recode load

During data collection (and preregistration), we referred to four conditions:
no, low, medium, and high.

Pregistration:
https://doi.org/10.17605/OSF.IO/JVMFD

During data processing, we decided to rename the conditions to keep the labeling consistent between the studies.

no: no
lo: low
hi: high (was medium)
vh: very high (was high)

tmpaEEG=read.csv("data_audio_signal_noise.csv", sep="\t",header=TRUE, dec=".");
#head(tmpaEEG)

# The data in each 3-min block are analyzed by minute. For each minute, the amp and itc are computed and then averaged across the four blocks of load.
tmpa2EEG=read.csv("data_audio_signal_noise_by_minute.csv", sep="\t",header=TRUE, dec=".");
#head(tmpa3EEG)

# Analysis of visual P3, as in our MMN paper (2019)
tmpvEEG=read.csv("Data_visualP3.csv", sep="\t",header=TRUE, dec=".");
#head(tmpwmc)

tmpbeh=read.csv("data_logfiles.txt", sep="\t",header=TRUE, dec=".");
#head(tmpbeh)

tmpnasa=read.csv("data_NASA.txt", sep="\t",header=TRUE, dec=".");
#head(tmpnasa)

tmpwmc=read.csv("data_wmc.txt", sep="\t",header=TRUE, dec=".");
tmpwmc=tmpwmc[tmpwmc$fp > 3,]
#head(tmpwmc)

D=merge(tmpbeh, tmpwmc, by = "fp", all.x = TRUE)
D=merge(D, tmpaEEG, by = "fp")
D=merge(D, tmpa2EEG, by = "fp")
D=merge(D, tmpnasa, by = "fp")
D=merge(D, tmpvEEG, by = "fp", all.x = TRUE)
rm(list=ls(pattern="tmp"))

if (any(as.numeric(table(D$fp))!=1)){
  stop("Subject numbers are not unique!")
  }

quick data check

Sample size: N = 45.

Sanity check for behavioral performance (across low, high, and very high load):

min number of hits (could be 0) : 96
max number of hits (could be 288) : 288
min number of false alarms (could be 0) : 0
max number of false alarms (could be 892) : 144

Note that hits are correct responses between 200 and 1000 ms after target onset (defined in ASSR_study2_process_beh.R)
False alarms are responses at other times: 1152 + 7*4 (initial trials) - 288 (trial after a target) = 892.

# add 0.5 and 1 to avoid ceiling/floor effects
D$HRlocorr = (D$nHRlo + 0.5)/(288+1)
D$HRhicorr = (D$nHRhi + 0.5)/(288+1)
D$HRvhcorr = (D$nHRvh + 0.5)/(288+1)
# 72*4 = 288
D$FAlocorr = (D$nFAlo + 0.5)/(1180+1-288)
D$FAhicorr = (D$nFAhi + 0.5)/(1180+1-288)
D$FAvhcorr = (D$nFAvh + 0.5)/(1180+1-288)
# 288*4 = 1152 + 7*4 (initial trials1) = 1180
# do not count the next trial after a target (because this is considered to be a valid response)
# Dprime
D$dprlo <- qnorm(D$HRlocorr) - qnorm(D$FAlocorr)
D$dprhi <- qnorm(D$HRhicorr) - qnorm(D$FAhicorr)
D$dprvh <- qnorm(D$HRvhcorr) - qnorm(D$FAvhcorr)
# no need for criterion
#tmp$criterion <- - (1/2) * (qnorm(tmp$aware_crit_corrected) + qnorm(tmp$aware_catch_corrected))
D$dprloMhi = D$dprlo - D$dprhi
D$dprloMvh = D$dprlo - D$dprvh
D$dprhiMvh = D$dprhi - D$dprvh
# RT to hits
D$meanRTloMhi = D$meanRTmslo - D$meanRTmshi
D$meanRTloMvh = D$meanRTmslo - D$meanRTmsvh
D$meanRThiMvh = D$meanRTmshi - D$meanRTmsvh

# the numbers 1 to 4 refer to different blocks

# Amp SNR raw
D$AmpSNRno = rowMeans(cbind(D$AmpSNRno1,D$AmpSNRno2,D$AmpSNRno3,D$AmpSNRno4))
D$AmpSNRlo = rowMeans(cbind(D$AmpSNRlo1,D$AmpSNRlo2,D$AmpSNRlo3,D$AmpSNRlo4))
D$AmpSNRhi = rowMeans(cbind(D$AmpSNRhi1,D$AmpSNRhi2,D$AmpSNRhi3,D$AmpSNRhi4))
D$AmpSNRvh = rowMeans(cbind(D$AmpSNRvh1,D$AmpSNRvh2,D$AmpSNRvh3,D$AmpSNRvh4))

# Amp S raw
D$AmpSno = rowMeans(cbind(D$AmpSno1,D$AmpSno2,D$AmpSno3,D$AmpSno4))
D$AmpSlo = rowMeans(cbind(D$AmpSlo1,D$AmpSlo2,D$AmpSlo3,D$AmpSlo4))
D$AmpShi = rowMeans(cbind(D$AmpShi1,D$AmpShi2,D$AmpShi3,D$AmpShi4))
D$AmpSvh = rowMeans(cbind(D$AmpSvh1,D$AmpSvh2,D$AmpSvh3,D$AmpSvh4))

# Amp N raw
D$AmpNno = rowMeans(cbind(D$AmpNno1,D$AmpNno2,D$AmpNno3,D$AmpNno4))
D$AmpNlo = rowMeans(cbind(D$AmpNlo1,D$AmpNlo2,D$AmpNlo3,D$AmpNlo4))
D$AmpNhi = rowMeans(cbind(D$AmpNhi1,D$AmpNhi2,D$AmpNhi3,D$AmpNhi4))
D$AmpNvh = rowMeans(cbind(D$AmpNvh1,D$AmpNvh2,D$AmpNvh3,D$AmpNvh4))

