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manuscript_results_and_plots.Rmd
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---
title: "implicit_learning_analysis_adapt_delta = 0.9"
author: "Simon Boylan"
date: "2024-04-26"
output: word_document
---
```{r setup, include=FALSE}
.libPaths(c("/home/mococo/R",.libPaths()))
knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(fig.height=7, fig.width=7)
```
```{r libraries, echo = FALSE, results = 'hide',message = FALSE}
library(brms)
library(bayestestR)
library(patchwork)
library(ggplot2)
library(bayesplot)
# library(sjPlot)
library(report)
```
```{r data processing, echo = FALSE, results = 'hide',message = FALSE}
# loading data and dealing with it
home = getwd()
# loading data
dataOcculted <- read.csv("dataOcculted.csv")
data2 <- read.csv("data2.csv")
dataJ <- read.csv("dataJ.csv")
dataE <- read.csv("dataE.csv")
data2$segment <- as.factor(data2$segment)
data2$repeated <- as.factor(data2$repeated)
## Joystick data
dataJ$segment <- as.factor(dataJ$segment)
dataJ$day <- as.factor(dataJ$day)
dataJ$repeated <- as.factor(dataJ$repeated)
## Eyetracking data
dataE$segment <- as.factor(dataE$segment)
dataE$day <- as.factor(dataE$day)
dataE$repeated <- as.factor(dataE$repeated)
## Occulted data
dataOcculted$repeated <- as.factor(dataOcculted$repeated)
dataOcculted$occulted <- as.factor(dataOcculted$occulted)
dataOcculted$subjects <- as.factor(dataOcculted$subjects)
frameWidth = 0.22
frameHeight = frameWidth
BbottomLeft = 0.078
LbottomLeft = 0.064
BbottomRight = 0.06
LbottomRight = 0.78
BtopLeft = 0.99-frameHeight
LtopLeft = 0.08
```
# Root mean square error (RMSE)
Experiment 1: RMSE ~ segment\*day + segmentLength + (1 + segmentLength + segment\*day | subjects), Experiment 2: RMSE ~ segment + segmentLength + (1 + segmentLength + segment | subjects)). Priors over parameters were set as normal distributions (mean = 0, standard of deviation = 30).
## Joystick
```{r RMSE J summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mJRMSE_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In the Joystick condition of Experiment 1, the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),"). Convergence and stability of the model was good (Rhat < 1.01 and ESS > 1000, see table 3 for full results and diagnostics).")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher RMSE than segments 2 and 4
This is : (2\*segment3+2\*segment5+segment3:day2 + segment5:day2)/3 > (2\*segment2+2\*segment4+segment2:day2+segment4:day2)/2
```{r segment effect RMSE J, echo = FALSE}
conditional_effects(model1,effect="segment")
hypothesis(model1,'(2*segment3+2*segment5+segment3:day2 + segment5:day2)/3 > (2*segment2+2*segment4+segment2:day2+segment4:day2)/2')
```
### Day effect
Is RMSE on day 1 equal to RMSE on day 2
This is : day 2 < 0
```{r RMSE J day effect, echo = FALSE}
conditional_effects(model1,effect="day")
hypothesis(model1,'day2<0')
```
### Segment-day interaction effect
Is the difference between segments 2 + 4 and 3 + 5 significantly different than the difference of segments 2 + 4 and 3 + 5 on second day
( segment3:day2 + segment5:day2)/3>(segment2:day2+segment4:day2)/2
```{r RMSE J interaction effect, echo = FALSE}
hypothesis(model1,'( segment3:day2 + segment5:day2)/3>(segment2:day2+segment4:day2)/2')
model = model1
dataTest <- dataJ
dataTestAvg <- aggregate(RMSE ~ subjects + segment + day, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$RMSE),]
condEffDay2 <- conditional_effects(model,effect="segment:day")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
dataTestAvg$day <- as.factor(dataTestAvg$day)
theme_set(theme_classic())
p_check <- pp_check(model1,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.45,0.9),
legend.spacing.x = unit(8, 'cm'),
legend.direction = "horizontal",
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, RMSE, colour = day),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
geom_point(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
