# Load some libraries
library(tidyverse) # importing, transforming, and visualizing data frames

## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.1.1     ✔ forcats 0.3.0

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library(stringr) # string wrangling
library(ez) # ANOVAs
library(BayesFactor) # Bayesian t-tests

## Loading required package: coda

## Loading required package: Matrix

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## Attaching package: 'Matrix'

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## ************
## Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
## 
## Type BFManual() to open the manual.
## ************

library(nlme) # multilevel models

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## Attaching package: 'nlme'

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library(broom) # transform model output into a data frame
library(knitr) # R notebook output
library(apa) # reporting statistical results in APA format.
library(here) # file paths

## here() starts at /Volumes/psychology$/Researchers/reteig/MFBrain

print(sessionInfo())

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
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## other attached packages:
##  [1] here_0.1               apa_0.3.0              knitr_1.21            
##  [4] broom_0.5.1            nlme_3.1-137           BayesFactor_0.9.12-4.2
##  [7] Matrix_1.2-14          coda_0.19-2            ez_4.4-0              
## [10] forcats_0.3.0          stringr_1.3.1          dplyr_0.7.8           
## [13] purrr_0.2.5            readr_1.1.1            tidyr_0.8.2           
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## [31] withr_2.1.2        pbapply_1.3-4      lazyeval_0.2.1    
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## [37] readxl_1.1.0       evaluate_0.12      MASS_7.3-50       
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## [43] data.table_1.11.8  hms_0.4.2          munsell_0.5.0     
## [46] zip_1.0.0          bindrcpp_0.2.2     compiler_3.5.1    
## [49] rlang_0.3.0.1      grid_3.5.1         nloptr_1.2.1      
## [52] rstudioapi_0.8     rmarkdown_1.11     gtable_0.2.0      
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## [61] bindr_0.1.1        stringi_1.2.4      parallel_3.5.1    
## [64] Rcpp_1.0.0         tidyselect_0.2.5   xfun_0.4

ezANOVA_pes <- function(ez_output) { # add partial eta-squared to output from ezANOVA
  ez_output$ANOVA$pes <- ez_output$ANOVA$SSn/(ez_output$ANOVA$SSn + ez_output$ANOVA$SSd)
  return(ez_output)
}

cohen_d_paired <- function(t_test_output) { # add cohen's d to output from paired t-tests
  t_test_output$d <- abs(t_test_output$statistic) / sqrt(t_test_output$parameter + 1)
  return(t_test_output)
}

# do frequentist and bayesian paired t-tests; print output as table
paired_test_eeg <- function(df, return_t = FALSE, return_BF = FALSE) {
  df %<>%
    filter(time %in% c("60","70")) %>% # keep only data for t-test
    group_by(region) %>% # for each hemisphere
    spread(time,voltage) %>%
    nest() %>% # create a separate data frame of all remaining columns. These data frames are stored in a list-column named "data"
    mutate(stats = map(data, ~t.test(.$`60`, .$`70`, paired = TRUE)),
           BF = map_dbl(data, ~extractBF(ttestBF(.$`60`, .$`70`, paired = TRUE), onlybf = TRUE)))
  return(df)
}

print_paired_test_eeg <- function(df, return_t = FALSE, return_BF = FALSE) {
  if (return_t) {
    df %<>%
      mutate(tidy_model = map(stats, tidy)) %>% # force the four test outputs to also be data frames in a list column ("tidy_model")
      unnest(tidy_model, .drop = TRUE) %>% # unpack the list-column with results, drop the list-column we used to organize the data %>%
      cohen_d_paired()
  } else if (return_BF) {
    df <- select(df, region, BF)
  }
  return(df)
}

Behavioral data per trial

Load

# List of files to load
dataDir <- here("data","behavior")
files <- list.files(dataDir, "_pertrial.txt", full.names = TRUE, recursive = TRUE)

# Function with data operations per file
load_trial_data <- function(filename) {
  per_trial <- read_tsv(filename, col_names = c("block", "trial_per_block", "target_present", "response", "RT", "outcome"), col_types = 'iiiiic') # read the data, name the columns, assign correct classes
  file_sub <- str_extract(filename, "S[0-9]{2}") # extract participant ID

  per_trial <- per_trial %>%
    mutate(participant = rep(file_sub,nrow(.)), # add column with participant ID
           time = rep(seq(8), each = nrow(.)/8)) # make new column to split rows into 8ths (every 10 minutes)
}

# Execute the function for each file; bind into single data frame
per_trial <- map_df(files, load_trial_data)

Clean

participants_to_include <- c("S01","S03","S04","S05","S06","S07","S08","S11","S12","S15","S16","S18","S19","S21","S22","S23","S25","S27","S28","S29","S30")
per_trial <- per_trial %>%
  filter(participant %in% participants_to_include) %>% # exclude participants not in list
  select(participant, time, target_present:outcome) %>% # reorder columns
  mutate(participant = parse_factor(participant,NULL), # create factors for statistical analysis
         time = parse_factor(time,NULL))

per_trial

## # A tibble: 50,400 x 6
##    participant time  target_present response    RT outcome          
##    <fct>       <fct>          <int>    <int> <int> <chr>            
##  1 S01         1                  0        0     0 Correct rejection
##  2 S01         1                  0        0     0 Correct rejection
##  3 S01         1                  0        0     0 Correct rejection
##  4 S01         1                  0        0     0 Correct rejection
##  5 S01         1                  0        0     0 Correct rejection
##  6 S01         1                  0        0     0 Correct rejection
##  7 S01         1                  0        0     0 Correct rejection
##  8 S01         1                  1        1   467 Hit              
##  9 S01         1                  0        0     0 Correct rejection
## 10 S01         1                  0        0     0 Correct rejection
## # ... with 50,390 more rows

For each participant and trial, we now have:

participant: participant ID (e.g. S01)
time: factor that splits data into 10-minute time periods (1-8)
target_present: 1 if target (short line) was shown; 0 otherwise (long line)
response: 1 if participant responded a target was present; 0 otherwise (no target present)
RT: response time in milliseconds
outcome: either Hit, Miss, False alarm or Correct rejection

Response time data

RT <- per_trial %>%
  filter(outcome == "Hit") %>% # include only correct trials
  group_by(participant,time) %>% # for each block
  summarise(RT = mean(RT)) # compute mean RT

dataDir <- here("data","behavior")
write_csv(RT,file.path(dataDir,"RT.csv"))

RM ANOVA

anovaRT <- ezANOVA(data = data.frame(RT), dv = .(RT), wid = .(participant), within = .(time), type = 3, detailed = TRUE) 
anovaRT %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	78708390.25	1785243.8	881.766	0.000	*	0.973	0.978
time	7	140	48285.99	376627.1	2.564	0.016	*	0.022	0.114

	Effect	W	p	p<.05
2	time	0.048	0.002	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.546	0.047	*	0.691	0.033	*

Main effect of time on RT: F(3.82, 76.45) = 2.56, p = .047, \(\eta^2_p\) = .11

Follow-up tests

RT_ttest <- RT %>%
  select(participant, time, RT) %>%
  spread(time,RT,sep = "_")

# compute t-tests
RT_1v6 <- t.test(RT_ttest$time_1, RT_ttest$time_6, paired = TRUE)
RT_1v7 <- t.test(RT_ttest$time_1, RT_ttest$time_7, paired = TRUE)
RT_1v8 <- t.test(RT_ttest$time_1, RT_ttest$time_8, paired = TRUE)
RT_6v7 <- t.test(RT_ttest$time_6, RT_ttest$time_7, paired = TRUE)

# put result in data frame, bind all 4 together
bind_rows(tidy(RT_1v6), 
          tidy(RT_1v7),
          tidy(RT_1v8),
          tidy(RT_6v7)) %>%
  cohen_d_paired() %>% # add Cohen's d
  bind_cols(tibble(condition =  c("1v6", "1v7", "1v8", "6v7")), .) %>% # add column indiciating condition comparisons
  kable(digits = 3) # display

condition	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
1v6	-44.470	-2.786	0.011	20	-77.767	-11.172	Paired t-test	two.sided	0.608
1v7	-21.999	-1.577	0.130	20	-51.098	7.100	Paired t-test	two.sided	0.344
1v8	-48.188	-2.307	0.032	20	-91.754	-4.622	Paired t-test	two.sided	0.503
6v7	22.470	1.447	0.163	20	-9.923	54.864	Paired t-test	two.sided	0.316

