Simulation o
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source("helper/slope.R")The goal of this simulation is to test whether the current analytical approach can control for confounds.
1. create confounding dataset (linearlly related last trial and dishabituation trial)
Takeaway: This section generates synthetic data where the deviant trial is linearly related to the last habituation trial, introducing a confound.
set.seed(123)
# Generate synthetic data
generate_fake_data <- function(n_subjects = 100) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(3, 5), 1)
trial_number <- 1:(n_trials + 1)
# Simulate exponential drop in habituation times with varying slopes
initial_looking_time <- runif(1, 5000, 10000)
decay_rate <- runif(1, 0.2, 0.5) # Randomly vary decay rate
habituation_times <- initial_looking_time * exp(-decay_rate * (1:n_trials))
deviant_time <- 1.2 * habituation_times[n_trials] + rnorm(1, 0, 500)
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(data, subject_data)
}
return(data)
}
# Generate dataset
fake_data_last_trial <- generate_fake_data(n_subjects = 100)habituation_data <- fake_data_last_trial %>%
# fist block
# before the dishabituation trials
filter(trial_number < deviant_position) %>%
# only keeping the relevant information
select(subject, trial_number, trial_looking_time)
adult_slope_df <- get_individual_slope(habituation_data)
adult_dishab_raw <- fake_data_last_trial %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_last_trial %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(dishab_diff = log(dishab_raw) - log(trial_looking_time)) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff, dishab_trial_num)1.1. constructed relationship
Visualizes the constructed linear relationship between the last habituation trial and the deviant trial.
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_last_trial %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "dishab raw ~ last trial", subtitle = "last trial vs dishab raw")## `geom_smooth()` using formula = 'y ~ x'
1.2 slope relationship
Explores the relationship between decay rate and dishabituation difference, no relationship
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "dishab raw ~ last trial", subtitle = "slope vs dishab")## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
2. create confounding dataset (linearlly related first trial and dishabituation trial)
Generates synthetic data where the deviant trial is linearly related to the first habituation trial
set.seed(123)
# Generate synthetic data
generate_fake_data <- function(n_subjects = 50) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(4, 6), 1)
trial_number <- 1:(n_trials + 1)
# Simulate exponential drop in habituation times with varying slopes
initial_looking_time <- runif(1, 5000, 10000)
decay_rate <- runif(1, 0.2, 0.5) # Randomly vary decay rate
habituation_times <- initial_looking_time * exp(-decay_rate * (1:n_trials))
deviant_time <- 1.5 * habituation_times[1] + rnorm(1, 0, 500) # Linearly related to the first trial
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(data, subject_data)
}
return(data)
}
# Generate dataset
fake_data_fist_trial <- generate_fake_data(n_subjects = 100)habituation_data <- fake_data_fist_trial %>%
# fist block
# before the dishabituation trials
filter(trial_number < deviant_position) %>%
# only keeping the relevant information
select(subject, trial_number, trial_looking_time)
adult_slope_df <- get_individual_slope(habituation_data)
adult_dishab_raw <- fake_data_fist_trial %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_fist_trial %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(dishab_diff = log(dishab_raw) - log(trial_looking_time)) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff, dishab_trial_num)2.1. constructed relationship
2.1.1 first trial vs dishab
Confirm the constructed relationship
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_fist_trial %>% filter(trial_number == 1),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "dishab raw ~ first trial",
subtitle = "dishab raw ~ first trial"
)## `geom_smooth()` using formula = 'y ~ x'
2.1.2 last trial vs dishab raw
Checking the impact on the last trial and the dishab raw, there’s an impact
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_fist_trial %>% group_by(subject) %>% filter(trial_number == (max(trial_number) -1)),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "dishab raw ~ first trial",
subtitle = "dishab raw ~ last trial"
)## `geom_smooth()` using formula = 'y ~ x'
2.2 slope?!?!
Explores the relationship between decay rate and dishabituation difference, negative relationship
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "dishab raw ~ first trial",
subtitle = "dishab diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
3. Confounding: slope and dishabituation directly related
Generates data where the deviant trial is explicitly related to the decay rate – this is kind of what we want to see in the dataset
# Set seed for reproducibility
set.seed(123)
# Generate synthetic data
generate_fake_data <- function(n_subjects = 50) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(4, 6), 1)
trial_number <- 1:(n_trials + 1)
# Simulate exponential drop in habituation times with varying slopes
initial_looking_time <- runif(1, 5000, 10000)
decay_rate <- runif(1, 0.2, 0.5) # Randomly vary decay rate
habituation_times <- initial_looking_time * exp(-decay_rate * (1:n_trials))
deviant_time <- 10000 * decay_rate + rnorm(1, 0, 500) # Linearly related to the decay slope
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(data, subject_data)
}
return(data)
}
# Generate dataset
fake_data_slope <- generate_fake_data(n_subjects = 50)habituation_data <- fake_data_slope %>%
# fist block
# before the dishabituation trials
filter(trial_number < deviant_position) %>%
# only keeping the relevant information
select(subject, trial_number, trial_looking_time)
adult_slope_df <- get_individual_slope(habituation_data)
adult_dishab_raw <- fake_data_slope %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_slope %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(dishab_diff = log(dishab_raw) - log(trial_looking_time)) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff, dishab_trial_num)3.1. relationships
Tests correlations between first/last trials and dishabituation time
3.1.1 first trial vs dishab
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_slope %>% filter(trial_number == 1),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "dishab raw ~ slope",
subtitle = "dishab raw ~ first trial"
)## `geom_smooth()` using formula = 'y ~ x'
3.1.2 last trial vs dishab raw
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_slope %>% group_by(subject) %>% filter(trial_number == (max(trial_number) -1)),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "dishab raw ~ slope",
subtitle = "dishab raw ~ last trial"
)## `geom_smooth()` using formula = 'y ~ x'
3.2 slope?!?
