Zen meditation neutralises emotional evaluation, but not implicit
affective processing of words
# Load necessary libraries
pacman::p_load(pacman, ggplot2, dplyr, GGally, readxl)
# Read the datasets
MWT_B_Intelligence_Test <- read_excel("MWT-B, Intelligence Test , Lusnig, Radach, Mueller, Hofmann, 2020.xlsx")
D2_R_Concentration_Capacity_Test <- read_excel("D2-R, Concentration Capacity Test, Lusnig, Radach, Mueller, Hofmann, 2020.xlsx")
Big_5_Personality_Test <- read_excel("Big 5, Personality Test, Lusnig, Radach, Mueller, Hofmann, 2020.xlsx")
Valence_Rating_Data <- read_excel("Valence Rating Data, Lusnig, Radach, Mueller, Hofmann, 2020.xlsx")
Response_Time_Data <- read_excel("Response Time Data, Lusnig, Radach, Mueller, Hofmann, 2020.xlsx")
# Create a function to examine datasets
examine_data <- function(data, dataset_name) {
cat("\n============================\n")
cat("Dataset: ", dataset_name, "\n")
cat("============================\n")
# View structure of the dataset
cat("\nStructure of the dataset:\n")
str(data)
# Summary of the dataset
cat("\nSummary statistics:\n")
print(summary(data))
# First few rows of the dataset
cat("\nFirst 6 rows of the dataset:\n")
print(head(data))
}
# Examine each dataset
examine_data(MWT_B_Intelligence_Test, "MWT-B Intelligence Test")
##
## ============================
## Dataset: MWT-B Intelligence Test
## ============================
##
## Structure of the dataset:
## tibble [40 × 3] (S3: tbl_df/tbl/data.frame)
## $ group : num [1:40] 0 0 0 0 0 0 0 0 0 0 ...
## $ subject: num [1:40] 1 2 3 4 5 6 7 8 9 10 ...
## $ Score : num [1:40] 32 28 31 29 26 17 22 25 22 32 ...
##
## Summary statistics:
## group subject Score
## Min. :0.0 Min. : 1.00 Min. :17.00
## 1st Qu.:0.0 1st Qu.: 5.75 1st Qu.:22.00
## Median :0.5 Median :10.50 Median :25.00
## Mean :0.5 Mean :10.50 Mean :25.02
## 3rd Qu.:1.0 3rd Qu.:15.25 3rd Qu.:28.00
## Max. :1.0 Max. :20.00 Max. :32.00
##
## First 6 rows of the dataset:
## # A tibble: 6 × 3
## group subject Score
## <dbl> <dbl> <dbl>
## 1 0 1 32
## 2 0 2 28
## 3 0 3 31
## 4 0 4 29
## 5 0 5 26
## 6 0 6 17
examine_data(D2_R_Concentration_Capacity_Test, "D2-R Concentration Capacity Test")