inset_element(p_check, left = LbottomLeft, bottom = BbottomLeft, right = BbottomLeft+frameWidth, top = BbottomLeft + frameHeight, align_to = 'plot')
# p_full <- p_CIposterior + p_interm + plot_layout(ncol = 1)
p_full <- p_interm
ggsave(filename="../publication/manuscript plots/JRMSE.png",plot=p_full,device="png",scale = 1)
p_full
```
## Eyetracking
RMSE Population-Level Effects and R2 of Better model:
```{r RMSE E summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mERMSE_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In the Eyetracking condition, divergent transitions in the model were corrected by increasing the adapt_delta parameter to 0.99. The model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher RMSE than segments 2 and 4
This is : (2\*segment3+2\*segment5+segment3:day2 + segment5:day2)/3 > (2\*segment2+2\*segment4+segment2:day2+segment4:day2)/2
```{r RMSE E segment effect, echo = FALSE}
conditional_effects(model1,effect="segment")
hypothesis(model1,'(2*segment3+2*segment5+segment3:day2 + segment5:day2)/3 > (2*segment2+2*segment4+segment2:day2+segment4:day2)/2')
```
### Day effect
Is RMSE on day 1 equal to RMSE on day 2
This is : day 2 < 0
```{r RMSE E day effect, echo = FALSE}
conditional_effects(model1,effect="day")
hypothesis(model1,'day2<0')
```
### Segment-day interaction effect
Is the difference between segments 2 + 4 and 3 + 5 significantly different than the difference of segments 2 + 4 and 3 + 5 on second day
( segment3:day2 + segment5:day2)/3>(segment2:day2+segment4:day2)/2
```{r RMSE E interaction effect, echo = FALSE}
hypothesis(model1,'( segment3:day2 + segment5:day2)/3>(segment2:day2+segment4:day2)/2')
model = model1
dataTest <- dataE
dataTestAvg <- aggregate(RMSE ~ subjects + segment + day, dataTest ,FUN = mean)
condEffDay2 <- conditional_effects(model,effect="segment:day")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
# dataTestAvg$subjects <- as.factor(dataTestAvg$subjects)
dataTestAvg$day <- as.factor(dataTestAvg$day)
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, RMSE, colour = day),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
geom_point(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
inset_element(p_check,left = LbottomRight, bottom = BbottomRight, right = LbottomRight+frameWidth, top = BbottomRight+frameHeight, align_to = 'plot')
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/ERMSE.png",plot=p_full,device="png",scale = 1)
p_full
```
## Second experiment
RMSE Population-Level Effects:
```{r RMSE 2 summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/m2RMSE_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In the second experiment, the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher RMSE than segments 2 and 4
This is : (segment3+segment5)/3 > (segment2+segment4)/2
```{r RMSE 2 segment effect, echo = FALSE}
hypothesis(model1,'(segment3+segment5)/3>(segment2+segment4)/2')
model = model1
dataTest <- data2
dataTestAvg <- aggregate(RMSE ~ repeated + subjects + segment, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$RMSE),]
condEffDay2 <- conditional_effects(model,effect="segment")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, RMSE, colour=repeated),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment"]],
aes(x=as.numeric(effect1__), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d")
) +
geom_point(
data=condEffDay2[["segment"]],
aes(x=as.numeric(effect1__), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d")
) +
inset_element(p_check,left = LbottomRight, bottom = BbottomRight, right = LbottomRight+frameWidth, top = BbottomRight+frameHeight, align_to = 'plot')
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/2RMSE.png",plot=p_full,device="png",scale = 1)
p_full
```
# Feed-forward component of tracking
The formulas used on the feed-forward variable were as follows: Experiment 1 : FF ~ segment\*day + segmentLength + (1 + segmentLength + segment\*day | subjects); Experiment 2 : FF ~ segment + segmentLength + (1 + segmentLength + segment | subjects)). Priors over parameters were set as normal distributions (mean = 0, standard of deviation = 30).