decay rate and dishabituation difference
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "dishab raw ~ slope",
subtitle = "dishab diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
4. non-confounding relationship
Now let’s create a dataset that the dishabituation time is arbitrarily high
# Set seed for reproducibility
set.seed(123)
# Generate synthetic data
generate_fake_data <- function(n_subjects = 50) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(4, 6), 1)
trial_number <- 1:(n_trials + 1)
# Simulate exponential drop in habituation times with varying slopes
initial_looking_time <- runif(1, 5000, 10000)
decay_rate <- runif(1, 0.2, 0.5) # Randomly vary decay rate
habituation_times <- initial_looking_time * exp(-decay_rate * (1:n_trials))
deviant_time <- runif(1, 10000, 15000) # Arbitrarily high but independent of previous trials
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(data, subject_data)
}
return(data)
}
# Generate dataset
fake_data_nr <- generate_fake_data(n_subjects = 50)habituation_data <- fake_data_nr %>%
# fist block
# before the dishabituation trials
filter(trial_number < deviant_position) %>%
# only keeping the relevant information
select(subject, trial_number, trial_looking_time)
adult_slope_df <- get_individual_slope(habituation_data)
adult_dishab_raw <- fake_data_nr %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_nr %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(dishab_diff = log(dishab_raw) - log(trial_looking_time)) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff, dishab_trial_num)4.1. relationships
4.1.1 first trial vs dishab
check the relationship between first trial and dishabituation
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_nr %>% filter(trial_number == 1),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab raw ~ first trial"
)## `geom_smooth()` using formula = 'y ~ x'
4.1.2 last trial vs dishab
check the relationship between last trial and dishabituation
adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_nr %>% group_by(subject) %>% filter(trial_number == (max(trial_number) -1)),
by = c("subject", "block_number")
) %>%
ggplot(aes(x = trial_looking_time, y = dishab_raw)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab raw ~ last trial"
)## `geom_smooth()` using formula = 'y ~ x'
4.2 slope?!?
yet still there exists a relationship between the slope and the dishabituation difference
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
4.3 raw data
weird, what does it look like?
fake_data_nr %>%
ggplot(aes(x = trial_number, y = trial_looking_time)) +
geom_point(position = position_jitter(width = .2)) +
facet_wrap(~deviant_position)
4.4. why the relationship?
The last habituation trial value depends inversely on the decay rate (higher decay → smaller last trial values).
The dishabituation difference is computed as a logarithmic ratio involving the last trial time. When t_last is smaller (due to a higher decay rate), the log difference grows larger, creating a spurious correlation.
# Generate synthetic data
generate_fake_data <- function(n_subjects = 50) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(4, 6), 1)
trial_number <- 1:(n_trials + 1)
# Simulate exponential drop in habituation times with varying slopes
initial_looking_time <- runif(1, 5000, 10000)
decay_rate <- runif(1, 0.2, 0.5) # Randomly vary decay rate
habituation_times <- initial_looking_time * exp(-decay_rate * (1:n_trials))
# Make the deviant trial completely independent by adding a large random value
deviant_time <- runif(1, 15000, 20000) # Arbitrarily high but independent of previous trials
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
decay_rate = decay_rate, # Include decay rate for analysis
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(data, subject_data)
}
return(data)
}
# Generate dataset
fake_data <- generate_fake_data(n_subjects = 50)
# Visualization: Relationship between decay rate and last trial time
last_trial_data <- fake_data %>%
group_by(subject) %>%
filter(trial_number == max(trial_number) - 1) # Select last habituation trial
# Plot decay rate vs last trial looking time
ggplot(last_trial_data, aes(x = decay_rate, y = trial_looking_time)) +
geom_point() +
geom_smooth(method = 'lm') +
labs(
title = 'Decay Rate vs Last Habituation Trial Time',
x = 'Decay Rate',
y = 'Last Trial Looking Time'
)## `geom_smooth()` using formula = 'y ~ x'
4.5 try normalizing by the first trial???
still doesn’t work. even though the deviant time is independent, the first trial indirectly scales the entire decay trajectory. Here’s the subtle issue:
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_nr %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
left_join(
fake_data_nr %>% group_by(subject, block_number) %>% filter(trial_number == 1) %>% rename(first_trial_looking_time = trial_looking_time) %>% select(subject, block_number, first_trial_looking_time)
,
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(
dishab_diff = log(dishab_raw) - log(trial_looking_time),
dishab_normalized = (dishab_raw / first_trial_looking_time)) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff, dishab_normalized, dishab_trial_num, first_trial_looking_time)
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_normalized)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
4.6 try modeling
model_d <- adult_dishab_d %>%
left_join(adult_slope_df)## Joining with `by = join_by(subject)`lm(
dishab_diff ~ decay_rate,
data = model_d
) %>% summary()##
## Call:
## lm(formula = dishab_diff ~ decay_rate, data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73879 -0.27604 0.00625 0.29323 0.70181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7079 0.1729 4.094 0.000162 ***
## decay_rate -4.3211 0.4526 -9.547 1.13e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3662 on 48 degrees of freedom
## Multiple R-squared: 0.655, Adjusted R-squared: 0.6479
## F-statistic: 91.15 on 1 and 48 DF, p-value: 1.129e-12lm(
dishab_diff ~ decay_rate + first_trial_looking_time,
data = model_d
) %>% summary()##
## Call:
## lm(formula = dishab_diff ~ decay_rate + first_trial_looking_time,
## data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.73373 -0.27297 0.06726 0.29824 0.64478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.849068 0.914957 -0.928 0.3582
## decay_rate -5.909253 1.018758 -5.800 5.39e-07 ***
## first_trial_looking_time 0.000187 0.000108 1.732 0.0899 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3588 on 47 degrees of freedom
## Multiple R-squared: 0.6757, Adjusted R-squared: 0.6619
## F-statistic: 48.97 on 2 and 47 DF, p-value: 3.207e-12lm(
dishab_diff ~ first_trial_looking_time,
data = model_d
) %>%
summary()##
## Call:
## lm(formula = dishab_diff ~ first_trial_looking_time, data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.79337 -0.37262 -0.04103 0.44039 0.92881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.254e+00 3.254e-01 13.074 < 2e-16 ***
## first_trial_looking_time -3.769e-04 6.092e-05 -6.186 1.3e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4651 on 48 degrees of freedom
## Multiple R-squared: 0.4436, Adjusted R-squared: 0.432
## F-statistic: 38.27 on 1 and 48 DF, p-value: 1.301e-075. REAL non-confounding!!
now let’s create another non-confounding dataset?!?