##
## ============================
## Dataset: D2-R Concentration Capacity Test
## ============================
##
## Structure of the dataset:
## tibble [40 × 5] (S3: tbl_df/tbl/data.frame)
## $ group : num [1:40] 0 0 0 0 0 0 0 0 0 0 ...
## $ subject : num [1:40] 1 2 3 4 5 6 7 8 9 10 ...
## $ Number of Processed Target Objects: num [1:40] 161 218 164 113 200 190 152 204 167 158 ...
## $ Concentration Capacity : num [1:40] 134 168 132 110 158 152 141 187 159 148 ...
## $ Percentage of Errors : num [1:40] 16.77 22.94 19.51 2.65 21 ...
##
## Summary statistics:
## group subject Number of Processed Target Objects
## Min. :0.0 Min. : 1.00 Min. :113.0
## 1st Qu.:0.0 1st Qu.: 5.75 1st Qu.:153.5
## Median :0.5 Median :10.50 Median :167.0
## Mean :0.5 Mean :10.50 Mean :168.7
## 3rd Qu.:1.0 3rd Qu.:15.25 3rd Qu.:191.8
## Max. :1.0 Max. :20.00 Max. :218.0
## Concentration Capacity Percentage of Errors
## Min. : 73.0 Min. : 0.5556
## 1st Qu.:128.5 1st Qu.: 6.9947
## Median :154.5 Median :12.8817
## Mean :146.0 Mean :13.7258
## 3rd Qu.:164.5 3rd Qu.:18.9870
## Max. :197.0 Max. :37.1951
##
## First 6 rows of the dataset:
## # A tibble: 6 × 5
## group subject `Number of Processed Target Objects` `Concentration Capacity`
## <dbl> <dbl> <dbl> <dbl>
## 1 0 1 161 134
## 2 0 2 218 168
## 3 0 3 164 132
## 4 0 4 113 110
## 5 0 5 200 158
## 6 0 6 190 152
## # ℹ 1 more variable: `Percentage of Errors` <dbl>
examine_data(Big_5_Personality_Test, "Big 5 Personality Test")
##
## ============================
## Dataset: Big 5 Personality Test
## ============================
##
## Structure of the dataset:
## tibble [40 × 10] (S3: tbl_df/tbl/data.frame)
## $ group : num [1:40] 0 0 0 0 0 0 0 0 0 0 ...
## $ subject : num [1:40] 1 2 3 4 5 6 7 8 9 10 ...
## $ Neuroticism : num [1:40] 4 3 5 6 4 2 5 4 6 6 ...
## $ Extraversion : num [1:40] 4 4 7 7 5 7 5 5 3 5 ...
## $ Openness : num [1:40] 4 6 8 8 6 6 8 5 8 8 ...
## $ Conscientiousness : num [1:40] 3 6 3 3 4 4 6 4 5 6 ...
## $ Agreeableness : num [1:40] 5 5 6 6 4 6 8 5 6 3 ...
## $ Need for Achievement and Performance: num [1:40] 4 3 5 2 3 4 3 5 1 3 ...
## $ Need for Safety and Peace : num [1:40] 4 4 4 1 6 2 3 3 5 6 ...
## $ Need for Power and Influence : num [1:40] 4 6 5 2 7 3 3 6 1 3 ...
##
## Summary statistics:
## group subject Neuroticism Extraversion Openness
## Min. :0.0 Min. : 1.00 Min. :1.00 Min. :2.00 Min. :2.0
## 1st Qu.:0.0 1st Qu.: 5.75 1st Qu.:3.75 1st Qu.:5.00 1st Qu.:4.0
## Median :0.5 Median :10.50 Median :4.00 Median :6.00 Median :6.0
## Mean :0.5 Mean :10.50 Mean :4.25 Mean :5.75 Mean :5.4
## 3rd Qu.:1.0 3rd Qu.:15.25 3rd Qu.:5.00 3rd Qu.:7.00 3rd Qu.:7.0
## Max. :1.0 Max. :20.00 Max. :7.00 Max. :8.00 Max. :9.0
## Conscientiousness Agreeableness Need for Achievement and Performance
## Min. :2.00 Min. :1.00 Min. :1.0
## 1st Qu.:3.00 1st Qu.:4.00 1st Qu.:3.0
## Median :5.00 Median :5.00 Median :5.0
## Mean :4.65 Mean :5.20 Mean :4.6
## 3rd Qu.:6.00 3rd Qu.:6.25 3rd Qu.:6.0
## Max. :8.00 Max. :9.00 Max. :8.0
## Need for Safety and Peace Need for Power and Influence
## Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.00
## Median :5.000 Median :5.00
## Mean :4.725 Mean :4.95
## 3rd Qu.:5.250 3rd Qu.:6.00
## Max. :9.000 Max. :7.00
##
## First 6 rows of the dataset:
## # A tibble: 6 × 10
## group subject Neuroticism Extraversion Openness Conscientiousness
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 1 4 4 4 3
## 2 0 2 3 4 6 6
## 3 0 3 5 7 8 3
## 4 0 4 6 7 8 3
## 5 0 5 4 5 6 4
## 6 0 6 2 7 6 4
## # ℹ 4 more variables: Agreeableness <dbl>,
## # `Need for Achievement and Performance` <dbl>,
## # `Need for Safety and Peace` <dbl>, `Need for Power and Influence` <dbl>
examine_data(Valence_Rating_Data, "Valence Rating Data")