## Joystick
```{r FF J summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mJFF_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("In the Joystick condition of Experiment 1, the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher FF than segments 2 and 4
This is : (2\*segment3+2\*segment5+segment3:day2 + segment5:day2)/3 < (2\*segment2+2\*segment4+segment2:day2+segment4:day2)/2
```{r FF J segment effect, echo = FALSE}
conditional_effects(model1,effect="segment")
hypothesis(model1,'(2*segment3+2*segment5+segment3:day2 + segment5:day2)/3 < (2*segment2+2*segment4+segment2:day2+segment4:day2)/2')
```
### Day effect
Is FF on day 1 equal to FF on day 2
This is : day 2 > 0
```{r FF J day effect, echo = FALSE}
conditional_effects(model1,effect="day")
hypothesis(model1,'day2>0')
```
### Segment-day interaction effect
Is the difference between segments 2 + 4 and 3 + 5 significantly different than the difference of segments 2 + 4 and 3 + 5 on second day
( segment3:day2 + segment5:day2)/3<(segment2:day2+segment4:day2)/2
```{r FF J interaction effect, echo = FALSE}
hypothesis(model1,'( segment3:day2 + segment5:day2)/3 < (segment2:day2+segment4:day2)/2')
model = model1
dataTest <- dataJ
dataTestAvg <- aggregate(FF ~ subjects + segment + day, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$FF),]
condEffDay2 <- conditional_effects(model,effect="segment:day")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
dataTestAvg$day <- as.factor(dataTestAvg$day)
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, FF, colour = day),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
geom_point(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
inset_element(p_check, left = LtopLeft, bottom = BtopLeft, right = LtopLeft+frameWidth, top = BtopLeft+frameHeight, align_to = 'plot')
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/JFF.png",plot=p_full,device="png",scale = 1)
p_full
```
## Eyetracking
```{r FF E summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mEFF_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In the eye-tracking condition, divergent transitions were corrected by increasing adapt_delta parameter to 0.95. The model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher FF than segments 2 and 4
This is : (2\*segment3+2\*segment5+segment3:day2 + segment5:day2)/3 < (2\*segment2+2\*segment4+segment2:day2+segment4:day2)/2
```{r FF E segment effect, echo = FALSE}
conditional_effects(model1,effect="segment")
hypothesis(model1,'(2*segment3+2*segment5+segment3:day2 + segment5:day2)/3 > (2*segment2+2*segment4+segment2:day2+segment4:day2)/2')
```
### Day effect
Is FF on day 1 equal to FF on day 2
This is : day 2 > 0
```{r FF E day effect, echo = FALSE}
conditional_effects(model1,effect="day")
hypothesis(model1,'day2<0')
```
### Segment-day interaction effect
Is the difference between segments 2 + 4 and 3 + 5 significantly different than the difference of segments 2 + 4 and 3 + 5 on second day
( segment3:day2 + segment5:day2)/3<(segment2:day2+segment4:day2)/2
```{r FF E interaction effect, echo = FALSE}
hypothesis(model1,'( segment3:day2 + segment5:day2)/3<(segment2:day2+segment4:day2)/2')
model = model1
dataTest <- dataE
dataTestAvg <- aggregate(FF ~ subjects + segment + day, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$FF),]
condEffDay2 <- conditional_effects(model,effect="segment:day")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
dataTestAvg$day <- as.factor(dataTestAvg$day)
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, FF, colour = day),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
geom_point(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
# coord_cartesian(ylim=c(-1.2,-0.4)) +
inset_element(p_check,left = LbottomRight, bottom = BbottomRight, right = LbottomRight+frameWidth, top = BbottomRight+frameHeight, align_to = 'plot')
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/EFF.png",plot=p_full,device="png",scale = 1)
p_full
```
## Second experiment
FF Population-Level Effects:
```{r FF 2 summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/m2FF_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In Experiment 2, the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher FF than segments 2 and 4
This is : (segment3+segment5)/3 < (segment2+segment4)/2
```{r FF 2 segment effect, echo = FALSE}
hypothesis(model1,'(segment3+segment5)/3<(segment2+segment4)/2')
model = model1
dataTest <- data2
dataTestAvg <- aggregate(FF ~ repeated + subjects + segment, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$FF),]
condEffDay2 <- conditional_effects(model,effect="segment")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, FF, colour=repeated),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment"]],
aes(x=as.numeric(effect1__), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d")
) +
geom_point(
data=condEffDay2[["segment"]],
aes(x=as.numeric(effect1__), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d")
) +
inset_element(p_check, left = LtopLeft, bottom = BtopLeft, right = LtopLeft+frameWidth, top = BtopLeft+frameHeight, align_to = 'plot')
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/2FF.png",plot=p_full,device="png",scale = 1)
p_full
```
# Pupil size analysis
Pupil size was only computed on the joystick condition, in both experiments: Experiment 1 : pupil size ~ segment\*day + segmentLength + (1 + segmentLength + segment\*day | subjects); Experiment 2 : pupil size ~ segment + segmentLength + (1 + segmentLength + segment | subjects)). Priors over parameters were set as student distributions (df = 1, mean = 0, standard of deviation = 0.1). Divergent transitions were corrected by increasing adapt_delta parameter to 0.95.