# Generate synthetic data
generate_fake_data <- function(n_subjects = 50) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(4, 6), 1)
trial_number <- 1:(n_trials + 1)
# Generate habituation times that always go down
initial_looking_time <- runif(1, 5000, 10000)
habituation_times <- sort(runif(n_trials, 500, 5000), decreasing = TRUE)
# Make sure the values never go below zero
habituation_times <- pmax(habituation_times, 500)
# Generate deviant trial looking time that is higher than the last habituation time
deviant_time <-runif(1, 20000, 30000)
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(
data, subject_data)
}
return(data)
}
# Generate dataset
fake_data_rnr <- generate_fake_data(n_subjects = 50)habituation_data <- fake_data_rnr %>%
# fist block
# before the dishabituation trials
filter(trial_number < deviant_position) %>%
# only keeping the relevant information
select(subject, trial_number, trial_looking_time)
adult_slope_df <- get_individual_slope(habituation_data)
adult_dishab_raw <- fake_data_rnr %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, trial_looking_time) %>%
rename(dishab_raw = trial_looking_time) %>%
left_join(
fake_data_rnr %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
left_join(
fake_data_rnr %>% group_by(subject, block_number) %>% filter(trial_number == 1) %>% rename(first_trial_looking_time = trial_looking_time) %>% select(subject, block_number, first_trial_looking_time)
,
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(
dishab_log_diff = log(dishab_raw) - log(trial_looking_time),
dishab_diff = dishab_raw - trial_looking_time) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff, dishab_log_diff, first_trial_looking_time, dishab_trial_num)5.1. slope?
check the slope….
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_log_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab log diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
5.2 try modeling??
model_d <- adult_dishab_d %>%
left_join(adult_slope_df)## Joining with `by = join_by(subject)`lm(
dishab_diff ~ decay_rate,
data = model_d
) %>% summary()##
## Call:
## lm(formula = dishab_diff ~ decay_rate, data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5496.0 -2082.5 151.5 1854.0 4424.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 22448 1511 14.86 <2e-16 ***
## decay_rate -5564 5058 -1.10 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2588 on 48 degrees of freedom
## Multiple R-squared: 0.02459, Adjusted R-squared: 0.004271
## F-statistic: 1.21 on 1 and 48 DF, p-value: 0.2768lm(
dishab_log_diff ~ decay_rate,
data = model_d
) %>% summary()##
## Call:
## lm(formula = dishab_log_diff ~ decay_rate, data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.90860 -0.41262 0.00744 0.35261 0.82718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.1680 0.2552 8.494 3.99e-11 ***
## decay_rate -3.0501 0.8546 -3.569 0.000826 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4373 on 48 degrees of freedom
## Multiple R-squared: 0.2097, Adjusted R-squared: 0.1932
## F-statistic: 12.74 on 1 and 48 DF, p-value: 0.0008257lm(
dishab_diff ~ decay_rate + first_trial_looking_time,
data = model_d
) %>% summary()##
## Call:
## lm(formula = dishab_diff ~ decay_rate + first_trial_looking_time,
## data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5728.1 -1794.2 205.1 1862.4 4758.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18422.3281 4767.7428 3.864 0.000341 ***
## decay_rate -8631.9034 6128.8893 -1.408 0.165595
## first_trial_looking_time 0.7177 0.8060 0.890 0.377772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2594 on 47 degrees of freedom
## Multiple R-squared: 0.04077, Adjusted R-squared: -4.504e-05
## F-statistic: 0.9989 on 2 and 47 DF, p-value: 0.376lm(
dishab_diff ~ first_trial_looking_time,
data = model_d
) %>%
summary()##
## Call:
## lm(formula = dishab_diff ~ first_trial_looking_time, data = model_d)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6383.3 -1824.6 263.6 1580.0 5179.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.371e+04 2.966e+03 7.995 2.24e-10 ***
## first_trial_looking_time 7.946e-02 6.733e-01 0.118 0.907
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2620 on 48 degrees of freedom
## Multiple R-squared: 0.0002901, Adjusted R-squared: -0.02054
## F-statistic: 0.01393 on 1 and 48 DF, p-value: 0.90656. Try control by residualized
fake_data_rnr## subject block_number task_type deviant_position trial_number trial_type