##
## ============================
## Dataset: Valence Rating Data
## ============================
##
## Structure of the dataset:
## tibble [7,963 × 8] (S3: tbl_df/tbl/data.frame)
## $ subject : chr [1:7963] "C1" "C1" "C1" "C1" ...
## $ group : num [1:7963] 1 1 1 1 1 1 1 1 1 1 ...
## $ Rating : num [1:7963] -3 -3 -3 -3 -3 -3 -3 -3 -2 -2 ...
## $ neg_high: num [1:7963] 1 1 1 1 0 0 0 0 1 1 ...
## $ neg_low : num [1:7963] 0 0 0 0 1 1 1 1 0 0 ...
## $ pos : num [1:7963] 0 0 0 0 0 0 0 0 0 0 ...
## $ time : num [1:7963] 0 0 0 0 0 0 0 0 0 0 ...
## $ item : chr [1:7963] "ERDBEBEN" "RAUB" "SABOTAGE" "MALARIA" ...
##
## Summary statistics:
## subject group Rating neg_high
## Length:7963 Min. :0.0000 Min. :-3.0000 Min. :-1
## Class :character 1st Qu.:0.0000 1st Qu.:-1.0000 1st Qu.:-1
## Mode :character Median :1.0000 Median : 0.0000 Median : 0
## Mean :0.5001 Mean :-0.3187 Mean : 0
## 3rd Qu.:1.0000 3rd Qu.: 0.0000 3rd Qu.: 1
## Max. :1.0000 Max. : 3.0000 Max. : 1
## neg_low pos time item
## Min. :-1.000000 Min. :-1.000000 Min. :0.0000 Length:7963
## 1st Qu.:-1.000000 1st Qu.:-1.000000 1st Qu.:0.0000 Class :character
## Median : 0.000000 Median : 0.000000 Median :0.0000 Mode :character
## Mean :-0.001507 Mean :-0.002386 Mean :0.4998
## 3rd Qu.: 0.000000 3rd Qu.: 0.000000 3rd Qu.:1.0000
## Max. : 1.000000 Max. : 1.000000 Max. :1.0000
##
## First 6 rows of the dataset:
## # A tibble: 6 × 8
## subject group Rating neg_high neg_low pos time item
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 C1 1 -3 1 0 0 0 ERDBEBEN
## 2 C1 1 -3 1 0 0 0 RAUB
## 3 C1 1 -3 1 0 0 0 SABOTAGE
## 4 C1 1 -3 1 0 0 0 MALARIA
## 5 C1 1 -3 0 1 0 0 STAU
## 6 C1 1 -3 0 1 0 0 VERLIES
examine_data(Response_Time_Data, "Response Time Data")
##
## ============================
## Dataset: Response Time Data
## ============================
##
## Structure of the dataset:
## tibble [7,016 × 8] (S3: tbl_df/tbl/data.frame)
## $ subject : chr [1:7016] "C1" "C1" "C1" "C1" ...
## $ group : num [1:7016] 1 1 1 1 1 1 1 1 1 1 ...
## $ RT : num [1:7016] 657 594 532 657 844 ...
## $ negative_high: num [1:7016] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ negative_low : num [1:7016] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ positive : num [1:7016] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ time : num [1:7016] 0 0 0 0 0 0 0 0 0 0 ...