# Amp S-N raw
D$AmpSmNno = rowMeans(cbind(D$AmpSmNno1,D$AmpSmNno2,D$AmpSmNno3,D$AmpSmNno4))
D$AmpSmNlo = rowMeans(cbind(D$AmpSmNlo1,D$AmpSmNlo2,D$AmpSmNlo3,D$AmpSmNlo4))
D$AmpSmNhi = rowMeans(cbind(D$AmpSmNhi1,D$AmpSmNhi2,D$AmpSmNhi3,D$AmpSmNhi4))
D$AmpSmNvh = rowMeans(cbind(D$AmpSmNvh1,D$AmpSmNvh2,D$AmpSmNvh3,D$AmpSmNvh4))

# the numbers 1 to 4 refer to different blocks

# Itc SNR raw
D$ItcSNRno = rowMeans(cbind(D$ItcSNRno1,D$ItcSNRno2,D$ItcSNRno3,D$ItcSNRno4))
D$ItcSNRlo = rowMeans(cbind(D$ItcSNRlo1,D$ItcSNRlo2,D$ItcSNRlo3,D$ItcSNRlo4))
D$ItcSNRhi = rowMeans(cbind(D$ItcSNRhi1,D$ItcSNRhi2,D$ItcSNRhi3,D$ItcSNRhi4))
D$ItcSNRvh = rowMeans(cbind(D$ItcSNRvh1,D$ItcSNRvh2,D$ItcSNRvh3,D$ItcSNRvh4))

# Itc S raw
D$ItcSno = rowMeans(cbind(D$ItcSno1,D$ItcSno2,D$ItcSno3,D$ItcSno4))
D$ItcSlo = rowMeans(cbind(D$ItcSlo1,D$ItcSlo2,D$ItcSlo3,D$ItcSlo4))
D$ItcShi = rowMeans(cbind(D$ItcShi1,D$ItcShi2,D$ItcShi3,D$ItcShi4))
D$ItcSvh = rowMeans(cbind(D$ItcSvh1,D$ItcSvh2,D$ItcSvh3,D$ItcSvh4))

# Itc N raw
D$ItcNno = rowMeans(cbind(D$ItcNno1,D$ItcNno2,D$ItcNno3,D$ItcNno4))
D$ItcNlo = rowMeans(cbind(D$ItcNlo1,D$ItcNlo2,D$ItcNlo3,D$ItcNlo4))
D$ItcNhi = rowMeans(cbind(D$ItcNhi1,D$ItcNhi2,D$ItcNhi3,D$ItcNhi4))
D$ItcNvh = rowMeans(cbind(D$ItcNvh1,D$ItcNvh2,D$ItcNvh3,D$ItcNvh4))

# Itc S-N raw
D$ItcSmNno = rowMeans(cbind(D$ItcSmNno1,D$ItcSmNno2,D$ItcSmNno3,D$ItcSmNno4))
D$ItcSmNlo = rowMeans(cbind(D$ItcSmNlo1,D$ItcSmNlo2,D$ItcSmNlo3,D$ItcSmNlo4))
D$ItcSmNhi = rowMeans(cbind(D$ItcSmNhi1,D$ItcSmNhi2,D$ItcSmNhi3,D$ItcSmNhi4))
D$ItcSmNvh = rowMeans(cbind(D$ItcSmNvh1,D$ItcSmNvh2,D$ItcSmNvh3,D$ItcSmNvh4))

# visual P3 is difference between targets and nontargets.
# the numbers 1 to 4 refer to different blocks

D$vP3lo1 = D$vP3_Tarlo1- D$vP3_Nonlo1
D$vP3lo2 = D$vP3_Tarlo2- D$vP3_Nonlo2
D$vP3lo3 = D$vP3_Tarlo3- D$vP3_Nonlo3
D$vP3lo4 = D$vP3_Tarlo4- D$vP3_Nonlo4

D$vP3hi1 = D$vP3_Tarhi1- D$vP3_Nonhi1
D$vP3hi2 = D$vP3_Tarhi2- D$vP3_Nonhi2
D$vP3hi3 = D$vP3_Tarhi3- D$vP3_Nonhi3
D$vP3hi4 = D$vP3_Tarhi4- D$vP3_Nonhi4

D$vP3vh1 = D$vP3_Tarvh1- D$vP3_Nonvh1
D$vP3vh2 = D$vP3_Tarvh2- D$vP3_Nonvh2
D$vP3vh3 = D$vP3_Tarvh3- D$vP3_Nonvh3
D$vP3vh4 = D$vP3_Tarvh4- D$vP3_Nonvh4

D$vP3lo = rowMeans(cbind(D$vP3lo1, D$vP3lo2, D$vP3lo3, D$vP3lo4))
D$vP3hi = rowMeans(cbind(D$vP3hi1, D$vP3hi2, D$vP3hi3, D$vP3hi4))
D$vP3vh = rowMeans(cbind(D$vP3vh1, D$vP3vh2, D$vP3vh3, D$vP3vh4))
D$vP3loMhi = D$vP3lo - D$vP3hi
D$vP3loMvh = D$vP3lo - D$vP3vh
D$vP3hiMvh = D$vP3hi - D$vP3vh

hit rates

Simple hit rates (%) were as follows:

low load = 93.4
high load = 70.01
very high load = 78.16

table

Conditions are no, lo, hi, vh (very high)
Differences: loMhi refers to lo minus hi
dpr: signal detection index d´
RTms: reaction time to hits (in ms)
pcu: partial credit unit score of working memory capacity
vP3: visual P3 to targets (vs. nontargets)
LL and UL refer to the 95% confidence interval (from two-tailed t tests)