## 1 S001 1 curiosity 5 1 background
## 2 S001 1 curiosity 5 2 background
## 3 S001 1 curiosity 5 3 background
## 4 S001 1 curiosity 5 4 background
## 5 S001 1 curiosity 5 5 deviant
## 6 S002 1 curiosity 5 1 background
## 7 S002 1 curiosity 5 2 background
## 8 S002 1 curiosity 5 3 background
## 9 S002 1 curiosity 5 4 background
## 10 S002 1 curiosity 5 5 deviant
## 11 S003 1 curiosity 5 1 background
## 12 S003 1 curiosity 5 2 background
## 13 S003 1 curiosity 5 3 background
## 14 S003 1 curiosity 5 4 background
## 15 S003 1 curiosity 5 5 deviant
## 16 S004 1 curiosity 5 1 background
## 17 S004 1 curiosity 5 2 background
## 18 S004 1 curiosity 5 3 background
## 19 S004 1 curiosity 5 4 background
## 20 S004 1 curiosity 5 5 deviant
## 21 S005 1 curiosity 5 1 background
## 22 S005 1 curiosity 5 2 background
## 23 S005 1 curiosity 5 3 background
## 24 S005 1 curiosity 5 4 background
## 25 S005 1 curiosity 5 5 deviant
## 26 S006 1 curiosity 5 1 background
## 27 S006 1 curiosity 5 2 background
## 28 S006 1 curiosity 5 3 background
## 29 S006 1 curiosity 5 4 background
## 30 S006 1 curiosity 5 5 deviant
## 31 S007 1 curiosity 5 1 background
## 32 S007 1 curiosity 5 2 background
## 33 S007 1 curiosity 5 3 background
## 34 S007 1 curiosity 5 4 background
## 35 S007 1 curiosity 5 5 deviant
## 36 S008 1 curiosity 7 1 background
## 37 S008 1 curiosity 7 2 background
## 38 S008 1 curiosity 7 3 background
## 39 S008 1 curiosity 7 4 background
## 40 S008 1 curiosity 7 5 background
## 41 S008 1 curiosity 7 6 background
## 42 S008 1 curiosity 7 7 deviant
## 43 S009 1 curiosity 7 1 background
## 44 S009 1 curiosity 7 2 background
## 45 S009 1 curiosity 7 3 background
## 46 S009 1 curiosity 7 4 background
## 47 S009 1 curiosity 7 5 background
## 48 S009 1 curiosity 7 6 background
## 49 S009 1 curiosity 7 7 deviant
## 50 S010 1 curiosity 5 1 background
## 51 S010 1 curiosity 5 2 background
## 52 S010 1 curiosity 5 3 background
## 53 S010 1 curiosity 5 4 background
## 54 S010 1 curiosity 5 5 deviant
## 55 S011 1 curiosity 7 1 background
## 56 S011 1 curiosity 7 2 background
## 57 S011 1 curiosity 7 3 background
## 58 S011 1 curiosity 7 4 background
## 59 S011 1 curiosity 7 5 background
## 60 S011 1 curiosity 7 6 background
## 61 S011 1 curiosity 7 7 deviant
## 62 S012 1 curiosity 5 1 background
## 63 S012 1 curiosity 5 2 background
## 64 S012 1 curiosity 5 3 background
## 65 S012 1 curiosity 5 4 background
## 66 S012 1 curiosity 5 5 deviant
## 67 S013 1 curiosity 5 1 background
## 68 S013 1 curiosity 5 2 background
## 69 S013 1 curiosity 5 3 background
## 70 S013 1 curiosity 5 4 background
## 71 S013 1 curiosity 5 5 deviant
## 72 S014 1 curiosity 7 1 background
## 73 S014 1 curiosity 7 2 background
## 74 S014 1 curiosity 7 3 background
## 75 S014 1 curiosity 7 4 background
## 76 S014 1 curiosity 7 5 background
## 77 S014 1 curiosity 7 6 background
## 78 S014 1 curiosity 7 7 deviant
## 79 S015 1 curiosity 5 1 background
## 80 S015 1 curiosity 5 2 background
## 81 S015 1 curiosity 5 3 background
## 82 S015 1 curiosity 5 4 background
## 83 S015 1 curiosity 5 5 deviant
## 84 S016 1 curiosity 5 1 background
## 85 S016 1 curiosity 5 2 background
## 86 S016 1 curiosity 5 3 background
## 87 S016 1 curiosity 5 4 background
## 88 S016 1 curiosity 5 5 deviant
## 89 S017 1 curiosity 7 1 background
## 90 S017 1 curiosity 7 2 background
## 91 S017 1 curiosity 7 3 background
## 92 S017 1 curiosity 7 4 background
## 93 S017 1 curiosity 7 5 background
## 94 S017 1 curiosity 7 6 background
## 95 S017 1 curiosity 7 7 deviant
## 96 S018 1 curiosity 5 1 background
## 97 S018 1 curiosity 5 2 background
## 98 S018 1 curiosity 5 3 background
## 99 S018 1 curiosity 5 4 background
## 100 S018 1 curiosity 5 5 deviant
## 101 S019 1 curiosity 5 1 background
## 102 S019 1 curiosity 5 2 background
## 103 S019 1 curiosity 5 3 background
## 104 S019 1 curiosity 5 4 background
## 105 S019 1 curiosity 5 5 deviant
## 106 S020 1 curiosity 5 1 background
## 107 S020 1 curiosity 5 2 background
## 108 S020 1 curiosity 5 3 background
## 109 S020 1 curiosity 5 4 background
## 110 S020 1 curiosity 5 5 deviant
## 111 S021 1 curiosity 5 1 background
## 112 S021 1 curiosity 5 2 background
## 113 S021 1 curiosity 5 3 background
## 114 S021 1 curiosity 5 4 background
## 115 S021 1 curiosity 5 5 deviant
## 116 S022 1 curiosity 7 1 background
## 117 S022 1 curiosity 7 2 background
## 118 S022 1 curiosity 7 3 background
## 119 S022 1 curiosity 7 4 background
## 120 S022 1 curiosity 7 5 background
## 121 S022 1 curiosity 7 6 background
## 122 S022 1 curiosity 7 7 deviant
## 123 S023 1 curiosity 5 1 background
## 124 S023 1 curiosity 5 2 background
## 125 S023 1 curiosity 5 3 background
## 126 S023 1 curiosity 5 4 background
## 127 S023 1 curiosity 5 5 deviant
## 128 S024 1 curiosity 5 1 background
## 129 S024 1 curiosity 5 2 background
## 130 S024 1 curiosity 5 3 background
## 131 S024 1 curiosity 5 4 background
## 132 S024 1 curiosity 5 5 deviant
## 133 S025 1 curiosity 7 1 background
## 134 S025 1 curiosity 7 2 background
## 135 S025 1 curiosity 7 3 background
## 136 S025 1 curiosity 7 4 background
## 137 S025 1 curiosity 7 5 background
## 138 S025 1 curiosity 7 6 background
## 139 S025 1 curiosity 7 7 deviant
## 140 S026 1 curiosity 5 1 background
## 141 S026 1 curiosity 5 2 background
## 142 S026 1 curiosity 5 3 background
## 143 S026 1 curiosity 5 4 background