RT_1v6_bf <- extractBF(ttestBF(RT_ttest$time_1, RT_ttest$time_6, paired = TRUE), onlybf = TRUE)
RT_1v7_bf <- extractBF(ttestBF(RT_ttest$time_1, RT_ttest$time_7, paired = TRUE), onlybf = TRUE)
RT_1v8_bf <- extractBF(ttestBF(RT_ttest$time_1, RT_ttest$time_8, paired = TRUE), onlybf = TRUE)
RT_6v7_bf <- extractBF(ttestBF(RT_ttest$time_6, RT_ttest$time_7, paired = TRUE), onlybf = TRUE)

tibble(condition =  c("1v6", "1v7", "1v8", "6v7"), 
       bf = c(RT_1v6_bf, RT_1v7_bf, RT_1v8_bf, RT_6v7_bf)) %>%
  kable(digits = 2)

condition	bf
1v6	4.52
1v7	0.66
1v8	1.95
6v7	0.56

Difference in RT between block 6 and block 7: t(20) = 1.45, p = .163, d = 0.32, BF₀₁ = 1.78

Signal detection data (Hit rate, False alarm rate, A’)

per_trial %>%
  group_by(participant,time) %>% # for each condition and participant
  count(outcome) %>% # count number of trials
  group_by(outcome) %>% # for each outcome
  summarise_at(vars(n), funs(mean, min, max, sd), na.rm = TRUE) %>% # apply summary functions to trial counts column
  kable(digits = 1, caption = 'Trial counts per type')

Trial counts per type
outcome	mean	min	max	sd
Correct rejection	234.6	207	240	6.3
False alarm	6.6	1	33	6.3
Hit	32.7	10	54	10.5
Miss	27.3	6	50	10.5

SDT <- per_trial %>%
  group_by(participant,time) %>% # for each block
  count(outcome) %>% # count how many Hits, Misses, False Alarms, and Correct rejecitons
  spread(outcome,n) %>% # make each of these a separate column
  mutate_all(funs(replace(., is.na(.), 0))) %>% # replace any missing counts with 0 (e.g. if there were no false alarms)
  summarise(hit_rate = Hit / (Hit + Miss),
            fa_rate = `False alarm` / (`False alarm` + `Correct rejection`)) %>%
  mutate(a_prime = .5 + (((hit_rate - fa_rate) * (1 + hit_rate - fa_rate)) / ((4 * hit_rate) * (1 - fa_rate))) )

dataDir <- here("data","behavior")
write_csv(SDT,file.path(dataDir,"SDT.csv"))

Sensitivity (A’)

RM ANOVA

anovaAprime <- ezANOVA(data = data.frame(SDT), dv = .(a_prime), wid = .(participant), within = .(time), type = 3, detailed = TRUE)
anovaAprime %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	128.222	0.155	16524.665	0	*	0.998	0.999
time	7	140	0.053	0.143	7.468	0	*	0.152	0.272

	Effect	W	p	p<.05
2	time	0.011	0	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.357	0.001	*	0.412	0	*

Main effect of time on A’: F(2.50, 49.96) = 7.47, p < .001, \(\eta^2_p\) = .27

Paired t-tests

A_ttest <- SDT %>%
  select(participant, time, a_prime) %>%
  spread(time,a_prime,sep = "_")

# compute t-tests, put result in data frame, bind all 4 together

A_1v6 <- t.test(A_ttest$time_1, A_ttest$time_6, paired = TRUE)
A_1v7 <- t.test(A_ttest$time_1, A_ttest$time_7, paired = TRUE)
A_1v8 <- t.test(A_ttest$time_1, A_ttest$time_8, paired = TRUE)
A_6v7 <- t.test(A_ttest$time_6, A_ttest$time_7, paired = TRUE)

bind_rows(tidy(A_1v6), 
          tidy(A_1v7 ),
          tidy(A_1v8),
          tidy(A_6v7)) %>%
  cohen_d_paired() %>% # add Cohen's d
  bind_cols(tibble(condition =  c("1v6", "1v7", "1v8", "6v7")), .) %>% # add column indiciating condition comparisons
  kable(digits = 3) # display

condition	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
1v6	0.052	5.752	0.000	20	0.033	0.071	Paired t-test	two.sided	1.255
1v7	0.029	2.400	0.026	20	0.004	0.053	Paired t-test	two.sided	0.524
1v8	0.062	4.232	0.000	20	0.031	0.092	Paired t-test	two.sided	0.923
6v7	-0.024	-2.509	0.021	20	-0.044	-0.004	Paired t-test	two.sided	0.547

A_1v6_bf <- extractBF(ttestBF(A_ttest$time_1, A_ttest$time_6, paired = TRUE), onlybf = TRUE)
A_1v7_bf <- extractBF(ttestBF(A_ttest$time_1, A_ttest$time_7, paired = TRUE), onlybf = TRUE)
A_1v8_bf <- extractBF(ttestBF(A_ttest$time_1, A_ttest$time_8, paired = TRUE), onlybf = TRUE)
A_6v7_bf <- extractBF(ttestBF(A_ttest$time_6, A_ttest$time_7, paired = TRUE), onlybf = TRUE)

tibble(condition = c("1v6", "1v7", "1v8", "6v7"), bf = c(A_1v6_bf, A_1v7_bf, A_1v8_bf, A_6v7_bf)) %>%
  kable(digits = 2)

condition	bf
1v6	1791.10
1v7	2.28
1v8	79.19
6v7	2.75

Difference in A’ between block 1 and block 7: t(20) = 2.40, p = .026, d = 0.52, BF₁₀ = 2.28
Difference in A’ between block 1 and block 8: t(20) = 4.23, p < .001, d = 0.92, BF₁₀ = 79.2
Difference in A’ between block 6 and block 7: t(20) = -2.51, p = .021, d = -0.55, BF₁₀ = 2.75

Hit rate

RM ANOVA

anovaHitRate <- ezANOVA(data = data.frame(SDT), dv = .(hit_rate), wid = .(participant), within = .(time), type = 3, detailed = TRUE)
anovaHitRate %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	49.926	2.413	413.780	0	*	0.921	0.954
time	7	140	0.813	1.859	8.745	0	*	0.160	0.304

	Effect	W	p	p<.05
2	time	0.01	0	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.349	0	*	0.402	0	*

Main effect of time on Hit rate: F(2.45, 48.92) = 8.74, p < .001, \(\eta^2_p\) = .30

Paired t-tests

HR_ttest <- SDT %>%
  select(participant, time, hit_rate) %>%
  spread(time, hit_rate, sep = "_")

# compute t-tests
Hit_1v6 <- t.test(HR_ttest$time_1, HR_ttest$time_6, paired = TRUE)
Hit_1v7 <- t.test(HR_ttest$time_1, HR_ttest$time_7, paired = TRUE)
Hit_1v8 <- t.test(HR_ttest$time_1, HR_ttest$time_8, paired = TRUE)
Hit_6v7 <- t.test(HR_ttest$time_6, HR_ttest$time_7, paired = TRUE)

# put result in data frame, bind all 4 together
bind_rows(tidy(Hit_1v6), 
          tidy(Hit_1v7),
          tidy(Hit_1v8),
          tidy(Hit_6v7)) %>%
  cohen_d_paired() %>% # add Cohen's d
  bind_cols(tibble(condition =  c("1v6", "1v7", "1v8", "6v7")), .) %>% # add column indiciating condition comparisons
  kable(digits = 3) # display

condition	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
1v6	0.213	6.331	0.000	20	0.143	0.284	Paired t-test	two.sided	1.382
1v7	0.137	2.749	0.012	20	0.033	0.240	Paired t-test	two.sided	0.600
1v8	0.236	4.273	0.000	20	0.121	0.351	Paired t-test	two.sided	0.932
6v7	-0.077	-2.171	0.042	20	-0.151	-0.003	Paired t-test	two.sided	0.474

