Psych 45 MIA 4 Results (Spring 2020)

Data Preparation

Read in data

dt = read.csv('Psych45 MIA 4_ Emotions and Memory 2020.csv')
# dt <- read.csv('/Users/zeynepenkavi/Documents/Psych45/WWW/demo_files/Psych45_MIA4 (Responses) - Form Responses 1.csv')

Exclude test data

names(dt) <- c("Timestamp", "Name", "Words", "Responses")

dt = dt%>%
  filter(Name != "Test" ) %>%
  distinct(Name, .keep_all=TRUE)

Convert to long format

longify = function(data){
  
  Words  = unlist(strsplit(gsub("\"|\\[|\\]| |\n", "", as.character(data$Words)), ","))
  Responses = unlist(strsplit(gsub("\"|\\[|\\]| |\n", "", as.character(data$Responses)), ","))
  
  if(length(Words) == length(Responses)){
    out = data.frame(word = Words, response = Responses)
  }
  
  else {
    Warning = paste("Warning: Longify failes for subject", unique(data$Name))
    out = data.frame(word = Warning, response = NA)
    }
  
   return(out)
}

dt_long = dt %>% 
  group_by(Name) %>%
  do(longify(.)) %>%
  drop_na()

Add valence and arousal info

list_info = read.csv('valence_ratings.csv')
list_info$word = as.character(list_info$word)


dt_long = dt_long %>%
  left_join(list_info, by = "word")

Results

Do participants remember more emotional than neutral words?

dt_long %>%
  group_by(valence) %>%
  summarise(prop_remb = sum(as.numeric(response))/n(),
            sem_remb = sem(as.numeric(response))) %>%
  ggplot(aes(x = valence, y = prop_remb, col=valence))+
  geom_point()+
  geom_errorbar(aes(ymin = prop_remb - sem_remb, ymax = prop_remb+sem_remb), position=position_dodge(0.9), width=0.25)+
  theme_classic()+
  scale_color_manual(values=c("red", "blue","green"))+
  theme(legend.position = "none")+
  ylab("Proportion remembered")+
  ylim(c(0.2, 0.5))

Most commonly retrieved words

dt_long %>%
  group_by(word) %>%
  summarise(prop_remb = sum(as.numeric(response))/n()) %>%
  arrange(-prop_remb)

## # A tibble: 45 x 2
##    word       prop_remb
##    <chr>          <dbl>
##  1 rape           0.795
##  2 suicide        0.615
##  3 love           0.557
##  4 laughter       0.549
##  5 depression     0.541
##  6 mother         0.525
##  7 kiss           0.475
##  8 murderer       0.467
##  9 leprosy        0.459
## 10 sweetheart     0.443
## # … with 35 more rows

Serial position effect

dt_long = dt_long %>%
  group_by(Name) %>%
  mutate(serial_position = 1:n()) %>%
  mutate(serial_category = ifelse(serial_position <6, "primacy", ifelse(serial_position>40, "recency", "middle"))) %>%
  mutate(serial_category = factor(serial_category,levels = c("primacy", "middle", "recency"))) %>%
  ungroup()

Categorical

dt_long %>%
  group_by(serial_category) %>%
  summarise(prop_remb = sum(as.numeric(response))/n(),
            sem_remb = sem(as.numeric(response))) %>%
  ggplot(aes(serial_category, prop_remb))+
  geom_point()+
  geom_line(group=1)+
  geom_errorbar(aes(ymin = prop_remb - sem_remb, ymax = prop_remb+sem_remb), position=position_dodge(0.9), width=0.25)+
  theme_classic()+
  ylim(c(0.2, 0.6))+
  ylab("Proportion remembered")

Continuous

dt_long %>%
  group_by(serial_position) %>%
  summarise(prop_remb = sum(as.numeric(response))/n(),
            sem_remb = sem(as.numeric(response))) %>%
  ggplot(aes(serial_position, prop_remb))+
  geom_point()+
  geom_errorbar(aes(ymin = prop_remb - sem_remb, ymax = prop_remb+sem_remb), position=position_dodge(0.9), width=0.25)+
  theme_classic()+
  geom_smooth()+
  ylab("Proportion remembered")

Is probability of recalling words in the middle of the list higher if the word is emotional?

dt_long %>%
  group_by(serial_category, valence) %>%
  summarise(prop_remb = sum(as.numeric(response))/n(),
            sem_remb = sem(as.numeric(response))) %>%
  ggplot(aes(serial_category, prop_remb, group= valence, fill=valence))+
  geom_bar(stat='identity', position=position_dodge())+
  geom_errorbar(aes(ymin = prop_remb - sem_remb, ymax = prop_remb+sem_remb), position=position_dodge(0.9), width=0.25)+
  scale_fill_manual(values=c("red", "green", "blue"))+
  theme_classic()+
  ylab("Proportion remembered")

dt_long %>%
  group_by(serial_position, valence)%>%
  summarise(prop_remb = sum(as.numeric(response))/n(),
            sem_remb = sem(as.numeric(response))) %>%
  ggplot(aes(serial_position, prop_remb, group= valence, col=valence))+
  geom_point(position=position_dodge(0.9), alpha=0.2)+
  geom_smooth(alpha = 0.5)+
  # geom_errorbar(aes(ymin = prop_remb - sem_remb, ymax = prop_remb+sem_remb), position=position_dodge(0.9), width=0.25)+
  scale_fill_manual(values=c("red", "green", "blue"))+
  theme_classic()+
  ylab("Proportion remembered")

Effect of valence/arousal on retrieval of each word

dt_long %>%
  group_by(valence_mean) %>%
  summarise(prop_remb = sum(as.numeric(response))/n(),
            sem_remb = sem(as.numeric(response))) %>%
  ggplot(aes(valence_mean, prop_remb))+
  geom_point()+
  geom_smooth(se=FALSE)+
  theme_classic()+
  ylab("Proportion remembered")+
  xlab("Valence rating")