## 144 S026 1 curiosity 5 5 deviant
## 145 S027 1 curiosity 7 1 background
## 146 S027 1 curiosity 7 2 background
## 147 S027 1 curiosity 7 3 background
## 148 S027 1 curiosity 7 4 background
## 149 S027 1 curiosity 7 5 background
## 150 S027 1 curiosity 7 6 background
## 151 S027 1 curiosity 7 7 deviant
## 152 S028 1 curiosity 7 1 background
## 153 S028 1 curiosity 7 2 background
## 154 S028 1 curiosity 7 3 background
## 155 S028 1 curiosity 7 4 background
## 156 S028 1 curiosity 7 5 background
## 157 S028 1 curiosity 7 6 background
## 158 S028 1 curiosity 7 7 deviant
## 159 S029 1 curiosity 5 1 background
## 160 S029 1 curiosity 5 2 background
## 161 S029 1 curiosity 5 3 background
## 162 S029 1 curiosity 5 4 background
## 163 S029 1 curiosity 5 5 deviant
## 164 S030 1 curiosity 7 1 background
## 165 S030 1 curiosity 7 2 background
## 166 S030 1 curiosity 7 3 background
## 167 S030 1 curiosity 7 4 background
## 168 S030 1 curiosity 7 5 background
## 169 S030 1 curiosity 7 6 background
## 170 S030 1 curiosity 7 7 deviant
## 171 S031 1 curiosity 5 1 background
## 172 S031 1 curiosity 5 2 background
## 173 S031 1 curiosity 5 3 background
## 174 S031 1 curiosity 5 4 background
## 175 S031 1 curiosity 5 5 deviant
## 176 S032 1 curiosity 5 1 background
## 177 S032 1 curiosity 5 2 background
## 178 S032 1 curiosity 5 3 background
## 179 S032 1 curiosity 5 4 background
## 180 S032 1 curiosity 5 5 deviant
## 181 S033 1 curiosity 7 1 background
## 182 S033 1 curiosity 7 2 background
## 183 S033 1 curiosity 7 3 background
## 184 S033 1 curiosity 7 4 background
## 185 S033 1 curiosity 7 5 background
## 186 S033 1 curiosity 7 6 background
## 187 S033 1 curiosity 7 7 deviant
## 188 S034 1 curiosity 5 1 background
## 189 S034 1 curiosity 5 2 background
## 190 S034 1 curiosity 5 3 background
## 191 S034 1 curiosity 5 4 background
## 192 S034 1 curiosity 5 5 deviant
## 193 S035 1 curiosity 5 1 background
## 194 S035 1 curiosity 5 2 background
## 195 S035 1 curiosity 5 3 background
## 196 S035 1 curiosity 5 4 background
## 197 S035 1 curiosity 5 5 deviant
## 198 S036 1 curiosity 7 1 background
## 199 S036 1 curiosity 7 2 background
## 200 S036 1 curiosity 7 3 background
## 201 S036 1 curiosity 7 4 background
## 202 S036 1 curiosity 7 5 background
## 203 S036 1 curiosity 7 6 background
## 204 S036 1 curiosity 7 7 deviant
## 205 S037 1 curiosity 7 1 background
## 206 S037 1 curiosity 7 2 background
## 207 S037 1 curiosity 7 3 background
## 208 S037 1 curiosity 7 4 background
## 209 S037 1 curiosity 7 5 background
## 210 S037 1 curiosity 7 6 background
## 211 S037 1 curiosity 7 7 deviant
## 212 S038 1 curiosity 7 1 background
## 213 S038 1 curiosity 7 2 background
## 214 S038 1 curiosity 7 3 background
## 215 S038 1 curiosity 7 4 background
## 216 S038 1 curiosity 7 5 background
## 217 S038 1 curiosity 7 6 background
## 218 S038 1 curiosity 7 7 deviant
## 219 S039 1 curiosity 7 1 background
## 220 S039 1 curiosity 7 2 background
## 221 S039 1 curiosity 7 3 background
## 222 S039 1 curiosity 7 4 background
## 223 S039 1 curiosity 7 5 background
## 224 S039 1 curiosity 7 6 background
## 225 S039 1 curiosity 7 7 deviant
## 226 S040 1 curiosity 7 1 background
## 227 S040 1 curiosity 7 2 background
## 228 S040 1 curiosity 7 3 background
## 229 S040 1 curiosity 7 4 background
## 230 S040 1 curiosity 7 5 background
## 231 S040 1 curiosity 7 6 background
## 232 S040 1 curiosity 7 7 deviant
## 233 S041 1 curiosity 7 1 background
## 234 S041 1 curiosity 7 2 background
## 235 S041 1 curiosity 7 3 background
## 236 S041 1 curiosity 7 4 background
## 237 S041 1 curiosity 7 5 background
## 238 S041 1 curiosity 7 6 background
## 239 S041 1 curiosity 7 7 deviant
## 240 S042 1 curiosity 7 1 background
## 241 S042 1 curiosity 7 2 background
## 242 S042 1 curiosity 7 3 background
## 243 S042 1 curiosity 7 4 background
## 244 S042 1 curiosity 7 5 background
## 245 S042 1 curiosity 7 6 background
## 246 S042 1 curiosity 7 7 deviant
## 247 S043 1 curiosity 5 1 background
## 248 S043 1 curiosity 5 2 background
## 249 S043 1 curiosity 5 3 background
## 250 S043 1 curiosity 5 4 background
## 251 S043 1 curiosity 5 5 deviant
## 252 S044 1 curiosity 5 1 background
## 253 S044 1 curiosity 5 2 background
## 254 S044 1 curiosity 5 3 background
## 255 S044 1 curiosity 5 4 background
## 256 S044 1 curiosity 5 5 deviant
## 257 S045 1 curiosity 7 1 background
## 258 S045 1 curiosity 7 2 background
## 259 S045 1 curiosity 7 3 background
## 260 S045 1 curiosity 7 4 background
## 261 S045 1 curiosity 7 5 background
## 262 S045 1 curiosity 7 6 background
## 263 S045 1 curiosity 7 7 deviant
## 264 S046 1 curiosity 5 1 background
## 265 S046 1 curiosity 5 2 background
## 266 S046 1 curiosity 5 3 background
## 267 S046 1 curiosity 5 4 background
## 268 S046 1 curiosity 5 5 deviant
## 269 S047 1 curiosity 7 1 background
## 270 S047 1 curiosity 7 2 background
## 271 S047 1 curiosity 7 3 background
## 