Dtmp = subset(D, select=c(dprlo, dprhi, dprvh, 
                          dprloMhi, dprloMvh, dprhiMvh,
                          meanRTmslo, meanRTmshi, meanRTmsvh,
                          meanRTloMhi, meanRTloMvh, meanRThiMvh,
                          pcu,
                          vP3lo, vP3hi, vP3vh,
                          vP3loMhi, vP3loMvh, vP3hiMvh))

RmCI = tableCIs(Dtmp)
RmCI %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Variable	Mean	LL	UL	N
dprlo	4.249	3.951	4.546	45
dprhi	2.177	1.982	2.372	45
dprvh	2.707	2.517	2.896	45
dprloMhi	2.072	1.890	2.253	45
dprloMvh	1.542	1.343	1.741	45
dprhiMvh	-0.530	-0.638	-0.422	45
meanRTmslo	381.114	369.712	392.517	45
meanRTmshi	500.728	487.701	513.755	45
meanRTmsvh	460.841	448.851	472.832	45
meanRTloMhi	-119.614	-129.930	-109.298	45
meanRTloMvh	-79.727	-87.900	-71.554	45
meanRThiMvh	39.887	31.211	48.563	45
pcu	0.772	0.728	0.816	40
vP3lo	6.912	5.225	8.599	42
vP3hi	2.108	1.097	3.118	42
vP3vh	3.862	2.563	5.161	42
vP3loMhi	4.804	3.524	6.084	42
vP3loMvh	3.050	2.089	4.010	42
vP3hiMvh	-1.754	-2.475	-1.034	42

write.table(RmCI, file = file.path(dir_fig, paste0("table_meansCI.tsv")), row.names = F, dec = ".", sep = "\t")

plot task performance

Dtmp = subset(D, select=c(dprlo, dprhi, dprvh, dprloMhi, dprloMvh, dprhiMvh))
RmCI = tableCIs(Dtmp)
#RmCI
Ddpr = RmCI[1:3,]
Dtmp = cbind(D$fp, Dtmp)
Dtmp = na.omit(Dtmp)
colnames(Dtmp) = c('fp','low','high','very high','loMhi','loMvh','hiMvh')
bp1 = plotoverlay(dm = Dtmp, lbl = "d´", dvy = "Sensitivity (d´)")

Dtmp = subset(D, select=c(meanRTmslo, meanRTmshi, meanRTmsvh, meanRTloMhi, meanRTloMvh, meanRThiMvh))
RmCI = tableCIs(Dtmp)
#RmCI
DRT = RmCI[1:3,1:4]

Dtmp = cbind(D$fp, Dtmp)
Dtmp = na.omit(Dtmp)
colnames(Dtmp) = c('fp','low','high','very high','loMhi','loMvh','hiMvh')
bp2 = plotoverlay(dm = Dtmp, lbl = "RT", dvy = "RT (ms)")
grid.arrange(bp1, bp2, ncol = 2)

Ddpr$Condition = c("low", "high", "very high")
DRT$Condition = c("low", "high", "very high")
Ddpr$Condition = factor(Ddpr$Condition, levels=c("low", "high", "very high"))
DRT$Condition = factor(DRT$Condition, levels=c("low", "high", "very high"))
fig_dpr = ggplot(Ddpr, aes(x=Condition, y=Mean, fill=Condition)) + 
  geom_bar(stat="identity"
          ,color="black" # add black border to each bar
          ,position=position_dodge()) + # separare bars
  theme_bw() + # get rid of background
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) + #no grid lines
  geom_errorbar(position=position_dodge(.9), width=.25, 
                aes(ymin=LL, ymax=UL)) + 
  scale_fill_manual(
        values = c("#999999", "#CCCCCC", "#FFFFFF"),
        name = "Condition",
        labels = c("low","high","very high")) +
  labs(title = paste0("d´(N = ", nrow(D),")")) +
    theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Condition") +
  labs(y = "Sensitivity (d´)") +
  labs(legend = "Condition")
fig_RT = ggplot(DRT, aes(x=Condition, y=Mean, fill=Condition)) + 
  geom_bar(stat="identity"
          ,color="black" # add black border to each bar
          ,position=position_dodge()) + # separare bars
  theme_bw() + # get rid of background
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) + #no grid lines
  geom_errorbar(position=position_dodge(.9), width=.25, 
                aes(ymin=LL, ymax=UL)) + 
  scale_fill_manual(
        values = c("#999999", "#CCCCCC", "#FFFFFF"),
        name = "Condition",
        labels = c("low","high","very high")) +
  labs(title = paste0("RT (N = ", nrow(D),")")) +
    theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Condition") +
  labs(y = "Reaction time (ms)") +
  labs(legend = "Condition")
grid.arrange(fig_dpr, fig_RT, ncol = 2)

bp1 = arrangeGrob(fig_dpr, fig_RT, ncol = 2)
ggsave(file.path(dir_fig, paste0("figure_meansCI_dprime.pdf")), bp1, dpi = 600, width = 7, height = 4)
rm(Ddpr, DRT, fig_dpr, fig_RT, bp1)

NASA-TLX ratings

Dnasa = data.frame()

mental

c = 'Mental'
Dnasa = plotNASA(Dnasa)

Mental	Mean	LL	UL	N
no	20.922	14.969	26.876	45
lo	34.606	28.841	40.370	45
hi	51.678	45.003	58.352	45
vh	46.367	40.165	52.568	45
noMlo	-13.683	-18.609	-8.758	45
noMhi	-30.756	-37.870	-23.641	45
noMvh	-25.444	-31.714	-19.175	45
loMhi	-17.072	-22.468	-11.677	45
loMvh	-11.761	-16.116	-7.407	45
hiMvh	5.311	1.957	8.665	45

physical

c = 'Physical'
Dnasa = plotNASA(Dnasa)

