Memory in Action 4: Emotion and memory

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These visualizations are based on 120 submissions.

Process data

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

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()

list_info = read.csv(here("data_4",'valence_ratings.csv'))
list_info$word = as.character(list_info$word)


dt_long = dt_long %>%
  left_join(list_info, by = "word") |> 
  mutate(response=as.numeric(response)) |> 
  filter(!is.na(valence)) # "hostage" and "mutilate" don't have ratings

Valence

How well are words remembered as a function of valence?

dt_long %>%
  group_by(valence) %>%
  ggplot(aes(x = valence, y = response, color=valence))+
  stat_summary(fun.data = "mean_cl_boot")+
  theme_classic()+
  scale_color_manual(values=c("negative"="red", "neutral"="blue","positive"="green"))+
  theme(legend.position = "none")+
  ylab("Proportion remembered")+
  coord_cartesian(ylim=c(0,.5))

ggsave(here(images, "valence.png"), dev="png")

More strongly valenced words (either positive or negative) are remembered better than neutral.

Which words are remembered best?

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

## # A tibble: 43 × 2
##    word       prop_remb
##    <chr>          <dbl>
##  1 mother         0.624
##  2 love           0.615
##  3 laughter       0.603
##  4 baby           0.5  
##  5 slaughter      0.487
##  6 kiss           0.470
##  7 murderer       0.444
##  8 depression     0.427
##  9 leprosy        0.419
## 10 rainbow        0.402
## # … with 33 more rows

Order

How does presentation order affect memory?

dt_serial = 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()

dt_serial %>%
  group_by(serial_category) %>%
  ggplot(aes(serial_category, response))+
  stat_summary(fun.data = "mean_cl_boot")+
  theme_classic()+
    coord_cartesian(ylim=c(0,.6))

Words presented in the first or last 5 words are remembered better than the ones in the middle.

This is also seen continuously.

dt_serial %>%
  group_by(serial_position) %>%
  ggplot(aes(serial_position, response))+
    stat_summary(fun.data = "mean_cl_boot")+
  theme_classic()+
  geom_smooth()+
  ylab("Proportion remembered")+
      coord_cartesian(ylim=c(0,.7))

ggsave(here(images, "time.png"), dev="png")

The earliest words are remembered best, declining over the first 10 or so words (primacy effect). Then in the middle memory is steady, but there’s a recency effect on the last 5 or so words.

Valence and Order

dt_serial %>%
  group_by(serial_category, valence) %>%
  ggplot(aes(serial_category, response, group= valence, color=valence))+
    stat_summary(fun.data = "mean_cl_boot", position=position_dodge(.2))+
  scale_color_manual(values=c("red", "green", "blue"))+
  theme_classic()+
  ylab("Proportion remembered")+
        coord_cartesian(ylim=c(0,.7))

ggsave(here(images, "valence_time.png"), dev="png")

The primacy effect is strong regardless of valence. Recency effect is strongest for valenced words. In the middle section, emotionally valenced words are remembed much better than neutral words.

dt_serial %>%
  group_by(serial_position, valence)%>%
  ggplot(aes(serial_position, response, group= valence, col=valence))+
  geom_smooth(alpha = 0.5)+
      stat_summary(fun.data = "mean_cl_boot", position=position_dodge(.2), geom="point")+
  scale_fill_manual(values=c("red", "green", "blue"))+
  theme_classic()+
        coord_cartesian(ylim=c(0,.8))+
  ylab("Proportion remembered")