272 S047 1 curiosity 7 4 background
## 273 S047 1 curiosity 7 5 background
## 274 S047 1 curiosity 7 6 background
## 275 S047 1 curiosity 7 7 deviant
## 276 S048 1 curiosity 5 1 background
## 277 S048 1 curiosity 5 2 background
## 278 S048 1 curiosity 5 3 background
## 279 S048 1 curiosity 5 4 background
## 280 S048 1 curiosity 5 5 deviant
## 281 S049 1 curiosity 5 1 background
## 282 S049 1 curiosity 5 2 background
## 283 S049 1 curiosity 5 3 background
## 284 S049 1 curiosity 5 4 background
## 285 S049 1 curiosity 5 5 deviant
## 286 S050 1 curiosity 5 1 background
## 287 S050 1 curiosity 5 2 background
## 288 S050 1 curiosity 5 3 background
## 289 S050 1 curiosity 5 4 background
## 290 S050 1 curiosity 5 5 deviant
## trial_looking_time stimulus_displayed task_question_type
## 1 4530.5691 images/stimuli/10_A_a.gif curiosity_background
## 2 3711.8192 images/stimuli/7_A_a.gif curiosity_background
## 3 2973.8823 images/stimuli/16_A_a.gif curiosity_background
## 4 1579.9735 images/stimuli/2_A_a.gif curiosity_background
## 5 24826.2379 images/stimuli/10_A_a.gif curiosity_background
## 6 4744.5113 images/stimuli/7_A_a.gif curiosity_background
## 7 2185.4554 images/stimuli/2_A_a.gif curiosity_background
## 8 562.7376 images/stimuli/14_A_a.gif curiosity_background
## 9 536.1869 images/stimuli/4_A_a.gif curiosity_background
## 10 29805.2450 images/stimuli/17_A_a.gif curiosity_background
## 11 4622.7696 images/stimuli/14_A_a.gif curiosity_background
## 12 3795.4479 images/stimuli/17_A_a.gif curiosity_background
## 13 3243.6982 images/stimuli/13_A_a.gif curiosity_background
## 14 1509.6746 images/stimuli/9_A_a.gif curiosity_background
## 15 28027.6102 images/stimuli/15_A_a.gif curiosity_background
## 16 4306.6773 images/stimuli/5_A_a.gif curiosity_background
## 17 3876.6916 images/stimuli/14_A_a.gif curiosity_background
## 18 2506.3544 images/stimuli/17_A_a.gif curiosity_background
## 19 991.7055 images/stimuli/2_A_a.gif curiosity_background
## 20 27955.3862 images/stimuli/19_A_a.gif curiosity_background
## 21 4118.9822 images/stimuli/6_A_a.gif curiosity_background
## 22 3110.9828 images/stimuli/12_A_a.gif curiosity_background
## 23 2066.1504 images/stimuli/19_A_a.gif curiosity_background
## 24 749.3667 images/stimuli/12_A_a.gif curiosity_background
## 25 23402.6573 images/stimuli/3_A_a.gif curiosity_background
## 26 2386.5477 images/stimuli/1_A_a.gif curiosity_background
## 27 1280.8042 images/stimuli/8_A_a.gif curiosity_background
## 28 713.0225 images/stimuli/1_A_a.gif curiosity_background
## 29 619.3817 images/stimuli/8_A_a.gif curiosity_background
## 30 23780.8653 images/stimuli/12_A_a.gif curiosity_background
## 31 3799.9701 images/stimuli/9_A_a.gif curiosity_background
## 32 3504.7307 images/stimuli/5_A_a.gif curiosity_background
## 33 3497.0298 images/stimuli/6_A_a.gif curiosity_background
## 34 1916.3316 images/stimuli/18_A_a.gif curiosity_background
## 35 24638.8671 images/stimuli/17_A_a.gif curiosity_background
## 36 4832.2214 images/stimuli/16_A_a.gif curiosity_background
## 37 4773.7580 images/stimuli/9_A_a.gif curiosity_background
## 38 4352.5087 images/stimuli/15_A_a.gif curiosity_background
## 39 2882.0080 images/stimuli/13_A_a.gif curiosity_background
## 40 2197.9113 images/stimuli/18_A_a.gif curiosity_background
## 41 848.9973 images/stimuli/15_A_a.gif curiosity_background
## 42 25494.3614 images/stimuli/12_A_a.gif curiosity_background
## 43 4222.4763 images/stimuli/11_A_a.gif curiosity_background
## 44 3033.9315 images/stimuli/11_A_a.gif curiosity_background
## 45 2623.7608 images/stimuli/6_A_a.gif curiosity_background
## 46 2554.1397 images/stimuli/4_A_a.gif curiosity_background
## 47 1546.2511 images/stimuli/15_A_a.gif curiosity_background
## 48 1365.5688 images/stimuli/17_A_a.gif curiosity_background
## 49 23140.7971 images/stimuli/8_A_a.gif curiosity_background
## 50 4695.3752 images/stimuli/7_A_a.gif curiosity_background
## 51 4190.1115 images/stimuli/4_A_a.gif curiosity_background
## 52 2898.8737 images/stimuli/10_A_a.gif curiosity_background
## 53 2230.1104 images/stimuli/16_A_a.gif curiosity_background
## 54 25459.8831 images/stimuli/16_A_a.gif curiosity_background
## 55 3831.1758 images/stimuli/10_A_a.gif curiosity_background
## 56 3139.1844 images/stimuli/5_A_a.gif curiosity_background
## 57 2942.1443 images/stimuli/12_A_a.gif curiosity_background
## 58 2749.3094 images/stimuli/4_A_a.gif curiosity_background
## 59 2310.3033 images/stimuli/3_A_a.gif curiosity_background
## 60 1276.5654 images/stimuli/7_A_a.gif curiosity_background
## 61 29363.5857 images/stimuli/11_A_a.gif curiosity_background
## 62 4998.4296 images/stimuli/10_A_a.gif curiosity_background
## 63 4871.4266 images/stimuli/2_A_a.gif curiosity_background
## 64 4098.8199 images/stimuli/3_A_a.gif curiosity_background
## 65 1297.9073 images/stimuli/13_A_a.gif curiosity_background
## 66 22607.4345 images/stimuli/7_A_a.gif curiosity_background
## 67 3579.5955 