Hit_1v6_bf <- extractBF(ttestBF(HR_ttest$time_1, HR_ttest$time_6, paired = TRUE), onlybf = TRUE)
Hit_1v7_bf <- extractBF(ttestBF(HR_ttest$time_1, HR_ttest$time_7, paired = TRUE), onlybf = TRUE)
Hit_1v8_bf <- extractBF(ttestBF(HR_ttest$time_1, HR_ttest$time_8, paired = TRUE), onlybf = TRUE)
Hit_6v7_bf <- extractBF(ttestBF(HR_ttest$time_6, HR_ttest$time_7, paired = TRUE), onlybf = TRUE)

tibble(condition =  c("1v6", "1v7", "1v8", "6v7"), 
       bf = c(Hit_1v6_bf, Hit_1v7_bf, Hit_1v8_bf, Hit_6v7_bf)) %>%
  kable(., digits = 2)

condition	bf
1v6	5667.76
1v7	4.23
1v8	86.24
6v7	1.56

Difference in Hit rate between block 6 and block 7: t(20) = -2.17, p = .042, d = -0.47, BF₁₀ = 1.56

False alarm rate

RM ANOVA

anova_FArate <- ezANOVA(data = data.frame(SDT), dv = .(fa_rate), wid = .(participant), within = .(time), type = 3, detailed = TRUE)
anova_FArate %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	0.086	0.064	26.816	0.00	*	0.443	0.573
time	7	140	0.005	0.044	2.302	0.03	*	0.045	0.103

	Effect	W	p	p<.05
2	time	0.015	0	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.441	0.084		0.53	0.071

Main effect of time on false alarm rate: F(3.08, 61.69) = 2.30, p = .084, \(\eta^2_p\) = .10

Paired t-tests

FAR_ttest <- SDT %>%
  select(participant, time, fa_rate) %>%
  spread(time,fa_rate,sep = "_")

# compute t-tests
FA_1v6 <- t.test(FAR_ttest$time_1, FAR_ttest$time_6, paired = TRUE)
FA_1v7 <- t.test(FAR_ttest$time_1, FAR_ttest$time_7, paired = TRUE)
FA_1v8 <- t.test(FAR_ttest$time_1, FAR_ttest$time_8, paired = TRUE)
FA_6v7 <- t.test(FAR_ttest$time_6, FAR_ttest$time_7, paired = TRUE)

# put result in data frame, bind all 4 together
bind_rows(tidy(FA_1v6), 
          tidy(FA_1v7),
          tidy(FA_1v8),
          tidy(FA_6v7)) %>%
  cohen_d_paired() %>% # add Cohen's d
  bind_cols(tibble(condition =  c("1v6", "1v7", "1v8", "6v7")), .) %>% # add column indiciating condition comparisons
  kable(digits = 3) # display

condition	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
1v6	0.011	1.220	0.237	20	-0.008	0.030	Paired t-test	two.sided	0.266
1v7	0.017	2.910	0.009	20	0.005	0.030	Paired t-test	two.sided	0.635
1v8	0.007	0.852	0.405	20	-0.011	0.025	Paired t-test	two.sided	0.186
6v7	0.006	1.095	0.286	20	-0.006	0.018	Paired t-test	two.sided	0.239

FA_1v6_bf <- extractBF(ttestBF(FAR_ttest$time_1, FAR_ttest$time_6, paired = TRUE), onlybf = TRUE)
FA_1v7_bf <- extractBF(ttestBF(FAR_ttest$time_1, FAR_ttest$time_7, paired = TRUE), onlybf = TRUE)
FA_1v8_bf <- extractBF(ttestBF(FAR_ttest$time_1, FAR_ttest$time_8, paired = TRUE), onlybf = TRUE)
FA_6v7_bf <- extractBF(ttestBF(FAR_ttest$time_6, FAR_ttest$time_7, paired = TRUE), onlybf = TRUE)

tibble(condition =  c("1v6", "1v7", "1v8", "6v7"), bf = c(FA_1v6_bf,FA_1v7_bf,FA_1v8_bf,FA_6v7_bf)) %>%
  kable(digits = 2)

condition	bf
1v6	0.44
1v7	5.69
1v8	0.31
6v7	0.39

Difference in false alarm rate between block 6 and block 7: t(20) = 1.10, p = .286, d = 0.24, BF₁₀ = 2.59

Ratings data

Load

# Lists of files to load
dataDir <- here("data","behavior")
files_mot <- list.files(dataDir, "_motivation.txt", full.names = TRUE, recursive = TRUE)
files_av <- list.files(dataDir, "_aversion.txt", full.names = TRUE, recursive = TRUE)

# Function with data operations per file
load_ratings_data <- function(filenames_mot, filenames_av) {
  file_sub <- str_extract(filenames_mot, "S[0-9]{2}") # extract participant ID
  motivation <- read_tsv(filenames_mot, col_names = "motivation", col_types = 'i')
  aversion <- read_tsv(filenames_av, col_names = "aversion", col_types = 'i')
  
  ratings <- bind_cols(motivation,aversion) %>% # combine motivation and aversion scores
    mutate(participant = rep(file_sub,nrow(.)), # add column with participant ID
           time = c("begin","1","2","3","4","5","6","post","7","8")) # add column with time point labels
}

# Execute the function for each file; bind into single data frame
ratings <- map2_df(files_mot,files_av, load_ratings_data)

Clean

participants_to_include <- c("S01","S03","S04","S05","S06","S07","S08","S11","S12","S15","S16","S18","S19","S21","S22","S23","S25","S27","S28","S29","S30")
ratings <- ratings %>%
  filter(participant %in% participants_to_include) %>% # exclude participants not in list
  select(participant, time, motivation, aversion) %>% # reorder ratings columns
  mutate(participant = parse_factor(participant,NULL), # create factors for statistical analysis
         time = parse_factor(time, levels = c("begin","1","2","3","4","5","6","post","7","8")))

ratings

## # A tibble: 210 x 4
##    participant time  motivation aversion
##    <fct>       <fct>      <int>    <int>
##  1 S01         begin          6        3
##  2 S01         1              7        3
##  3 S01         2              7        2
##  4 S01         3              6        5
##  5 S01         4              4        5
##  6 S01         5              3        5
##  7 S01         6              3        6
##  8 S01         post           5        5
##  9 S01         7              6        4
## 10 S01         8              4        6
## # ... with 200 more rows

For each participant, we now have:

participant: participant ID (e.g. S01)
time: 10 time point when rating was taken. 8 are numbers, indicating ratings after a block of 10 minutes (e.g. 6 is after 60 minutes of task performance). begin is the rating before the task started; post is the rating directly after the motivation instruction that was shown after block 6.
motivation: 7-point scale rating of aversion to continue the task (1 is no aversion, 7 is strongest aversion)
aversion: 7-point scale rating of motivation to continue the task (1 is no motivation, 7 is strongest motivation)

dataDir <- here("data","behavior")
write_csv(ratings, file.path(dataDir,"motivation_aversion.csv"))

Motivation

RM ANOVA

anovaMotivation <- ezANOVA(data = data.frame(ratings), dv = .(motivation), wid = .(participant), within = .(time), type = 3, detailed = TRUE)
anovaMotivation %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	4004.233	208.467	384.161	0	*	0.899	0.951
time	9	180	86.576	243.724	7.104	0	*	0.161	0.262

	Effect	W	p	p<.05
2	time	0.001	0	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.432	0	*	0.55	0	*

Main effect of time on motivation: F(3.89, 77.80) = 7.10, p < .001, \(\eta^2_p\) = .26

Paired t-tests

Mot_ttest <- ratings %>%
  select(participant, time, motivation) %>%
  spread(time, motivation, sep = "_")