## Joystick
```{r pupil J summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mJpupil_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In the Joystick condition of Experiment 1, the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher pupil than segments 2 and 4
This is : (2\*segment3+2\*segment5+segment3:day2 + segment5:day2) > (2\*segment4+segment4:day2)/2
```{r pupil J segment effect, echo = FALSE}
conditional_effects(model1,effect="segment")
hypothesis(model1,'(2*segment3+2*segment5+segment3:day2 + segment5:day2)/2 > (2*segment4+segment4:day2)/2')
```
### Day effect
Is pupil on day 1 equal to pupil on day 2
This is : day 2 < 0
```{r pupil J day effect, echo = FALSE}
conditional_effects(model1,effect="day")
hypothesis(model1,'day2 > 0')
```
### Segment-day interaction effect
Is the difference between segments 2 + 4 and 3 + 5 significantly different than the difference of segments 2 + 4 and 3 + 5 on second day
(segment3:day2 + segment5:day2)/2>(segment4:day2)/2
```{r pupil J interaction effect, echo = FALSE}
hypothesis(model1,'(segment3:day2 + segment5:day2)/2>(segment4:day2)/2')
model = model1
dataTest <- dataJ
dataTest$segment[dataTest$segment==1]=NaN
dataTest <- dataTest[!is.nan(dataTest$pupil_size),]
dataTest <- dataTest[!is.nan(dataTest$segment),]
dataTest$pupil_size <- dataTest$pupil_size
dataTestAvg <- aggregate(pupil_size ~ subjects + segment + day, dataTest ,FUN = mean)
condEffDay2 <- conditional_effects(model,effect="segment:day")
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(1, 1),
legend.justification = c(1, 1),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, pupil_size,colour=day),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
geom_point(
data=condEffDay2[["segment:day"]],
aes(x=(as.numeric(effect1__)+c(0.9,1.1,0.9,1.1,0.9,1.1,0.9,1.1)-1), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
inset_element(p_check,left = LbottomRight, bottom = BbottomRight, right = LbottomRight+frameWidth, top = BbottomRight+frameHeight, align_to = 'plot')
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/pupil.png",plot=p_full,device="png",scale = 1)
p_full
```
## Second experiment
```{r pupil 2 summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/m2pupil_good.RDS",sep = ''))
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("In Experiment 2, the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n Summary of estimates, and diagnostics can be found in the table below: \n')
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
cat('\n\n effect sizes and significance can be found in this table: \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Segment effect
Do segments 3 and 5 have higher pupil than segments 2 and 4
This is : (segment3+segment5)/2 > (segment4)/2
```{r pupil 2 segment effect, echo = FALSE}
hypothesis(model1,'(segment3+segment5)/2 > (segment4)/2')
model = model1
dataTest <- data2
dataTest$segment[dataTest$segment==1]=NaN
dataTest <- dataTest[!is.nan(dataTest$pupil_size),]
dataTest <- dataTest[!is.nan(dataTest$segment),]
dataTest$pupil_size <- dataTest$pupil_size
dataTestAvg <- aggregate(pupil_size ~ repeated + subjects + segment, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$pupil_size),]
condEffDay2 <- conditional_effects(model,effect="segment")
dataTestAvg$segment <- as.numeric(dataTestAvg$segment)
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
geom_point(
data=dataTestAvg,
aes(segment, pupil_size, colour=repeated),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["segment"]],
aes(x=(as.numeric(effect1__)+1), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#00bfc4","#f8766d","#00bfc4","#f8766d")
) +
geom_point(
data=condEffDay2[["segment"]],
aes(x=(as.numeric(effect1__)+1), y=estimate__),
size=3,
colour=c("#00bfc4","#f8766d","#00bfc4","#f8766d")
) +
inset_element(p_check, left = LtopLeft, bottom = BtopLeft, right = LtopLeft+frameWidth, top = BtopLeft+frameHeight, align_to = 'plot')
# p_full <- p_CIposterior + p_interm + plot_layout(ncol = 1)
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/2pupil.png",plot=p_full,device="png",scale = 1)
p_full
```
# mediation analysis RMSE
We ran mediation analyses to evaluate the implication of prediction mechanisms (feed-forward) in mediating the effect of segment on RMSE . For this analysis, the segments were averaged within the random and repeated groups, in order to have a binary segment condition (either repeated or random).