Physical	Mean	LL	UL	N
no	17.017	11.599	22.435	45
lo	22.767	16.608	28.925	45
hi	28.444	20.947	35.942	45
vh	26.539	19.223	33.855	45
noMlo	-5.750	-8.409	-3.091	45
noMhi	-11.428	-16.198	-6.657	45
noMvh	-9.522	-14.005	-5.040	45
loMhi	-5.678	-9.191	-2.165	45
loMvh	-3.772	-6.997	-0.547	45
hiMvh	1.906	-0.517	4.328	45

temporal

c = 'Temporal'
Dnasa = plotNASA(Dnasa)

Temporal	Mean	LL	UL	N
no	22.417	16.423	28.411	45
lo	37.222	31.753	42.692	45
hi	52.206	45.036	59.375	45
vh	46.239	39.664	52.814	45
noMlo	-14.806	-19.388	-10.223	45
noMhi	-29.789	-36.854	-22.724	45
noMvh	-23.822	-29.969	-17.676	45
loMhi	-14.983	-19.364	-10.602	45
loMvh	-9.017	-12.388	-5.645	45
hiMvh	5.967	3.247	8.686	45

performance

c = 'Performance'
Dnasa = plotNASA(Dnasa)

Performance	Mean	LL	UL	N
no	69.422	61.773	77.072	45
lo	61.094	54.059	68.130	45
hi	36.006	29.508	42.503	45
vh	44.772	37.847	51.698	45
noMlo	8.328	1.480	15.175	45
noMhi	33.417	25.166	41.667	45
noMvh	24.650	17.376	31.924	45
loMhi	25.089	20.512	29.666	45
loMvh	16.322	12.710	19.934	45
hiMvh	-8.767	-11.935	-5.598	45

effort

c = 'Effort'
Dnasa = plotNASA(Dnasa)

Effort	Mean	LL	UL	N
no	21.028	15.808	26.248	45
lo	38.539	33.064	44.013	45
hi	51.133	44.820	57.447	45
vh	48.306	42.149	54.463	45
noMlo	-17.511	-22.687	-12.335	45
noMhi	-30.106	-37.123	-23.088	45
noMvh	-27.278	-33.507	-21.049	45
loMhi	-12.594	-17.061	-8.128	45
loMvh	-9.767	-13.139	-6.394	45
hiMvh	2.828	-0.370	6.026	45

frustration

c = 'Frustration'
Dnasa = plotNASA(Dnasa)

Frustration	Mean	LL	UL	N
no	17.300	12.054	22.546	45
lo	22.678	15.929	29.427	45
hi	34.767	26.629	42.904	45
vh	29.722	21.961	37.484	45
noMlo	-5.378	-9.127	-1.629	45
noMhi	-17.467	-23.720	-11.213	45
noMvh	-12.422	-17.634	-7.210	45
loMhi	-12.089	-16.329	-7.849	45
loMvh	-7.044	-9.857	-4.232	45
hiMvh	5.044	2.116	7.973	45

colnames(Dnasa)[1] = 'Condition'
colnames(Dnasa)[6] = 'Variable'
Dnasa$Condition = factor(Dnasa$Condition, levels=c("no","lo","hi","vh"))
Dnasa$Variable = factor(Dnasa$Variable, levels=c("Mental", "Physical", "Temporal", "Performance", "Effort", "Frustration"))

all ratings

fig_mean = ggplot(Dnasa, aes(x=Variable, y=Mean, fill=Condition)) + 
  geom_bar(stat="identity"
          ,color="black" # add black border to each bar
          ,position=position_dodge()) + # separare bars
  theme_bw() + # get rid of background
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) + #no grid lines
  geom_errorbar(position=position_dodge(.9), width=.25, 
                aes(ymin=LL, ymax=UL)) + 
  scale_fill_manual(
        values = c("#000000", "#999999", "#CCCCCC", "#FFFFFF"),
        name = "Condition",
        labels = c("no","low","high","very high")) +
  labs(title = paste0("NASA-TLX ratings (N = ", nrow(D),")")) +
    theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Item") +
  labs(y = "Mean CR100 rating (95% CIs)") +
  labs(legend = "Load")
plot(fig_mean)

ggsave(file.path(dir_fig, paste0("figure_NASA_ratings.pdf")), width = 8, height = 6, dpi = 600)
rm(Dnasa)

visual P3

The sample lacks three subjects for visual P3 because of equipment failure: The visual onsets were not recorded.

Dtmp = subset(D, select=c(vP3lo, vP3hi, vP3vh, vP3loMhi, vP3loMvh, vP3hiMvh))
RmCI = tableCIs(Dtmp)
Dtmp = cbind(D$fp, Dtmp)
Dtmp = na.omit(Dtmp)
colnames(Dtmp) = c('fp','low','high','very high','loMhi','loMvh','hiMvh')
bp1 = plotoverlay(dm = Dtmp,lbl = "Visual P3", dvy = "Mean amplitude (µV)")
plot(bp1)

RmCI %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Variable	Mean	LL	UL	N
vP3lo	6.912	5.225	8.599	42
vP3hi	2.108	1.097	3.118	42
vP3vh	3.862	2.563	5.161	42
vP3loMhi	4.804	3.524	6.084	42
vP3loMvh	3.050	2.089	4.010	42
vP3hiMvh	-1.754	-2.475	-1.034	42

Bayes factors for load

Bayes factor (BF) analyses from Bayesian one-sample t tests of difference scores (e.g., no minus high load). Although the focus is on signal-minus-noise (SmN), the output includes SNR (signal-to-noise ratio) and S (signal).