# compute t-tests
Mot_beginV6 <- t.test(Mot_ttest$time_begin, Mot_ttest$time_6, paired = TRUE)
Mot_beginVpost <- t.test(Mot_ttest$time_begin, Mot_ttest$time_post, paired = TRUE)
Mot_beginV8 <- t.test(Mot_ttest$time_begin, Mot_ttest$time_8, paired = TRUE)
Mot_6Vpost <- t.test(Mot_ttest$time_6, Mot_ttest$time_post, paired = TRUE)

# put result in data frame, bind all 4 together
bind_rows(tidy(Mot_beginV6), 
          tidy(Mot_beginVpost),
          tidy(Mot_beginV8),
          tidy(Mot_6Vpost)) %>%
  cohen_d_paired() %>% # add Cohen's d
  bind_cols(tibble(condition =  c("begin vs. 6", "begin vs. post", "begin vs. 8", "6 vs. post")), .) %>% # add column indiciating condition comparisons
  kable(digits = 3) # display

condition	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
begin vs. 6	1.524	3.553	0.002	20	0.629	2.418	Paired t-test	two.sided	0.775
begin vs. post	-0.571	-1.743	0.097	20	-1.255	0.112	Paired t-test	two.sided	0.380
begin vs. 8	0.762	1.817	0.084	20	-0.113	1.636	Paired t-test	two.sided	0.397
6 vs. post	-2.095	-5.139	0.000	20	-2.946	-1.245	Paired t-test	two.sided	1.121

Mot_beginV6_bf <- extractBF(ttestBF(Mot_ttest$time_begin, Mot_ttest$time_6, paired = TRUE), onlybf = TRUE)
Mot_beginVpost_bf <- extractBF(ttestBF(Mot_ttest$time_begin, Mot_ttest$time_post, paired = TRUE), onlybf = TRUE)
Mot_beginV8_bf <- extractBF(ttestBF(Mot_ttest$time_begin, Mot_ttest$time_8, paired = TRUE), onlybf = TRUE)
Mot_6Vpost_bf <- extractBF(ttestBF(Mot_ttest$time_6, Mot_ttest$time_post, paired = TRUE), onlybf = TRUE)

tibble(condition =  c("begin vs. 6", "begin vs. post", "begin vs. 8", "6 vs. post"), 
       bf = c(Mot_beginV6_bf, Mot_beginVpost_bf, Mot_beginV8_bf, Mot_6Vpost_bf)) %>%
  kable(digits = 2)

condition	bf
begin vs. 6	19.89
begin vs. post	0.82
begin vs. 8	0.91
6 vs. post	514.52

Difference in motivation between begin and block 6: t(20) = 3.55, p = .002, d = 0.78, BF₁₀ = 19.9
Difference in motivation between block 6 and post: t(20) = -5.14, p < .001, d = -1.12, BF₁₀ = 515
Difference in motivation between begin and post: t(20) = -1.74, p = .097, d = -0.38, BF₀₁ = 1.21
Difference in motivation between begin and block 8: t(20) = 1.82, p = .084, d = 0.40, BF₀₁ = 1.09

Aversion

RM ANOVA

anovaAversion <- ezANOVA(data = data.frame(ratings), dv = .(aversion), wid = .(participant), within = .(time), type = 3, detailed = TRUE)
anovaAversion %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	4109.719	443.981	185.130	0	*	0.867	0.903
time	9	180	119.186	188.114	12.672	0	*	0.159	0.388

	Effect	W	p	p<.05
2	time	0	0	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.384	0	*	0.474	0	*

Main effect of time on aversion: F(3.46, 69.17) = 12.67, p < .001, \(\eta^2_p\) = .39

Paired t-tests

Av_ttest <- ratings %>%
  select(participant, time, aversion) %>%
  spread(time, aversion, sep = "_")

# compute t-tests
Av_beginV6 <- t.test(Av_ttest$time_begin, Av_ttest$time_6, paired = TRUE)
Av_beginVpost <- t.test(Av_ttest$time_begin, Av_ttest$time_post, paired = TRUE)
Av_beginV8 <- t.test(Av_ttest$time_begin, Av_ttest$time_8, paired = TRUE)
Av_6Vpost <- t.test(Av_ttest$time_6, Av_ttest$time_post, paired = TRUE)

# put result in data frame, bind all 4 together
bind_rows(tidy(Av_beginV6), 
          tidy(Av_beginVpost),
          tidy(Av_beginV8),
          tidy(Av_6Vpost)) %>%
  cohen_d_paired() %>% # add Cohen's d
  bind_cols(tibble(condition =  c("begin vs. 6", "begin vs. post", "begin vs. 8", "6 vs. post")), .) %>% # add column indiciating condition comparisons
  kable(digits = 3) # display

condition	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
begin vs. 6	-2.667	-6.162	0.000	20	-3.569	-1.764	Paired t-test	two.sided	1.345
begin vs. post	-2.000	-4.369	0.000	20	-2.955	-1.045	Paired t-test	two.sided	0.953
begin vs. 8	-2.381	-4.591	0.000	20	-3.463	-1.299	Paired t-test	two.sided	1.002
6 vs. post	0.667	3.162	0.005	20	0.227	1.106	Paired t-test	two.sided	0.690

Av_beginV6_bf <- extractBF(ttestBF(Av_ttest$time_begin, Av_ttest$time_6, paired = TRUE), onlybf = TRUE)
Av_beginVpost_bf <- extractBF(ttestBF(Av_ttest$time_begin, Av_ttest$time_post, paired = TRUE), onlybf = TRUE)
Av_beginV8_bf <- extractBF(ttestBF(Av_ttest$time_begin, Av_ttest$time_8, paired = TRUE), onlybf = TRUE)
Av_6Vpost_bf <- extractBF(ttestBF(Av_ttest$time_6, Av_ttest$time_post, paired = TRUE), onlybf = TRUE)

tibble(condition = c("begin vs. 6", "begin vs. post", "begin vs. 8", "6 vs. post"), 
       bf = c(Av_beginV6_bf, Av_beginVpost_bf, Av_beginV8_bf, Av_6Vpost_bf)) %>%
  kable(digits = 2)

condition	bf
begin vs. 6	4057.15
begin vs. post	105.14
begin vs. 8	166.27
6 vs. post	9.20

Difference in aversion between begin and block 6: t(20) = -6.16, p < .001, d = -1.34, BF₁₀ = 4057
Difference in aversion between block 6 and post: t(20) = 3.16, p = .005, d = 0.69, BF₁₀ = 9.2

EEG data

data_dir_eeg <- here("data", "EEG", "statistics")

Theta - ITPC

Load the files with EEG data for statistics, written out from MATLAB

ITPC_10m_lPO <- read_tsv(file.path(data_dir_eeg, "ITPC", "10m_peak_ITPC_PO7P5P7_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

ITPC_10m_rPO <- read_tsv(file.path(data_dir_eeg, "ITPC", "10m_peak_ITPC_PO8P6P8_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

ITPC_10m_MF <- read_tsv(file.path(data_dir_eeg, "ITPC", "10m_peak_ITPC_FCzFC1FC2_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

ITPC_20m_lPO <- read_tsv(file.path(data_dir_eeg, "ITPC", "20m_peak_ITPC_PO7P5P7_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

ITPC_20m_rPO <- read_tsv(file.path(data_dir_eeg, "ITPC", "20m_peak_ITPC_PO8P6P8_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

ITPC_20m_MF <- read_tsv(file.path(data_dir_eeg, "ITPC", "20m_peak_ITPC_FCzFC1FC2_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

Correct rejections (10 minutes)

ITPC_10m <- reduce(list(ITPC_10m_lPO, ITPC_10m_rPO, ITPC_10m_MF),full_join)  %>%
  select(-X26,-ends_with("_time"), -ends_with("_freq")) %>% # drop spurious and peak time/frequency columns
  gather(key = "key", value = "voltage", -subnum) %>%  # gather all columns with voltages
  separate(key, into = c("region","time"), extra = "drop", sep = "\\_Correct_rejection_|\\_t") %>% # split into electrode pool (3) and time (8)
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X26")
## Joining, by = c("subnum", "X26")