images/stimuli/9_A_a.gif curiosity_background
## 68 2939.7283 images/stimuli/4_A_a.gif curiosity_background
## 69 2548.3545 images/stimuli/20_A_a.gif curiosity_background
## 70 2368.3597 images/stimuli/15_A_a.gif curiosity_background
## 71 21270.6444 images/stimuli/17_A_a.gif curiosity_background
## 72 4949.0101 images/stimuli/12_A_a.gif curiosity_background
## 73 4731.1150 images/stimuli/13_A_a.gif curiosity_background
## 74 3781.8948 images/stimuli/16_A_a.gif curiosity_background
## 75 1761.1842 images/stimuli/10_A_a.gif curiosity_background
## 76 1475.5320 images/stimuli/5_A_a.gif curiosity_background
## 77 789.3454 images/stimuli/15_A_a.gif curiosity_background
## 78 22102.0848 images/stimuli/6_A_a.gif curiosity_background
## 79 4898.9710 images/stimuli/10_A_a.gif curiosity_background
## 80 2747.1365 images/stimuli/20_A_a.gif curiosity_background
## 81 2743.5725 images/stimuli/15_A_a.gif curiosity_background
## 82 2376.9576 images/stimuli/7_A_a.gif curiosity_background
## 83 23434.7225 images/stimuli/16_A_a.gif curiosity_background
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## 290 25633.1149 images/stimuli/7_A_a.gif curiosity_background
## task_question_response
## 1 3
## 2 2
## 3 5
## 4 1
## 5 1
## 6 3
## 7 3
## 8 4
## 9 2
## 10 2
## 11 4
## 12 3
## 13 2
## 14 2
## 15 4
## 16 5
## 17 3
## 18 1
## 19 2
## 20 5
## 21 4
## 22 5
## 23 2
## 24 5
## 25 5
## 26 2
## 27 2
## 28 4
## 29 2
## 30 2
## 31 3
## 32 4
## 33 2
## 34 2
## 35 5
## 36 5
## 37 1
## 38 5
## 39 3
## 40 5
## 41 2
## 42 1
## 43 1
## 44 3
## 45 2
## 46 1
## 47 4
## 48 3
## 49 2
## 50 3
## 51 3
## 52 3
## 53 5
## 54 3
## 55 2
## 56 1
## 57 5
## 58 3
## 59 3
## 60 5
## 61 5
## 62 5
## 63 4
## 64 5
## 65 2
## 66 1
## 67 3
## 68 2
## 69 3
## 70 3
## 71 5
## 72 5
## 73 1
## 74 2
## 75 5
## 76 3
## 77 4
## 78 5
## 79 4
## 80 4
## 81 1
## 82 4
## 83 3
## 84 3
## 85 1
## 86 4
## 87 5
## 88 4
## 89 3
## 90 1
## 91 3
## 92 5
## 93 5
## 94 2
## 95 5
## 96 5
## 97 3
## 98 5
## 99 1
## 100 1
## 101 5
## 102 1
## 103 2
## 104 3
## 105 3
## 106 5
## 107 1
## 108 1
## 109 2
## 110 5
## 111 2
## 112 5
## 113 4
## 114 1
## 115 5
## 116 4
## 117 3
## 118 1
## 119 5
## 120 2
## 121 1
## 122 4
## 123 5
## 124 2
## 125 4
## 126 2
## 127 1
## 128 5
## 129 3
## 130 3
## 131 2
## 132 5
## 133 3
## 134 2
## 135 3
## 136 5
## 137 3
## 138 5
## 139 4
## 140 2
## 141 3
## 142 4
## 143 2
## 144 3
## 145 3
## 146 3
## 147 5
## 148 1
## 149 3
## 150 1
## 151 5
## 152 5
## 153 1
## 154 5
## 155 3
## 156 2
## 157 5
## 158 3
## 159 2
## 160 4
## 161 5
## 162 2
## 163 3
## 164 2
## 165 3
## 166 1
## 167 4
## 168 4
## 169 2
## 170 3
## 171 5
## 172 1
## 173 1
## 174 4
## 175 3
## 176 4
## 177 3
## 178 5
## 179 4
## 180 2
## 181 4
## 182 3
## 183 5
## 184 3
## 185 5
## 186 3
## 187 4
## 188 4
## 189 3
## 190 5
## 191 2
## 192 4
## 193 3
## 194 4
## 195 3
## 196 1
## 197 4
## 198 1
## 199 1
## 200 4
## 201 3
## 202 2
## 203 4
## 204 5
## 205 2
## 206 3
## 207 4
## 208 4
## 209 4
## 210 2
## 211 2
## 212 2
## 213 4
## 214 1
## 215 3
## 216 3
## 217 3
## 218 2
## 219 5
## 220 4
## 221 2
## 222 1
## 223 5
## 224 5
## 225 3
## 226 2
## 227 5
## 228 1
## 229 3
## 230 3
## 231 4
## 232 5
## 233 2
## 234 2
## 235 5
## 236 1
## 237 2
## 238 3
## 239 4
## 240 1
## 241 3
## 242 2
## 243 1
## 244 1
## 245 2
## 246 5
## 247 4
## 248 2
## 249 1
## 250 2
## 251 1
## 252 4
## 253 5
## 254 4
## 255 5
## 256 4
## 257 4
## 258 4
## 259 1
## 260 2
## 261 4
## 262 2
## 263 2
## 264 3
## 265 2
## 266 3
## 267 1
## 268 4
## 269 1
## 270 3
## 271 4
## 272 3
## 273 2
## 274 4
## 275 3
## 276 5
## 277 5
## 278 5
## 279 4
## 280 4
## 281 5
## 282 5
## 283 3
## 284 2
## 285 2
## 286 5
## 287 2
## 288 4
## 289 3
## 290 5fake_data_rnr$trial_type_numeric <- as.numeric(as.factor(fake_data_rnr$trial_type))
# Define the nonlinear model
exp_decay <- function(trial_number, trial_type_numeric, a, b) {
a * exp(b * trial_number * trial_type_numeric)
}
# Fit the model
habituation_model <- nlme(
trial_looking_time ~ exp_decay(trial_number, trial_type_numeric, a, b),
data = fake_data_rnr,
fixed = a + b ~ 1,
random = b ~ 1 + trial_number | subject, # Add random intercept
start = c(a = mean(fake_data_rnr$trial_looking_time),
b = -0.01), # Adjust starting values
control = nlmeControl(msMaxIter = 200, pnlsTol = 1e-6)
)
fake_data_rnr$resid = resid(habituation_model)
adult_dishab_raw <- fake_data_rnr %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, resid) %>%
rename(dishab_raw = resid) %>%
left_join(
fake_data_rnr %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(
dishab_diff_resid = dishab_raw - resid) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_diff_resid, dishab_trial_num)
adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_diff_resid)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab log diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
6.1. piece-wise combination
change the model by piece wise combination - this works!