The BFs were computed with Aladins R script.
https://doi.org/10.17045/sthlmuni.4981154.v3

The BF01 uses uniform H1 models with different lower limits (LL) and upper limits (UL).
BF01 is the evidence for the null hypothesis relative to the alternative hypothesis.
If BF01 > 3, this is evidence for the null.
If BF01 < 1/3, this is evidence against the null.

amplitude SmN

iv = "AmpSmN"
Dtmp = getdiffs(D, iv = iv)
Dtmp = tableCIs(Dtmp[,-c(1:4)])
Dtmp %>%
  kable(digits = 3, caption = "Descriptives") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Descriptives
Variable	Mean	LL	UL	N
no	0.243	0.210	0.275	45
lo	0.244	0.212	0.275	45
hi	0.243	0.211	0.275	45
vh	0.242	0.210	0.274	45
noMlo	-0.001	-0.012	0.010	45
noMhi	0.000	-0.013	0.012	45
noMvh	0.000	-0.013	0.013	45
loMhi	0.000	-0.012	0.013	45
loMvh	0.001	-0.012	0.014	45
hiMvh	0.001	-0.011	0.012	45

Dtmp = contrastBFs(D, iv = iv)
colnames(Dtmp) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
Dtmp %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
noMlo	-0.001	144.1	152.2	33.5
noMhi	0.000	127.0	125.7	27.2
noMvh	0.000	123.6	113.6	24.4
loMhi	0.000	123.8	110.8	23.7
loMvh	0.001	123.5	103.3	22.0
hiMvh	0.001	142.6	122.7	26.4

BFamp = Dtmp[Dtmp$Variable %in% c("noMlo","noMhi","loMhi"),]

amplitude SNR

iv = "AmpSNR"
Dtmp = getdiffs(D, iv = iv)
Dtmp = tableCIs(Dtmp[,-c(1:4)])
Dtmp %>%
  kable(digits = 3, caption = "Descriptives") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Descriptives
Variable	Mean	LL	UL	N
no	6.609	5.643	7.575	45
lo	6.742	5.723	7.761	45
hi	6.746	5.766	7.727	45
vh	6.805	5.749	7.861	45
noMlo	-0.133	-0.559	0.292	45
noMhi	-0.138	-0.503	0.227	45
noMvh	-0.196	-0.548	0.156	45
loMhi	-0.004	-0.399	0.391	45
loMvh	-0.062	-0.465	0.340	45
hiMvh	-0.058	-0.376	0.259	45

Dtmp = contrastBFs(D, iv = iv)
colnames(Dtmp) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
Dtmp %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
noMlo	-0.134	3.1	5.8	1.5
noMhi	-0.138	3.3	7.2	1.7
noMvh	-0.196	2.4	9.0	2.0
loMhi	-0.004	4.0	4.1	1.2
loMvh	-0.062	3.8	5.0	1.3
hiMvh	-0.058	4.7	6.6	1.6

amplitude S

iv = "AmpS"
Dtmp = getdiffs(D, iv = iv)
Dtmp = tableCIs(Dtmp[,-c(1:4)])
Dtmp %>%
  kable(digits = 3, caption = "Descriptives") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Descriptives
Variable	Mean	LL	UL	N
no	0.297	0.265	0.329	45
lo	0.295	0.263	0.326	45
hi	0.294	0.263	0.325	45
vh	0.293	0.261	0.324	45
noMlo	0.003	-0.007	0.012	45
noMhi	0.003	-0.007	0.014	45
noMvh	0.005	-0.006	0.015	45
loMhi	0.001	-0.012	0.013	45
loMvh	0.002	-0.010	0.014	45
hiMvh	0.002	-0.009	0.012	45

Dtmp = contrastBFs(D, iv = iv)
colnames(Dtmp) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
Dtmp %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
noMlo	0.003	143.1	95.7	20.3
noMhi	0.003	126.1	83.2	17.5
noMvh	0.005	100.8	60.0	12.4
loMhi	0.000	129.0	114.2	24.5
loMvh	0.002	128.7	96.2	20.4
hiMvh	0.002	145.9	112.0	24.0

ITC SmN

iv = "ItcSmN"
Dtmp = getdiffs(D, iv = iv)
Dtmp = tableCIs(Dtmp[,-c(1:4)])
Dtmp %>%
  kable(digits = 3, caption = "Descriptives") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Descriptives
Variable	Mean	LL	UL	N
no	0.368	0.323	0.413	45
lo	0.373	0.327	0.419	45
hi	0.364	0.317	0.411	45
vh	0.361	0.315	0.408	45
noMlo	-0.004	-0.021	0.012	45
noMhi	0.005	-0.015	0.024	45
noMvh	0.007	-0.008	0.022	45
loMhi	0.009	-0.009	0.027	45
loMvh	0.011	-0.006	0.028	45
hiMvh	0.002	-0.012	0.017	45

Dtmp = contrastBFs(D, iv = iv)
colnames(Dtmp) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
Dtmp %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
noMlo	-0.004	84.5	135.0	29.4
noMhi	0.004	73.5	52.7	10.9
noMvh	0.007	68.4	40.8	8.3
loMhi	0.009	53.4	31.5	6.4
loMvh	0.011	37.5	20.4	4.1
hiMvh	0.002	104.5	79.2	16.6

BFitc = Dtmp[Dtmp$Variable %in% c("noMlo","noMhi","loMhi"),]

ITC SNR

iv = "ItcSNR"
Dtmp = getdiffs(D, iv = iv)
Dtmp = tableCIs(Dtmp[,-c(1:4)])
Dtmp %>%
  kable(digits = 3, caption = "Descriptives") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Descriptives
Variable	Mean	LL	UL	N
no	5.676	5.101	6.251	45
lo	5.632	5.055	6.209	45
hi	5.661	5.063	6.260	45
vh	5.605	5.003	6.208	45
noMlo	0.043	-0.175	0.262	45
noMhi	0.014	-0.246	0.274	45
noMvh	0.070	-0.151	0.292	45
loMhi	-0.029	-0.294	0.236	45
loMvh	0.027	-0.199	0.253	45
hiMvh	0.056	-0.162	0.275	45