## Joystick
```{r RMSE J mediation FF, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mJRMSEmediation_good.RDS",sep = ''))
mediation(model1)
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("The model's direct effect explanatory power is (R2 = ",round(R2[2,1],2),", 95% CI [",round(R2[2,3],2),",",round(R2[2,4],2),",], adj. R2 = ",round(adjR2[2,1],2),").")
cat("\n The model's indirect effect explanatory power is (R2 = ",round(R2[1,1],2),", 95% CI [",round(R2[1,3],2),",",round(R2[1,4],2),",], adj. R2 = ",round(adjR2[1,1],2),").")
```
## Eyetracking
```{r RMSE E mediation on FF, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/mERMSEmediation_good.RDS",sep = ''))
mediation(model1)
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("The model's direct effect explanatory power is (R2 = ",round(R2[2,1],2),", 95% CI [",round(R2[2,3],2),",",round(R2[2,4],2),",], adj. R2 = ",round(adjR2[2,1],2),").")
cat("\n The model's indirect effect explanatory power is (R2 = ",round(R2[1,1],2),", 95% CI [",round(R2[1,3],2),",",round(R2[1,4],2),",], adj. R2 = ",round(adjR2[1,1],2),").")
```
## Second experiment'
```{r RMSE 2 mediation 0.057, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/m2RMSEmediation_good.RDS",sep = ''))
mediation(model1)
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("The model's direct effect explanatory power is (R2 = ",round(R2[2,1],2),", 95% CI [",round(R2[2,3],2),",",round(R2[2,4],2),",], adj. R2 = ",round(adjR2[2,1],2),").")
cat("\n The model's indirect effect explanatory power is (R2 = ",round(R2[1,1],2),", 95% CI [",round(R2[1,3],2),",",round(R2[1,4],2),",], adj. R2 = ",round(adjR2[1,1],2),").")
```
# Hidden target task
After implicit learning task, we ran 12 trials of only three segments were the target was hidden during 4seconds either in repeated task or during the last random segment.
We analyse the results using RMSE, cross correlation, mutual information and spectral difference
RMSE doesn't seem to be a good candidate as it doesn't take into account the time delay. Lag isn't a very noisy measure.
Cross correlation and mutual information seem to preferentially pick the low frequencies of the signal thus not taking the correction and differences between segments in higher frequencies. The best measurement in or point of view is the spectral difference.