RM ANOVA

anova_ITPC_10m <- ezANOVA(data = data.frame(ITPC_10m), dv = .(voltage), wid = .(subnum), within = .(time, region), type = 3, detailed = TRUE)
anova_ITPC_10m %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	64.538	9.348	138.079	0.000	*	0.836	0.873
time	7	140	0.161	0.721	4.463	0.000	*	0.013	0.182
region	2	40	2.328	2.227	20.907	0.000	*	0.156	0.511
time:region	14	280	0.030	0.338	1.799	0.038	*	0.002	0.083

	Effect	W	p	p<.05
2	time	0.076	0.019	*
3	region	0.739	0.057
4	time:region	0.000	0.159

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.547	0.003	*	0.692	0.001	*
3	region	0.793	0.000	*	0.850	0.000	*
4	time:region	0.552	0.084		0.934	0.043	*

Main effect of time on theta ITPC: F(3.83, 76.52) = 4.46, p = .003, \(\eta^2_p\) = .18

Paired t-tests

ITPC_10m_6v7 <- paired_test_eeg(ITPC_10m)

print_paired_test_eeg(ITPC_10m_6v7, return_t = TRUE) %>%
  kable(digits = 3)

region	BF	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
PO7P5P7	20.073	-0.046	-3.558	0.002	20	-0.072	-0.019	Paired t-test	two.sided	0.776
PO8P6P8	1.086	-0.027	-1.936	0.067	20	-0.056	0.002	Paired t-test	two.sided	0.422
FCzFC1FC2	8.643	-0.046	-3.130	0.005	20	-0.077	-0.015	Paired t-test	two.sided	0.683

print_paired_test_eeg(ITPC_10m_6v7, return_BF = TRUE) %>%
  kable(digits = 2)

region	BF
PO7P5P7	20.07
PO8P6P8	1.09
FCzFC1FC2	8.64

Difference in theta ITPC between block 6 and 7 in left PO electrodes: t(20) = -3.56, p = .002, d = -0.78, BF₁₀ = 20.1
Difference in theta ITPC between block 6 and 7 in right PO electrodes: t(20) = -1.94, p = .067, d = -0.42, BF₁₀ = 1.09
Difference in theta ITPC between block 6 and 7 in MF electrodes: t(20) = -3.13, p = .005, d = -0.68, BF₁₀ = 8.64

Multilevel model

Response: A’
Fixed effect: ITPC
Random intercept: -
Random slope: -
Nesting structure: time points nested in subjects
Correlation structure: First-order autoregressive

# The order/naming of the subjects in the behavioral ("participant") and EEG ("subnum") dataframes is not consistent; this provises the mapping
subject_order_EEG <- tibble(subnum = seq(1,21), participant = c('S11','S12','S15','S16','S18','S19','S01','S21','S22','S23','S25','S27','S28','S29','S30','S03',    'S04','S05','S06','S07','S08')) 

ITPC_SDT_10m_mixed <- ITPC_10m %>%
  mutate(subnum = as.integer(subnum)) %>% # coerce subnum from ITPC_10m to be integer (to match in subject_order_eeg)
  left_join(.,subject_order_EEG, by = "subnum") %>% # add the "participant" mapping column
   # make these columns match the SDT data frame
  mutate(participant = parse_factor(participant, levels = levels(SDT$participant)), # participant is a factor in SDT, and in different order
         time = parse_factor(as.integer(time), levels = NULL)) %>% # time goes from 1 to 8 and is a factor in SDT
  left_join(.,SDT, by = c("time","participant")) %>% # add the SDT data
  mutate(time = as.integer(time)) %>% # repeated measure ID has to be integer for the models
  rename(ITPC = voltage) # rename "voltage" column from ITPC data frame

rPO:

ITPC_SDT_10m_mixed %>% 
  gls(a_prime ~ ITPC, 
            correlation = corAR1(form = ~ time | participant),
            data = .,
            subset = region == "PO8P6P8") %>%
  summary()

## Generalized least squares fit by REML
##   Model: a_prime ~ ITPC 
##   Data: . 
##   Subset: region == "PO8P6P8" 
##         AIC       BIC   logLik
##   -633.5932 -621.1452 320.7966
## 
## Correlation Structure: AR(1)
##  Formula: ~time | participant 
##  Parameter estimate(s):
##       Phi 
## 0.6963717 
## 
## Coefficients:
##                 Value  Std.Error  t-value p-value
## (Intercept) 0.8246873 0.01479529 55.73984   0e+00
## ITPC        0.1130090 0.02902639  3.89332   1e-04
## 
##  Correlation: 
##      (Intr)
## ITPC -0.89 
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.46020657 -0.71526578 -0.02120056  0.60097948  2.05325350 
## 
## Residual standard error: 0.04632284 
## Degrees of freedom: 168 total; 166 residual

lPO:

ITPC_SDT_10m_mixed %>% 
  gls(a_prime ~ ITPC, 
            correlation = corAR1(form = ~ time | participant),
            data = .,
            subset = region == "PO7P5P7") %>%
  summary()

## Generalized least squares fit by REML
##   Model: a_prime ~ ITPC 
##   Data: . 
##   Subset: region == "PO7P5P7" 
##         AIC       BIC   logLik
##   -627.6256 -615.1777 317.8128
## 
## Correlation Structure: AR(1)
##  Formula: ~time | participant 
##  Parameter estimate(s):
##       Phi 
## 0.7517676 
## 
## Coefficients:
##                 Value  Std.Error  t-value p-value
## (Intercept) 0.8432367 0.01326363 63.57510  0.0000
## ITPC        0.1145791 0.03688280  3.10657  0.0022
## 
##  Correlation: 
##      (Intr)
## ITPC -0.805
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.49982851 -0.62840807  0.03004564  0.65998032  1.84162189 
## 
## Residual standard error: 0.0508978 
## Degrees of freedom: 168 total; 166 residual

MF:

ITPC_SDT_10m_mixed %>% 
  gls(a_prime ~ ITPC, 
            correlation = corAR1(form = ~ time | participant),
            data = .,
            subset = region == "FCzFC1FC2") %>%
  summary()

## Generalized least squares fit by REML
##   Model: a_prime ~ ITPC 
##   Data: . 
##   Subset: region == "FCzFC1FC2" 
##         AIC       BIC   logLik
##   -635.6804 -623.2325 321.8402
## 
## Correlation Structure: AR(1)
##  Formula: ~time | participant 
##  Parameter estimate(s):
##       Phi 
## 0.7030903 
## 
## Coefficients:
##                 Value  Std.Error  t-value p-value
## (Intercept) 0.8254778 0.01400682 58.93401   0e+00
## ITPC        0.1469372 0.03558883  4.12874   1e-04
## 
##  Correlation: 
##      (Intr)
## ITPC -0.874
## 
## Standardized residuals:
##         Min          Q1         Med          Q3         Max 
## -2.32801071 -0.66794111  0.01643409  0.63781178  2.23474612 
## 
## Residual standard error: 0.04646776 
## Degrees of freedom: 168 total; 166 residual

Hits and misses (20 minutes)

ITPC_20m <- reduce(list(ITPC_20m_lPO, ITPC_20m_rPO, ITPC_20m_MF), full_join) %>%
  select(-X26,-ends_with("_time"), -ends_with("_freq")) %>% #
  gather(key = "key", value = "voltage", -subnum) %>% # gather all columns with voltages
  separate(key, into = c("region","condition","time"), extra = "merge", sep = "_") %>% # split into region (3), condition (2) and time (4). Split after underscore (but stop after splitting into 3 columns)
  mutate(time = str_replace(time,"_t.+","")) %>% # remove unnecessary info after last underscore in time vector
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         condition = parse_factor(condition, levels = NULL),
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X26")
## Joining, by = c("subnum", "X26")

RM ANOVA

anova_ITPC_20m <- ezANOVA(data = data.frame(ITPC_20m), dv = .(voltage), wid = .(subnum), within = .(time, region, condition), type = 3, detailed = TRUE) 
anova_ITPC_20m %>%
  ezANOVA_pes() %>%
  kable()