Dtmp = contrastBFs(D, iv = iv)
colnames(Dtmp) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
Dtmp %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
noMlo	0.043	6.8	5.1	1.2
noMhi	0.014	6.1	5.6	1.3
noMvh	0.070	5.9	4.0	1.0
loMhi	-0.029	5.9	7.1	1.6
loMvh	0.027	6.9	5.8	1.3
hiMvh	0.056	6.4	4.6	1.1

ITC S

iv = "ItcS"
Dtmp = getdiffs(D, iv = iv)
Dtmp = tableCIs(Dtmp[,-c(1:4)])
Dtmp %>%
  kable(digits = 3, caption = "Descriptives") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Descriptives
Variable	Mean	LL	UL	N
no	0.448	0.403	0.493	45
lo	0.454	0.408	0.500	45
hi	0.443	0.396	0.490	45
vh	0.441	0.395	0.488	45
noMlo	-0.006	-0.022	0.011	45
noMhi	0.005	-0.015	0.025	45
noMvh	0.007	-0.008	0.022	45
loMhi	0.011	-0.007	0.029	45
loMvh	0.013	-0.004	0.030	45
hiMvh	0.002	-0.012	0.016	45

Dtmp = contrastBFs(D, iv = iv)
colnames(Dtmp) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
Dtmp %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
noMlo	-0.006	75.8	141.2	30.9
noMhi	0.005	69.8	48.4	10.0
noMvh	0.007	67.0	39.7	8.1
loMhi	0.011	42.3	23.7	4.8
loMvh	0.013	30.5	16.3	3.3
hiMvh	0.002	108.2	86.0	18.1

BF only for SmN

RBF = rbind(BFamp, BFitc)
Vlabels = c('Amp_noMlo', 'Amp_noMhi', 'Amp_loMhi','ITC_noMlo', 'ITC_noMhi', 'ITC_loMhi')
RBF = data.frame(Vlabels, RBF, row.names = NULL)
RBF = RBF[,-2]
colnames(RBF) = c('Variable','Mean','[-1, +1]','[0, +1]','[0, +0.2]')
RBF %>%
  kable(digits = 3, caption = "BF results") %>%
  add_header_above(c(" " = 2, "BF01" = 3)) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

BF results
		BF01
Variable	Mean	[-1, +1]	[0, +1]	[0, +0.2]
Amp_noMlo	-0.001	144.1	152.2	33.5
Amp_noMhi	0.000	127.0	125.7	27.2
Amp_loMhi	0.000	123.8	110.8	23.7
ITC_noMlo	-0.004	84.5	135.0	29.4
ITC_noMhi	0.004	73.5	52.7	10.9
ITC_loMhi	0.009	53.4	31.5	6.4

write.table(RBF, file = file.path(dir_fig, paste0("table_SmN_BF.tsv")), row.names = F, dec = ".", sep = "\t")

plot amp and ITC

iv = "AmpSmN"
Dtmp = getdiffs(D, iv = iv)
Dtmp = Dtmp[,c(5:8,10)]
Dtmp = cbind(D$fp, Dtmp)
colnames(Dtmp)[1] = 'fp'
bp1 = plotoverlay(dm = Dtmp,lbl = paste0("Amplitude SmN"), dvy = "Mean amplitude (µV)")

iv = "ItcSmN"
Dtmp = getdiffs(D, iv = iv)
Dtmp = Dtmp[,c(5:8,10)]
Dtmp = cbind(D$fp, Dtmp)
colnames(Dtmp)[1] = 'fp'
bp2 = plotoverlay(dm = Dtmp,lbl = paste0("ITC SmN"), dvy = "Mean ITC")
grid.arrange(bp1, bp2, ncol = 2)

bp3 = arrangeGrob(bp1, bp2, ncol = 2)
ggsave(file.path(dir_fig, paste0("figure_SmN_meansCI_amp_itc.pdf")), bp3, dpi = 600, width = 7, height = 4)
rm(bp1, bp2, bp3)

correlations with behavior

Data exploration (fishing) suggests that across subjects, an RT increase from low to high load correlated with a decrease in visual P3 (r = -0.366). Suggests also that an increase in mental NASA rating from low to high load correlated with a d´ decrease (r = -0.335).

table

Dtmp = subset(D, select=c(dprloMhi, meanRTloMhi))
Dtmp$MentalloMhi = D$Mentallo - D$Mentalhi
Dtmp$vP3loMhi = D$vP3loMhi
Dtmp$AmploMhi = D$AmpSmNlo - D$AmpSmNhi
Dtmp$ItcloMhi = D$ItcSmNlo - D$ItcSmNhi
Dtmp = na.omit(Dtmp)
cor(Dtmp) %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

	dprloMhi	meanRTloMhi	MentalloMhi	vP3loMhi	AmploMhi	ItcloMhi
dprloMhi	1.000	-0.147	-0.335	0.235	0.029	-0.005
meanRTloMhi	-0.147	1.000	0.117	-0.367	-0.256	-0.114
MentalloMhi	-0.335	0.117	1.000	-0.159	-0.064	-0.037
vP3loMhi	0.235	-0.367	-0.159	1.000	0.107	0.180
AmploMhi	0.029	-0.256	-0.064	0.107	1.000	0.743
ItcloMhi	-0.005	-0.114	-0.037	0.180	0.743	1.000

scatterplots

pairs(Dtmp)

#cor.test(D$meanRTloMhi, D$vP3loMhi)

correlations with wm capacity

Working memory capacity was the wmc.pcu score.

Any difference scores refer to signal minus noise (SmN) measures.

The sample is smaller: The first three subjects were excluded because they performed the task at the end (rather than at the beginning), and two more subjects were excluded because of equipment failure.