## Root mean square error (RMSE) without ouliers (-3 < Z < 3)
RMSE ~ repeated:occulted + segmentLength + (1 + segmentLength + repeated:occulted | subjects)
RMSE Population-Level Effects and R2 of first occultation model:
```{r RMSE Occulted summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/m2_occult_RMSE_good.RDS",sep = ''))
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n effect sizes : \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Repeated vs random effect
Has repeated (1) got a lower RMSE than repeated (0)
This is : repeated < 0
```{r RMSE occulted repeated effect, echo = FALSE}
conditional_effects(model1,effect="repeated")
hypothesis(model1,'repeated1 < 0')
```
### Occulted vs visible effect
Has occulted segments got a higher RMSE than visible segments
This is : occulted1 > 0
```{r RMSE occulted occulted effect, echo = FALSE}
conditional_effects(model1,effect="occulted")
hypothesis(model1,'occulted1 > 0')
```
### Occulted and repeated interraction effect
Has occulted segments got a higher RMSE than visible segments
This is : occulted1:repeated1 + repeated1 < 0
```{r RMSE occulted interaction effect, echo = FALSE}
hypothesis(model1,'occulted1:repeated1 + repeated1 < 0')
model = model1
dataTest <- dataOcculted
dataTestAvg <- aggregate(RMSE ~ repeated + occulted + subjects, dataTest ,FUN = mean)
dataTestAvg <- dataTestAvg[!is.nan(dataTestAvg$RMSE),]
condEffDay2 <- conditional_effects(model,effect="occulted:repeated")
theme_set(theme_classic())
p_check <- pp_check(model,ndraws = 100) +
theme(
panel.border = element_rect(colour = "black", fill=NA, size=1),
plot.margin = margin(t=0,r=0,b=0,l=0),
panel.background = element_rect(fill = "transparent", colour = NA),
legend.margin = margin(t=0,r=0,b=0,l=0),
legend.background = element_rect(fill="transparent",colour=NA),
legend.position = c(0.8,0.8),
legend.text = element_text(size=6),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()
)
p_interm <- ggplot() +
theme(
legend.position = c(0.5, 1),
legend.direction = "horizontal",
legend.justification = c(0.5, 0.5),
legend.background = element_rect(fill = "transparent", colour = NA)
) +
# coord_cartesian(ylim=c(-1,1.5)) +
geom_point(
data=dataTestAvg,
aes(occulted, RMSE, colour=repeated),
size=1,
alpha=0.5,
position=position_jitter(h=0, w=0.05)
) +
geom_errorbar(
data=condEffDay2[["occulted:repeated"]],
aes(x=(as.numeric(effect1__)+c(-0.1,0.1,-0.1,0.1)), ymin=lower__, ymax=upper__),
width=0.15,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
geom_point(
data=condEffDay2[["occulted:repeated"]],
aes(x=(as.numeric(effect1__)+c(-0.1,0.1,-0.1,0.1)), y=estimate__),
size=3,
colour=c("#f8766d","#00bfc4","#f8766d","#00bfc4")
) +
inset_element(p_check, left = LtopLeft, bottom = BtopLeft, right = LtopLeft+frameWidth, top = BtopLeft+frameHeight, align_to = 'plot')
# p_full <- p_CIposterior + p_interm + plot_layout(ncol = 1)
p_full <- p_interm + plot_layout(ncol = 1)
ggsave(filename="../publication/manuscript plots/2occultedRMSE.png",plot=p_full,device="png",scale = 1)
p_full
```
## Spectrale difference (SpectDiff) without ouliers (-3 < Z < 3)
spectral_diff ~ occulted\*repeated + segmentLength + (1 + segmentLength + occulted\*repeated | subjects)
Spectral difference Population-Level Effects:
```{r Spectral difference summary, echo = FALSE}
model1 <- readRDS(paste(home,"/savings/m2_occult_SpecDiff_good.RDS",sep = ''))
MySum <- summary(model1)
knitr::kable(MySum$fixed, digits = 2)
R2 <- bayes_R2(model1)
adjR2 <- loo_R2(model1)
cat("\n\nR2 for model\n")
cat("the model's explanatory power is substantial (R2 = ",round(R2[1],2),", 95% CI [",round(R2[3],2),",",round(R2[4],2),",], adj. R2 = ",round(adjR2[1],2),").")
cat('\n\n effect sizes : \n')
knitr::kable(sexit(model1), digits = c(2,2,2,2,4,4,4))
```
### Repeated vs random effect
Has repeated (1) got a lower spectral difference than repeated (0)
This is : repeated < 0
```{r Spectral difference repeated effect, echo = FALSE}
conditional_effects(model1,effect="repeated")
hypothesis(model1,'repeated1 < 0')
```