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	83.4314806	6.2077650	268.797161	0.0000000	*	0.8855208	0.9307472
time	3	60	0.1643773	0.7534134	4.363537	0.0075997	*	0.0150112	0.1791011
region	2	40	1.3826828	1.7153602	16.121195	0.0000073	*	0.1136270	0.4463085
condition	1	20	1.8009405	0.6177012	58.311058	0.0000002	*	0.1430809	0.7446082
time:region	6	120	0.0329346	0.4527114	1.454994	0.1995046		0.0030442	0.0678161
time:condition	3	60	0.0292374	0.4203938	1.390952	0.2542835		0.0027034	0.0650252
region:condition	2	40	0.1092865	0.2652160	8.241321	0.0010067	*	0.0100307	0.2918178
time:region:condition	6	120	0.0194744	0.3533687	1.102212	0.3651112		0.0018023	0.0522321

	Effect	W	p	p<.05
2	time	0.5022246	0.0246778	*
3	region	0.8480515	0.2089350
5	time:region	0.4093961	0.7301661
6	time:condition	0.8781756	0.7870651
7	region:condition	0.7943573	0.1122408
8	time:region:condition	0.1647764	0.0456248	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.6811105	0.0185383	*	0.7594201	0.0148678	*
3	region	0.8680944	0.0000240	*	0.9433946	0.0000122	*
5	time:region	0.7535219	0.2172721		1.0007596	0.1995046
6	time:condition	0.9313545	0.2560163		1.0979803	0.2542835
7	region:condition	0.8294331	0.0021491	*	0.8951516	0.0016035	*
8	time:region:condition	0.6811810	0.3616961		0.8779956	0.3645042

Main effect of condition on theta ITPC: F(1, 20) = 58.31, p < .001, \(\eta^2_p\) = .74
Theta ITPC: time by condition interaction theta ITPC: F(3, 60) = 1.39, p = .254, \(\eta^2_p\) = .07

Theta - Power

Load the files with EEG data for statistics, written out from MATLAB

power_10m_lPO <- read_tsv(file.path(data_dir_eeg, "power", "10m_peak_power_PO7P5P7_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

power_10m_rPO <- read_tsv(file.path(data_dir_eeg, "power", "10m_peak_power_PO8P6P8_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

power_10m_MF <- read_tsv(file.path(data_dir_eeg, "power", "10m_peak_power_FCzFC1FC2_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

power_20m_lPO <- read_tsv(file.path(data_dir_eeg, "power", "20m_peak_power_PO7P5P7_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

power_20m_rPO <- read_tsv(file.path(data_dir_eeg, "power", "20m_peak_power_PO8P6P8_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

power_20m_MF <- read_tsv(file.path(data_dir_eeg, "power", "20m_peak_power_FCzFC1FC2_[150_500]ms_[3_7]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X26' [26]

Correct rejections (10 minutes)

power_10m <- reduce(list(power_10m_lPO, power_10m_rPO, power_10m_MF),full_join)  %>%
  select(-X26,-ends_with("_time"), -ends_with("_freq")) %>% # drop spurious and peak time/frequency columns
  gather(key = "key", value = "voltage", -subnum) %>%  # gather all columns with voltages
  separate(key, into = c("region","time"), extra = "drop", sep = "\\_Correct_rejection_|\\_t") %>% # split into electrode pool (3) and time (8)
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X26")
## Joining, by = c("subnum", "X26")

RM ANOVA

anova_power_10m <- ezANOVA(data = data.frame(power_10m), dv = .(voltage), wid = .(subnum), within = .(time, region), type = 3, detailed = TRUE)
anova_power_10m %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	924.033	417.848	44.228	0.000	*	0.586	0.689
time	7	140	17.375	70.812	4.907	0.000	*	0.026	0.197
region	2	40	70.503	130.765	10.783	0.000	*	0.098	0.350
time:region	14	280	1.396	32.831	0.851	0.613		0.002	0.041

	Effect	W	p	p<.05
2	time	0.013	0.000	*
3	region	0.964	0.709
4	time:region	0.000	0.057

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.435	0.004	*	0.521	0.002	*
3	region	0.966	0.000	*	1.067	0.000	*
4	time:region	0.495	0.547		0.785	0.590

Main effect of time on theta power: F(3.04, 60.85) = 4.91, p = .004, \(\eta^2_p\) = .20

Paired t-tests

power_10m_6v7 <- paired_test_eeg(power_10m)

print_paired_test_eeg(power_10m_6v7, return_t = TRUE) %>%
  kable(digits = 3)

region	BF	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
PO7P5P7	0.274	0.109	0.646	0.525	20	-0.242	0.459	Paired t-test	two.sided	0.141
PO8P6P8	0.313	0.115	0.844	0.409	20	-0.169	0.399	Paired t-test	two.sided	0.184
FCzFC1FC2	0.468	0.137	1.285	0.214	20	-0.085	0.359	Paired t-test	two.sided	0.280

print_paired_test_eeg(power_10m_6v7, return_BF = TRUE) %>%
  kable(digits = 2)

region	BF
PO7P5P7	0.27
PO8P6P8	0.31
FCzFC1FC2	0.47

Difference in theta power between block 6 and 7 in left PO electrodes: t(20) = 0.65, p = .525, d = 0.14, BF₀₁ = 3.64
Difference in theta power between block 6 and 7 in right PO electrodes: t(20) = 0.84, p = .409, d = 0.18, BF₀₁ = 3.2
Difference in theta power between block 6 and 7 in MF electrodes: t(20) = 1.28, p = .214, d = 0.28, BF₀₁ = 2.14

Hits and misses (20 minutes)

power_20m <- reduce(list(power_20m_lPO, power_20m_rPO, power_20m_MF), full_join) %>%
  select(-X26,-ends_with("_time"), -ends_with("_freq")) %>%
  gather(key = "key", value = "voltage", -subnum) %>% # gather all columns with voltages
  separate(key, into = c("region","condition","time"), extra = "merge", sep = "_") %>% # split into region (3), condition (2) and time (4). Split after underscore (but stop after splitting into 3 columns)
  mutate(time = str_replace(time,"_t.+","")) %>% # remove unnecessary info after last underscore in time vector
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         condition = parse_factor(condition, levels = NULL),
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X26")
## Joining, by = c("subnum", "X26")

RM ANOVA

anova_power_20m <- ezANOVA(data = data.frame(power_20m), dv = .(voltage), wid = .(subnum), within = .(time, region, condition), type = 3, detailed = TRUE)

anova_power_20m %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	1375.376	291.044	94.513	0.000	*	0.655	0.825
time	3	60	15.207	73.126	4.159	0.010	*	0.021	0.172
region	2	40	59.714	165.422	7.220	0.002	*	0.076	0.265
condition	1	20	101.678	47.271	43.019	0.000	*	0.123	0.683
time:region	6	120	2.691	53.747	1.001	0.428		0.004	0.048
time:condition	3	60	1.722	33.237	1.036	0.383		0.002	0.049
region:condition	2	40	3.843	36.673	2.096	0.136		0.005	0.095
time:region:condition	6	120	0.694	25.228	0.550	0.769		0.001	0.027

	Effect	W	p	p<.05
2	time	0.760	0.400
3	region	0.925	0.476
5	time:region	0.136	0.019	*
6	time:condition	0.876	0.779
7	region:condition	0.660	0.019	*
8	time:region:condition	0.411	0.735

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.867	0.014	*	1.008	0.010	*
3	region	0.930	0.003	*	1.022	0.002	*
5	time:region	0.584	0.406		0.724	0.415
6	time:condition	0.928	0.380		1.093	0.383
7	region:condition	0.746	0.151		0.793	0.148
8	time:region:condition	0.769	0.724		1.028	0.769

Main effect of condition on theta power: F(1, 20) = 43.02, p < .001, \(\eta^2_p\) = .68
Theta power: time by condition interaction theta power: F(3, 60) = 1.04, p = .383, \(\eta^2_p\) = .05

ERPs - P1

Load the files with EEG data for statistics, written out from MATLAB

P1_10m_lPO <- read_tsv(file.path(data_dir_eeg, "P1", "10m_peak_ERPs_PO7P5P7_[110_180]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