LL and UL refer to the 95% confidence interval of the correlation (from one-tailed t tests).

The BFs were computed with the BayesFactor package in R.
https://richarddmorey.github.io/BayesFactor/

The BF01 used a flat prior (beta width = 1). Note that this prior is the default in JASP, which is a stand-alone program to conduct Bayesian analyses.
https://jasp-stats.org/

Cred_LL and Cred_UL refer to the 95% credible interval of the correlation (from BayesFactor).

table

Variable	Correlation	LL	UL	BF01	Cred_LL	Cred_UL	N
Amp_no	0.042	-0.273	0.349	4.915	-0.265	0.334	40
Amp_lo	0.088	-0.229	0.389	4.405	-0.223	0.379	40
Amp_hi	0.041	-0.274	0.348	4.927	-0.264	0.336	40
Amp_vh	0.025	-0.289	0.334	5.020	-0.281	0.327	40
Amp_noMlo	-0.127	-0.422	0.192	3.780	-0.407	0.187	40
Amp_noMhi	0.006	-0.306	0.317	5.074	-0.297	0.303	40
Amp_noMvh	0.039	-0.276	0.347	4.938	-0.265	0.330	40
Amp_loMhi	0.116	-0.203	0.413	3.969	-0.191	0.397	40
Amp_loMvh	0.149	-0.170	0.440	3.377	-0.170	0.422	40
Amp_hiMvh	0.040	-0.275	0.347	4.933	-0.266	0.331	40
Itc_no	0.054	-0.262	0.360	4.815	-0.261	0.353	40
Itc_lo	0.077	-0.240	0.380	4.556	-0.228	0.368	40
Itc_hi	0.021	-0.293	0.330	5.039	-0.284	0.318	40
Itc_vh	0.045	-0.271	0.351	4.898	-0.263	0.333	40
Itc_noMlo	-0.071	-0.374	0.246	4.630	-0.363	0.230	40
Itc_noMhi	0.079	-0.238	0.381	4.533	-0.238	0.360	40
Itc_noMvh	0.023	-0.290	0.332	5.029	-0.279	0.316	40
Itc_loMhi	0.145	-0.175	0.436	3.463	-0.175	0.418	40
Itc_loMvh	0.085	-0.232	0.387	4.449	-0.225	0.379	40
Itc_hiMvh	-0.079	-0.381	0.238	4.528	-0.368	0.234	40

scatterplots

Dtmp2 = subset(Dtmp2, select = c(Amp_no, Amp_hi, Amp_noMhi, Itc_no, Itc_hi, Itc_noMhi, pcu))
pairs(Dtmp2)

effect of block

Explore factorial analyses (load x block).

ASSRs: no vs high load
visual P3: low vs high load

by measure

Dtmp1a = subset(D, select=c(fp, 
                            AmpSmNno1, AmpSmNno2, AmpSmNno3, AmpSmNno4,
                            AmpSmNhi1, AmpSmNhi2, AmpSmNhi3, AmpSmNhi4))
Dtmp1b = subset(D, select=c(fp, 
                            AmpSNRno1, AmpSNRno2, AmpSNRno3, AmpSNRno4,
                            AmpSNRhi1, AmpSNRhi2, AmpSNRhi3, AmpSNRhi4))
Dtmp1c = subset(D, select=c(fp, 
                            AmpSno1, AmpSno2, AmpSno3, AmpSno4,
                            AmpShi1, AmpShi2, AmpShi3, AmpShi4))
Dtmp1d = subset(D, select=c(fp, 
                            ItcSmNno1, ItcSmNno2, ItcSmNno3, ItcSmNno4,
                            ItcSmNhi1, ItcSmNhi2, ItcSmNhi3, ItcSmNhi4))
Dtmp1e = subset(D, select=c(fp, 
                            ItcSNRno1, ItcSNRno2, ItcSNRno3, ItcSNRno4,
                            ItcSNRhi1, ItcSNRhi2, ItcSNRhi3, ItcSNRhi4))
Dtmp1f = subset(D, select=c(fp, 
                            ItcSno1, ItcSno2, ItcSno3, ItcSno4,
                            ItcShi1, ItcShi2, ItcShi3, ItcShi4))
Dtmp1g = subset(D, select=c(fp, 
                            vP3lo1, vP3lo2, vP3lo3, vP3lo4,
                            vP3hi1, vP3hi2, vP3hi3, vP3hi4))
Dtmp1g = na.omit(Dtmp1g)
Dtmp1 = list(Dtmp1a, Dtmp1b, Dtmp1c, Dtmp1d, Dtmp1e, Dtmp1f, Dtmp1g)
Dvars = c('Amplitude_SmN','Amplitude_SNR','Amplitude_S',
          'Intertrial_Coherence_SmN','Intertrial_Coherence_SNR','Intertrial_Coherence_S',
          'Visual_P3')
Fres = (matrix(ncol = 5, nrow = length(Dvars)))
Fres = as.data.frame(Fres)
colnames(Fres) = c('Variable','Overall_Interaction_p','Linear_Interaction_p','BF01_Interaction', 'BF01_error%')

amplitude SmN

Fres = computeANOVA(1, Dtmp1[[1]])