# Separate habituation and deviant trials
habituation_data <- fake_data_rnr %>%
filter(trial_type == "background") # Habituation trials only
deviant_data <- fake_data_rnr %>%
filter(trial_type == "deviant") # Deviant trial only
# Nonlinear mixed-effects model for habituation trials
habituation_model <- nlme(
trial_looking_time ~ a * exp(b * trial_number),
data = habituation_data,
fixed = a + b ~ 1, # Fixed effects for decay
random = b ~ 1 | subject, # Random intercept and slope
start = c(a = mean(habituation_data$trial_looking_time), b = -0.1), # Initial guesses for parameters
control = nlmeControl(msMaxIter = 200, pnlsTol = 1e-6) # Controls for stability
)
deviant_model <- lme(
trial_looking_time ~ 1, # Deviant trial modeled as intercept-only
random = ~ 1 | subject, # Random intercept to account for subject variability
data = deviant_data
)
habituation_data$resid_hab <- resid(habituation_model)
deviant_data$resid_dev <- resid(deviant_model)
# Merge the residuals for analysis
combined_residuals <- habituation_data %>%
select(subject, trial_number, resid_hab) %>%
bind_rows(
deviant_data %>%
select(subject, trial_number, resid_dev) %>%
rename(resid_hab = resid_dev)
)
random_effects <- ranef(habituation_model)$b
fixed_decay_rate <- fixef(habituation_model)["b"]
individual_decay_rates <- fixed_decay_rate + random_effects
deviant_resid_data <- deviant_data %>%
mutate(decay_rate = individual_decay_rates)
ggplot(deviant_resid_data, aes(x = decay_rate, y = resid_dev)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Decay Rate vs Deviant Residuals",
x = "Decay Rate",
y = "Deviant Residuals")## `geom_smooth()` using formula = 'y ~ x'
7. do Z score within participant
fake_data_rnr <- fake_data_rnr %>% group_by(subject) %>%
mutate(z_score_looking_time = (trial_looking_time - mean(trial_looking_time)) / sd(trial_looking_time)) %>%
ungroup()
fake_data_rnr## # A tibble: 290 × 13
## subject block_number task_type deviant_position trial_number trial_type
## <chr> <dbl> <chr> <dbl> <int> <chr>
## 1 S001 1 curiosity 5 1 background
## 2 S001 1 curiosity 5 2 background
## 3 S001 1 curiosity 5 3 background
## 4 S001 1 curiosity 5 4 background
## 5 S001 1 curiosity 5 5 deviant
## 6 S002 1 curiosity 5 1 background
## 7 S002 1 curiosity 5 2 background
## 8 S002 1 curiosity 5 3 background
## 9 S002 1 curiosity 5 4 background
## 10 S002 1 curiosity 5 5 deviant
## # ℹ 280 more rows
## # ℹ 7 more variables: trial_looking_time <dbl>, stimulus_displayed <chr>,
## # task_question_type <chr>, task_question_response <int>,
## # trial_type_numeric <dbl>, resid <dbl>, z_score_looking_time <dbl>habituation_data <- fake_data_rnr %>%
# fist block
# before the dishabituation trials
filter(trial_number < deviant_position) %>%
# only keeping the relevant information
select(subject, trial_number, z_score_looking_time) %>%
rename(trial_looking_time = z_score_looking_time)
adult_slope_df <- get_individual_slope(habituation_data)
adult_dishab_raw <- fake_data_rnr %>%
filter(trial_number == deviant_position)
adult_dishab_d <- adult_dishab_raw %>%
select(subject,block_number, z_score_looking_time, trial_looking_time) %>%
rename(dishab_raw = z_score_looking_time) %>%
left_join(
fake_data_rnr %>% filter(trial_number < deviant_position) %>% group_by(subject, block_number) %>% filter(trial_number == max(trial_number)),
by = c("subject", "block_number")
) %>%
# calculating the key
mutate(
dishab_z_score_diff = dishab_raw - z_score_looking_time) %>%
mutate(dishab_trial_num = trial_number + 1) %>%
select(subject, dishab_z_score_diff, dishab_trial_num)adult_dishab_d %>%
left_join(adult_slope_df) %>%
ggplot(aes(x = decay_rate, y = dishab_z_score_diff)) +
geom_point() +
geom_smooth(method = "lm") +
labs(
title = "No relationship",
subtitle = "dishab log diff ~ slope"
)## Joining with `by = join_by(subject)`
## `geom_smooth()` using formula = 'y ~ x'
8. Try to see if the piece-wise destroy the constructed relationship?
no – if there’s a real realtionship it’s still there!
# Generate synthetic data
generate_fake_data <- function(n_subjects = 50) {
# Create subject IDs
subjects <- paste0("S", sprintf("%03d", 1:n_subjects))
# Initialize empty data frame
data <- data.frame()
# Loop through each subject
for (subj in subjects) {
# Randomly assign number of habituation trials (4 or 6)
n_trials <- sample(c(4, 6), 1)
trial_number <- 1:(n_trials + 1)
# Simulate exponential drop in habituation times with varying slopes
initial_looking_time <- runif(1, 5000, 10000)
decay_rate <- runif(1, 0.2, 0.5) # Randomly vary decay rate
habituation_times <- initial_looking_time * exp(-decay_rate * (1:n_trials))
deviant_time <- 10000 * decay_rate + rnorm(1, 0, 500) # Linearly related to the decay slope
# Combine looking times
trial_looking_time <- c(habituation_times, deviant_time)
# Assign trial types
trial_type <- c(rep('background', n_trials), 'deviant')
# Create data frame for subject
subject_data <- data.frame(
subject = subj,
block_number = 1,
task_type = 'curiosity',
deviant_position = n_trials + 1,
trial_number = trial_number,
trial_type = trial_type,
trial_looking_time = trial_looking_time,
stimulus_displayed = paste0('images/stimuli/', sample(1:20, n_trials + 1, replace = TRUE), '_A_a.gif'),
task_question_type = 'curiosity_background',
task_question_response = sample(1:5, n_trials + 1, replace = TRUE)
)
# Append subject data
data <- rbind(data, subject_data)
}
return(data)
}
# Generate dataset
fake_data_slope <- generate_fake_data(n_subjects = 50)# Separate habituation and deviant trials
habituation_data <- fake_data_slope %>%
filter(trial_type == "background") # Habituation trials only
deviant_data <- fake_data_slope %>%
filter(trial_type == "deviant") # Deviant trial only
# Nonlinear mixed-effects model for habituation trials
habituation_model <- nlme(
trial_looking_time ~ a * exp(b * trial_number),
data = habituation_data,
fixed = a + b ~ 1, # Fixed effects for decay
random = b ~ 1 | subject, # Random intercept and slope
start = c(a = mean(habituation_data$trial_looking_time), b = -0.1), # Initial guesses for parameters
control = nlmeControl(msMaxIter = 200, pnlsTol = 1e-6) # Controls for stability
)
deviant_model <- lme(
trial_looking_time ~ 1, # Deviant trial modeled as intercept-only
random = ~ 1 | subject, # Random intercept to account for subject variability
data = deviant_data
)
habituation_data$resid_hab <- resid(habituation_model)
deviant_data$resid_dev <- resid(deviant_model)
# Merge the residuals for analysis
combined_residuals <- habituation_data %>%
select(subject, trial_number, resid_hab) %>%
bind_rows(
deviant_data %>%
select(subject, trial_number, resid_dev) %>%
rename(resid_hab = resid_dev)
)
random_effects <- ranef(habituation_model)$b
fixed_decay_rate <- fixef(habituation_model)["b"]
individual_decay_rates <- fixed_decay_rate + random_effects
deviant_resid_data <- deviant_data %>%
mutate(decay_rate = individual_decay_rates)
ggplot(deviant_resid_data, aes(x = decay_rate, y = resid_dev)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Decay Rate vs Deviant Residuals",
x = "Decay Rate",
y = "Deviant Residuals")## `geom_smooth()` using formula = 'y ~ x'