P1_10m_rPO <- read_tsv(file.path(data_dir_eeg, "P1", "10m_peak_ERPs_PO8P6P8_[110_180]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

P1_20m_lPO <- read_tsv(file.path(data_dir_eeg, "P1", "20m_peak_ERPs_PO7P5P7_[110_180]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

P1_20m_rPO <- read_tsv(file.path(data_dir_eeg, "P1", "20m_peak_ERPs_PO8P6P8_[110_180]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

Correct rejections (10 minutes)

P1_10m <- full_join(P1_10m_lPO,P1_10m_rPO) %>%
  select(-X18,-ends_with("_time")) %>% # drop spurious and peak time columns
  gather(key = "key", value = "voltage", -subnum) %>%  # gather all columns with voltages
  separate(key, into = c("region","time"), extra = "drop", sep = "\\_Correct_rejection_|\\_t") %>% # split into region (2) and time (8)
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X18")

RM ANOVA

anova_P1_10m <- ezANOVA(data = data.frame(P1_10m), dv = .(voltage), wid = .(subnum), within = .(time, region), type = 3, detailed = TRUE)
anova_P1_10m %>%
  ezANOVA_pes() %>%
  kable()

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	278.878349	142.91960	39.0259077	0.0000042	*	0.4745800	0.6611657
time	7	140	3.513505	60.35357	1.1643071	0.3271770		0.0112516	0.0550128
region	1	20	47.393079	72.52800	13.0689061	0.0017267	*	0.1330718	0.3952023
time:region	7	140	0.468308	32.95234	0.2842336	0.9592468		0.0015145	0.0140125

	Effect	W	p	p<.05
2	time	0.0545227	0.0043692	*
4	time:region	0.1801815	0.3314069

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.5264141	0.3326329		0.6600623	0.3325955
4	time:region	0.7198980	0.9218219		0.9914061	0.9584455

Main effect of time on P1: F(3.68, 73.70) = 1.16, p = .333, \(\eta^2_p\) = .06
Main effect of hemisphere on P1: F(1, 20) = 13.07, p = .002, \(\eta^2_p\) = .40

Paired t-tests

P1_10m_6v7 <- paired_test_eeg(P1_10m)

print_paired_test_eeg(P1_10m_6v7, return_t = TRUE) %>%
  kable(digits = 3)

region	BF	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
PO7P5P7	0.496	-0.250	-1.338	0.196	20	-0.640	0.140	Paired t-test	two.sided	0.292
PO8P6P8	0.963	-0.283	-1.854	0.079	20	-0.601	0.035	Paired t-test	two.sided	0.405

print_paired_test_eeg(P1_10m_6v7, return_BF = TRUE) %>%
  kable(digits = 2)

region	BF
PO7P5P7	0.50
PO8P6P8	0.96

Hits and misses (20 minutes)

P1_20m <- full_join(P1_20m_lPO,P1_20m_rPO) %>%
  select(-X18,-ends_with("_time")) %>% 
  gather(key = "key", value = "voltage", -subnum) %>% # gather all columns with voltages
  separate(key, into = c("region","condition","time"), extra = "merge", sep = "\\s|\\_") %>% # split into region (2), condition (2) and time (4). Split after space or underscore
  mutate(time = str_replace(time,"_t.+","")) %>% # remove unnecessary info after underscore in time vector
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         condition = parse_factor(condition, levels = NULL),
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X18")

RM ANOVA

anova_P1_20m <- ezANOVA(data = data.frame(P1_20m), dv = .(voltage), wid = .(subnum), within = .(time, region, condition), type = 3, detailed = TRUE)
anova_P1_20m %>% 
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	260.018	159.027	32.701	0.000	*	0.344	0.621
time	3	60	3.865	85.910	0.900	0.447		0.008	0.043
region	1	20	35.928	52.503	13.686	0.001	*	0.067	0.406
condition	1	20	7.634	22.832	6.687	0.018	*	0.015	0.251
time:region	3	60	1.182	47.461	0.498	0.685		0.002	0.024
time:condition	3	60	0.871	53.399	0.326	0.806		0.002	0.016
region:condition	1	20	7.991	21.378	7.476	0.013	*	0.016	0.272
time:region:condition	3	60	1.434	54.184	0.529	0.664		0.003	0.026

	Effect	W	p	p<.05
2	time	0.523	0.033	*
5	time:region	0.726	0.308
6	time:condition	0.766	0.418
8	time:region:condition	0.710	0.270

	Effect	GGe	p[GG]	HFe	p[HF]
2	time	0.709	0.420	0.796	0.429
5	time:region	0.858	0.657	0.996	0.684
6	time:condition	0.839	0.771	0.969	0.800
8	time:region:condition	0.841	0.634	0.972	0.659

P1 amplitude: region by condition interaction: F(1, 20) = 7.48, p = .013, \(\eta^2_p\) = .27
P1 amplitude: time by region by condition interaction: F(3, 60) = 0.53, p = .664, \(\eta^2_p\) = .03

ERPs - N1

Load the files with EEG data for statistics, written out from MATLAB

N1_10m_lPO <- read_tsv(file.path(data_dir_eeg, "N1", "10m_peak_ERPs_PO7P5P7_[190_260]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

N1_10m_rPO <- read_tsv(file.path(data_dir_eeg, "N1", "10m_peak_ERPs_PO8P6P8_[190_260]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

N1_20m_lPO <- read_tsv(file.path(data_dir_eeg, "N1", "20m_peak_ERPs_PO7P5P7_[190_260]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

N1_20m_rPO <- read_tsv(file.path(data_dir_eeg, "N1", "20m_peak_ERPs_PO8P6P8_[190_260]ms_[1_1]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X18' [18]

Correct rejections (10 minutes)

N1_10m <- full_join(N1_10m_lPO,N1_10m_rPO) %>%
  select(-X18,-ends_with("_time")) %>% # drop spurious and peak time columns
  gather(key = "key", value = "voltage", -subnum) %>%  # gather all columns with voltages
  separate(key, into = c("region","time"), extra = "drop", sep = "\\_Correct_rejection_|\\_t") %>% # split into region (2) and time (8)
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X18")

RM ANOVA

anova_N1_10m <- ezANOVA(data = data.frame(N1_10m), dv = .(voltage), wid = .(subnum), within = .(time, region), type = 3, detailed = TRUE)
anova_N1_10m %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	491.592	677.571	14.510	0.001	*	0.294	0.420
time	7	140	23.605	81.507	5.792	0.000	*	0.020	0.225
region	1	20	287.641	376.592	15.276	0.001	*	0.196	0.433
time:region	7	140	9.247	46.226	4.001	0.001	*	0.008	0.167

	Effect	W	p	p<.05
2	time	0.017	0.000	*
4	time:region	0.078	0.021	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.412	0.002	*	0.490	0.001	*
4	time:region	0.620	0.004	*	0.814	0.001	*

Main effect of time on N1: F(2.89, 57.75) = 5.79, p = .002, \(\eta^2_p\) = .22
Main effect of hemiphere on N1: F(1, 20) = 15.28, p < .001, \(\eta^2_p\) = .43

Paired t-tests

N1_10m_6v7 <- paired_test_eeg(N1_10m)

print_paired_test_eeg(N1_10m_6v7, return_t = TRUE) %>%
  kable(digits = 3)

region	BF	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
PO7P5P7	0.290	0.126	0.734	0.472	20	-0.231	0.483	Paired t-test	two.sided	0.160
PO8P6P8	0.239	-0.052	-0.334	0.742	20	-0.380	0.275	Paired t-test	two.sided	0.073

print_paired_test_eeg(N1_10m_6v7, return_BF = TRUE) %>%
  kable(digits = 2)

region	BF
PO7P5P7	0.29
PO8P6P8	0.24

Difference in N1 between block 6 and 7 in left PO regions: t(20) = 0.73, p = .472, d = 0.16, BF₀₁ = 3.45
Difference in N1 between block 6 and 7 in right PO regions: t(20) = -0.33, p = .742, d = -0.07, BF₀₁ = 4.18

