EEG_temporal_reproduction/derivatives/behavioral_analysis.Rmd

---
title: "Blocked vs. Interleaved: How range contexts modulate time perception and its EEG signatures"
author: "Cemre Baykan, Xiuna Zhu, Artyom Zinchenko, Zhuanghua Shi"
date: "August 2023"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('preproc_data.R')
```


```{r}
m_dat= data_beh %>% dplyr::group_by(SubName,targetDur, cond, group) %>%    
  dplyr::summarise(n = n(),m_err = mean(rep_err), se_err = sd(rep_err)/ sqrt(n))

mm_dat= m_dat %>% dplyr::group_by(targetDur, cond, group) %>%    
  dplyr::summarise(n = n(),mm_err = mean(m_err), mse_err = sd(m_err)/ sqrt(n))
```

```{r}
m_dat_sub= m_dat %>% dplyr::group_by(SubName, cond, group) %>%    
  dplyr::summarise(n = n(),mm_err = mean(m_err), mse_err = sd(m_err)/ sqrt(n))

m_dat_sub %>% dplyr::group_by(cond, group) %>%    
  dplyr::summarise(n = n(),mmm_err = mean(mm_err), mse_err = sd(mm_err)/ sqrt(n))
```



###plot
```{r}
fig_mRep_exp1 = ggplot(mm_dat,
                       aes(targetDur, mm_err, group = interaction(cond, group), color = cond)) + 
  geom_point(size = 0.8) + geom_hline(yintercept = 0, linetype="dashed")+ 
  geom_line(data = mm_dat, size=0.8) + 
  geom_errorbar(aes(ymin = mm_err- mse_err , ymax = mm_err + mse_err), width = 0.02) + 
  theme_new + scale_color_manual(values = colors_plot) +
  theme(strip.background = element_blank()) +
  scale_x_continuous(breaks= c(400, 570, 800,1200,1700,2400), labels=c("400","566","800","1200","1700","2400"))+
  scale_y_continuous(breaks= c(-200, -100, 0,100,200), labels=c("-200","-100","0","100","200"))+
  labs(x = 'Target interval (ms)', y = 'Reproduction error (ms)', shape= '', color='')+ theme(legend.position=c(0.9,0.95))+ggtitle("")+
  theme(plot.title =element_text(size=12,face='bold')) 

#ggsave(file.path('figures','fig_beh.png'), fig_mRep_exp1, width = 4.3, height = 2.7)
fig_mRep_exp1
```

# LMM
```{r} 
m_dat$targetDur_n= m_dat$targetDur-1200 
m_dat$cond = as.factor(m_dat$cond)
m_dat$group = as.factor(m_dat$group)
m_dat= m_dat%>% dplyr::mutate(group= factor(group,levels=c("Upper","Lower")))
contrasts(m_dat$cond) = contr.Sum(levels(m_dat$cond))
contrasts(m_dat$group) = contr.Sum(levels(m_dat$group))
```

```{r}
# fit the linear mixed model
lmm2 = lmer(m_err ~ targetDur_n+ cond+ group+
              targetDur_n:cond + cond:group + # fixed effect and interaction
              (1|SubName), # random effect
              data = m_dat)
#output as table
tab_model(lmm2, p.val = 'kr') # Kenward-Roger approximation, alternative 'satterthwaite' approxmiation
```