## $ item : chr [1:7016] "TARNUNG" "SCHWUR" "PEGEL" "SKORPION" ...
##
## Summary statistics:
## subject group RT negative_high
## Length:7016 Min. :0.0000 Min. : 0.3009 Min. :-1.000000
## Class :character 1st Qu.:0.0000 1st Qu.:594.4577 1st Qu.: 0.000000
## Mode :character Median :0.0000 Median :668.5010 Median : 0.000000
## Mean :0.4999 Mean :656.1289 Mean : 0.006414
## 3rd Qu.:1.0000 3rd Qu.:750.3961 3rd Qu.: 1.000000
## Max. :1.0000 Max. :999.4331 Max. : 1.000000
## negative_low positive time item
## Min. :-1.000000 Min. :-1.000000 Min. :0.0000 Length:7016
## 1st Qu.: 0.000000 1st Qu.: 0.000000 1st Qu.:0.0000 Class :character
## Median : 0.000000 Median : 0.000000 Median :1.0000 Mode :character
## Mean :-0.007697 Mean : 0.008694 Mean :0.5078
## 3rd Qu.: 0.000000 3rd Qu.: 1.000000 3rd Qu.:1.0000
## Max. : 1.000000 Max. : 1.000000 Max. :1.0000
##
## First 6 rows of the dataset:
## # A tibble: 6 × 8
## subject group RT negative_high negative_low positive time item
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 C1 1 657. -1 -1 -1 0 TARNUNG
## 2 C1 1 594. -1 -1 -1 0 SCHWUR
## 3 C1 1 532. -1 -1 -1 0 PEGEL
## 4 C1 1 657. -1 -1 -1 0 SKORPION
## 5 C1 1 844. -1 -1 -1 0 SCHEICH
## 6 C1 1 688. -1 -1 -1 0 TAUMEL
# Group summary and comparison
MWT_summary <- MWT_B_Intelligence_Test %>%
group_by(group) %>%
summarise(mean_score = mean(Score), sd_score = sd(Score))
print(MWT_summary)
## # A tibble: 2 × 3
## group mean_score sd_score
## <dbl> <dbl> <dbl>
## 1 0 25.0 4.10
## 2 1 25.1 3.24
# Boxplot comparing intelligence scores by group
ggplot(MWT_B_Intelligence_Test, aes(x = factor(group), y = Score)) +
geom_boxplot() +
labs(title = "MWT-B Intelligence Test Scores by Group", x = "Group", y = "Score") +
theme()
# Correlation between processed target objects, concentration capacity, and errors
correlation_matrix <- cor(D2_R_Concentration_Capacity_Test %>% select(`Number of Processed Target Objects`, `Concentration Capacity`, `Percentage of Errors`))
print(correlation_matrix)
## Number of Processed Target Objects
## Number of Processed Target Objects 1.00000000
## Concentration Capacity 0.79951087
## Percentage of Errors -0.02501962
## Concentration Capacity Percentage of Errors
## Number of Processed Target Objects 0.7995109 -0.02501962
## Concentration Capacity 1.0000000 -0.45874739
## Percentage of Errors -0.4587474 1.00000000
# Scatter plot between concentration capacity and error percentage
ggplot(D2_R_Concentration_Capacity_Test, aes(x = `Concentration Capacity`, y = `Percentage of Errors`, color = factor(group))) +
geom_point() +
labs(title = "Concentration Capacity vs. Error Percentage", x = "Concentration Capacity", y = "Error Percentage") +
theme()
# Ensure only numeric columns are selected for scaling.
Big_5_scaled <- Big_5_Personality_Test %>%
select(group, Neuroticism:`Need for Power and Influence`) %>% # Select the necessary columns
mutate(across(Neuroticism:`Need for Power and Influence`, scale)) # Scale numeric columns
# Parallel coordinate plot
ggparcoord(Big_5_scaled, columns = 2:9, groupColumn = 1, scale = "globalminmax") +
labs(title = "Big 5 Personality Traits Comparison by Group") +
theme()
# Summary of valence ratings
Valence_summary <- Valence_Rating_Data %>%
group_by(group) %>%
summarise(mean_rating = mean(Rating), sd_rating = sd(Rating))
print(Valence_summary)
## # A tibble: 2 × 3
## group mean_rating sd_rating
## <dbl> <dbl> <dbl>
## 1 0 -0.248 1.34
## 2 1 -0.390 1.48
# Histogram of ratings
ggplot(Valence_Rating_Data, aes(x = Rating, fill = factor(group))) +
geom_histogram(bins = 20, position = "dodge") +
labs(title = "Distribution of Valence Ratings by Group", x = "Rating", y = "Count") +
theme()
# Summary of response times by group
RT_summary <- Response_Time_Data %>%
group_by(group) %>%
summarise(mean_RT = mean(RT), sd_RT = sd(RT))
print(RT_summary)
## # A tibble: 2 × 3
## group mean_RT sd_RT
## <dbl> <dbl> <dbl>
## 1 0 667. 188.
## 2 1 646. 183.
# Boxplot of response times by group
ggplot(Response_Time_Data, aes(x = factor(group), y = RT)) +
geom_boxplot() +
labs(title = "Response Time by Group", x = "Group", y = "Response Time (ms)") +
theme()
# Scatter plot of RT vs positive/negative tagging
ggplot(Response_Time_Data, aes(x = RT, y = positive, color = factor(group))) +
geom_point() +
labs(title = "Response Time vs Positive Emotional Tagging", x = "Response Time (ms)", y = "Positive Tagging") +
theme()