Hits and misses (20 minutes)

N1_20m <- full_join(N1_20m_lPO,N1_20m_rPO) %>%
  select(-X18,-ends_with("_time")) %>% # also drop correct rejections (if there)
  gather(key = "key", value = "voltage", -subnum) %>% # gather all columns with voltages
  separate(key, into = c("region","condition","time"), extra = "merge", sep = "\\s|\\_") %>% # split into region (2), condition (2) and time (4). Split after space or underscore
  mutate(time = str_replace(time,"_t.+","")) %>% # remove unnecessary info after underscore in time vector
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         region = parse_factor(region, levels = NULL), 
         condition = parse_factor(condition, levels = NULL),
         time = parse_factor(time, levels = NULL))

## Joining, by = c("subnum", "X18")

RM ANOVA

anova_N1_20m <- ezANOVA(data = data.frame(N1_20m), dv = .(voltage), wid = .(subnum), within = .(time, region, condition), type = 3, detailed = TRUE)
anova_N1_20m %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	609.706	607.240	20.081	0.000	*	0.299	0.501
time	3	60	9.817	86.333	2.274	0.089		0.007	0.102
region	1	20	362.837	347.975	20.854	0.000	*	0.203	0.510
condition	1	20	51.167	61.268	16.702	0.001	*	0.035	0.455
time:region	3	60	2.127	58.460	0.728	0.539		0.001	0.035
time:condition	3	60	1.840	106.349	0.346	0.792		0.001	0.017
region:condition	1	20	40.250	67.874	11.860	0.003	*	0.027	0.372
time:region:condition	3	60	7.896	92.973	1.699	0.177		0.005	0.078

	Effect	W	p
2	time	0.811	0.560
5	time:region	0.940	0.949
6	time:condition	0.626	0.119
8	time:region:condition	0.859	0.725

	Effect	GGe	p[GG]	HFe	p[HF]
2	time	0.892	0.097	1.042	0.089
5	time:region	0.963	0.535	1.143	0.539
6	time:condition	0.752	0.735	0.852	0.760
8	time:region:condition	0.912	0.182	1.071	0.177

N1 amplitude: hemisphere by condition interaction: F(1, 20) = 11.86, p = .003, \(\eta^2_p\) = .37
N1 amplitude: time by hemisphere by condition interaction: F(3, 60) = 1.70, p = .177, \(\eta^2_p\) = .08

Power lateralization - Alpha

Load the files with EEG data for statistics, written out from MATLAB

latindex_10m_onfile <- read_tsv(file.path(data_dir_eeg, "lat_index", "10m_average_latindex_PO8P6P8_[-1000_-100]ms_[9_14]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X10' [10]

latindex_20m_onfile <- read_tsv(file.path(data_dir_eeg, "lat_index", "20m_average_latindex_PO8P6P8_[-1000_-100]ms_[9_14]Hz.txt"), col_types = cols(.default = col_double(), subnum = col_integer()))

## Warning: Missing column names filled in: 'X10' [10]

Correct rejections (10 minutes)

latindex_10m <- latindex_10m_onfile %>%
  select(-X10) %>% # drop spurious column
  gather(key = "key", value = "voltage", -subnum) %>%  # gather all columns with voltages
  separate(key, into = c("region","time"), extra = "drop", sep = "\\_Correct_rejection_|\\_t") %>% # split into electrode pool (1) and time (8)
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         time = parse_factor(time, levels = NULL))

RM ANOVA

anova_latindex_10m <- ezANOVA(data = data.frame(latindex_10m), dv = .(voltage), wid = .(subnum), within = .(time), type = 3, detailed = TRUE)
anova_latindex_10m %>%  
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	0.820	4.186	3.916	0.062		0.157	0.164
time	7	140	0.011	0.231	0.938	0.479		0.002	0.045

	Effect	W	p	p<.05
2	time	0.029	0	*

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.503	0.438		0.624	0.452

Main effect of time on alpha lateralization: F(3.52, 70.42) = 0.94, p = .438, \(\eta^2_p\) = .04

Paired t-tests

6 vs. 7:

latindex_10m_6v7 <- paired_test_eeg(latindex_10m)

print_paired_test_eeg(latindex_10m_6v7, return_t = TRUE) %>%
  kable(digits = 3)

region	BF	estimate	statistic	p.value	parameter	conf.low	conf.high	method	alternative	d
PO8P6P8	0.229	0.001	0.101	0.92	20	-0.022	0.024	Paired t-test	two.sided	0.022

print_paired_test_eeg(latindex_10m_6v7, return_BF = TRUE) %>%
  kable(digits = 2)

region	BF
PO8P6P8	0.23

Difference in alpha lateralization between block 6 and 7: t(20) = 0.10, p = .920, d = 0.02, BF₀₁ = 4.38

Hits and misses (20 minutes)

latindex_20m <- latindex_20m_onfile %>%
  select(-X10) %>% # drop spurious column
  gather(key = "key", value = "voltage", -subnum) %>%  # gather all columns with voltages
  separate(key, into = c("region","condition","time"), extra = "merge", sep = "_") %>% # split into region (1), condition (2) and time (4). Split after underscore (but stop after splitting into 3 columns)
  mutate(time = str_replace(time,"_t.+","")) %>%# remove unnecessary info after last underscore in time vector
  mutate(subnum = parse_factor(subnum, levels = NULL), # make into factors for statistical analysis
         condition = parse_factor(condition, levels = NULL), 
         time = parse_factor(time, levels = NULL))

RM ANOVA

anova_latindex_20m <- ezANOVA(data = data.frame(latindex_20m), dv = .(voltage), wid = .(subnum), within = .(time, condition), type = 3, detailed = TRUE)
anova_latindex_20m %>%
  ezANOVA_pes() %>%
  kable(digits = 3)

Effect	DFn	DFd	SSn	SSd	F	p	p<.05	ges	pes
(Intercept)	1	20	0.862	4.273	4.035	0.058		0.153	0.168
time	3	60	0.004	0.272	0.283	0.838		0.001	0.014
condition	1	20	0.048	0.076	12.676	0.002	*	0.010	0.388
time:condition	3	60	0.004	0.169	0.465	0.708		0.001	0.023

	Effect	W	p	p<.05
2	time	0.782	0.468
4	time:condition	0.848	0.688

	Effect	GGe	p[GG]	p[GG]<.05	HFe	p[HF]	p[HF]<.05
2	time	0.868	0.810		1.009	0.838
4	time:condition	0.893	0.687		1.044	0.708

Main effect of condition on alpha lateralization: F(1, 20) = 12.68, p = .002, \(\eta^2_p\) = .39
time by condition interaction alpha lateralization: F(3, 60) = 0.46, p = .708, \(\eta^2_p\) = .02

MFBrain

Leon Reteig

Behavioral data per trial

Load

Clean

Response time data

RM ANOVA

Follow-up tests

Signal detection data (Hit rate, False alarm rate, A’)

Sensitivity (A’)

RM ANOVA

Paired t-tests

Hit rate

RM ANOVA

Paired t-tests

False alarm rate

RM ANOVA

Paired t-tests

Ratings data

Load

Clean

Motivation

RM ANOVA

Paired t-tests

Aversion

RM ANOVA

Paired t-tests

EEG data

Theta - ITPC

Correct rejections (10 minutes)

RM ANOVA

Paired t-tests

Multilevel model

Hits and misses (20 minutes)

RM ANOVA

Theta - Power

Correct rejections (10 minutes)

RM ANOVA

Paired t-tests

Hits and misses (20 minutes)

RM ANOVA

ERPs - P1

Correct rejections (10 minutes)

RM ANOVA

Paired t-tests

Hits and misses (20 minutes)

RM ANOVA

ERPs - N1

Correct rejections (10 minutes)

RM ANOVA

Paired t-tests

Hits and misses (20 minutes)

RM ANOVA

Power lateralization - Alpha

Correct rejections (10 minutes)

RM ANOVA

Paired t-tests

Hits and misses (20 minutes)

RM ANOVA