0+CUE|FILE), data=AOV[B6>2])
M <- lsmeans(m,~CUE)
summary(contrast(M,"poly"))
lsmip(M, ~CUE, type="response", aspect=1.5)

# Learning Task: Latency
load("LearnLatency.RData")
AOV[,CUE:=ordered(CUE, levels=c("L","P","H"))]
AOV[,B6:=ordered(B6)]

m <- lmer(log(lat)~CUE+(CUE|FILE), data=AOV)
anova(m, ddf="K")

M <- lsmeans::lsmeans(m,~CUE)
lsmip(M, ~CUE, aspect=1.5, type="response")

summary(m, ddf="K")
M <- model.matrix(m)
m0 <- lmer(log(lat)~M[,-c(1,2)]+(CUE|FILE), data=AOV)
unclass(anova(m0, m))$AIC
m0 <- lmer(log(lat)~M[,-c(1,3)]+(CUE|FILE), data=AOV)
unclass(anova(m0, m))$AIC


# Learning Task: Total Dwell Time
load("LearnTotalDwellTime.RData")
AOV[,CUE:=ordered(CUE, levels=c("L","P","H"))]
AOV[,B6:=ordered(B6)]

m <- lmer(sqrt(dur)~CUE+(1|FILE)+(0+CUE|FILE), data=AOV)
anova(m, ddf="K")

M <- lsmeans::lsmeans(m,~CUE)
lsmip(M, ~CUE, aspect=1.5)

summary(m, ddf="K")
M <- model.matrix(m)
m0 <- lmer(sqrt(dur)~M[,-c(1,2)]+(1|FILE)+(0+CUE|FILE), data=AOV)
unclass(anova(m0,m))$AIC
m0 <- lmer(sqrt(dur)~M[,-c(1,3)]+(1|FILE)+(0+CUE|FILE), data=AOV)
unclass(anova(m0,m))$AIC