## =====================================================================
##                         Amplitude_SmN
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.2514559 0.2155149 0.2873969
##    no     2 45 0.2431811 0.2045487 0.2818135
##    no     3 45 0.2559860 0.2217547 0.2902174
##    no     4 45 0.2201614 0.1874002 0.2529226
##  high     1 45 0.2468285 0.2117379 0.2819192
##  high     2 45 0.2560215 0.2243014 0.2877415
##  high     3 45 0.2460925 0.2099493 0.2822356
##  high     4 45 0.2235309 0.1904516 0.2566101
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 21.2414      1   3.8643     44 241.8630 < 2.2e-16 ***
## load         0.0000      1   0.1541     44   0.0046 0.9463522    
## block        0.0534      3   0.3387    132   6.9377 0.0002261 ***
## load:block   0.0066      3   0.3555    132   0.8209 0.4845334    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.67429 0.00483
## load:block        0.94568 0.79362
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])    
## block      0.79333  0.0007558 ***
## load:block 0.96256  0.4806937    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps   Pr(>F[HF])
## block      0.8417401 0.0005690922
## load:block 1.0373621 0.4845334175
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                      Value Df  test stat   approx F num Df den Df Pr(>F)
## load1 : block1 -0.00019868  1 1.2438e-05 0.00054726      1     44 0.9814
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 13.60731 ±2.05%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

amplitude SNR

Fres = computeANOVA(2, Dtmp1[[2]])

## =====================================================================
##                         Amplitude_SNR
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N     Mean    CI_LL    CI_UL
##    no     1 45 7.026529 5.878655 8.174403
##    no     2 45 7.036133 5.860118 8.212148
##    no     3 45 6.556063 5.519877 7.592250
##    no     4 45 5.816517 4.902929 6.730104
##  high     1 45 7.101279 6.087925 8.114634
##  high     2 45 6.930762 5.881942 7.979582
##  high     3 45 6.868489 5.731600 8.005378
##  high     4 45 6.085284 4.950328 7.220240
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 16052.7      1   3563.7     44 198.1991 < 2.2e-16 ***
## load            1.7      1    129.9     44   0.5777    0.4513    
## block          69.5      3    361.6    132   8.4557 3.515e-05 ***
## load:block      2.5      3    486.5    132   0.2254    0.8786    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.90386 0.50466
## load:block        0.84125 0.19365
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])    
## block      0.93346  5.799e-05 ***
## load:block 0.89337      0.858    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps   Pr(>F[HF])
## block      1.0033800 3.514749e-05
## load:block 0.9568011 8.707257e-01
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                   Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 -0.15809  1 0.0052277  0.23123      1     44  0.633
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 25.68044 ±2.69%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

amplitude S

Fres = computeANOVA(3, Dtmp1[[3]])

## =====================================================================
##                         Amplitude_S
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.3030946 0.2671156 0.3390736
##    no     2 45 0.2941862 0.2575843 0.3307881
##    no     3 45 0.3135706 0.2795390 0.3476022
##    no     4 45 0.2783396 0.2468592 0.3098199
##  high     1 45 0.2936031 0.2595675 0.3276387
##  high     2 45 0.3078343 0.2770129 0.3386558
##  high     3 45 0.2962455 0.2603443 0.3321467
##  high     4 45 0.2788185 0.2473201 0.3103168
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 31.4803      1   3.7578     44 368.6022 < 2.2e-16 ***
## load         0.0009      1   0.1078     44   0.3697  0.546280    
## block        0.0372      3   0.2926    132   5.5905  0.001219 ** 
## load:block   0.0121      3   0.3133    132   1.6952  0.171145    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.81724 0.12520
## load:block        0.98243 0.97967
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])   
## block      0.87475   0.002093 **
## load:block 0.98819   0.171805   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps  Pr(>F[HF])
## block      0.9352657 0.001610889
## load:block 1.0674201 0.171145153
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                     Value Df  test stat   approx F num Df den Df Pr(>F)
## load1 : block1 0.00016788  1 1.1574e-05 0.00050925      1     44 0.9821
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 3.652064 ±13.11%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

ITC SmN

Fres = computeANOVA(4, Dtmp1[[4]])

## =====================================================================
##                         Intertrial_Coherence_SmN
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.3650258 0.3144738 0.4155778
##    no     2 45 0.3829952 0.3325454 0.4334449
##    no     3 45 0.3764730 0.3303361 0.4226099
##    no     4 45 0.3486003 0.3003355 0.3968651
##  high     1 45 0.3749499 0.3273693 0.4225306
##  high     2 45 0.3752265 0.3240165 0.4264366
##  high     3 45 0.3679060 0.3158521 0.4199600
##  high     4 45 0.3369472 0.2876637 0.3862307
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 48.228      1   7.8927     44 268.8630 < 2.2e-16 ***
## load         0.002      1   0.3698     44   0.2184  0.642576    
## block        0.069      3   0.6482    132   4.6769  0.003876 ** 
## load:block   0.006      3   0.6359    132   0.4459  0.720584    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.83195 0.16423
## load:block        0.84592 0.20993
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])   
## block      0.89046   0.005567 **
## load:block 0.90132   0.700478   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps  Pr(>F[HF])
## block      0.9534298 0.004519667
## load:block 0.9660190 0.713908294
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                   Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 0.010361  1  0.022342   1.0055      1     44 0.3215
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 22.53883 ±2.1%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

ITC SNR

Fres = computeANOVA(5, Dtmp1[[5]])

## =====================================================================
##                         Intertrial_Coherence_SNR
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N     Mean    CI_LL    CI_UL
##    no     1 45 5.616130 4.936821 6.295440
##    no     2 45 5.860586 5.191262 6.529911
##    no     3 45 5.861011 5.260676 6.461346
##    no     4 45 5.365169 4.750441 5.979896
##  high     1 45 5.846516 5.241097 6.451935
##  high     2 45 5.715651 5.039596 6.391705
##  high     3 45 5.868372 5.150802 6.585941
##  high     4 45 5.215323 4.578267 5.852378
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df  F value  Pr(>F)    
## (Intercept) 11567.9      1  1278.26     44 398.1879 < 2e-16 ***
## load            0.0      1    65.84     44   0.0122 0.91245    
## block          18.0      3   140.56    132   5.6295 0.00116 ** 
## load:block      2.2      3   168.91    132   0.5614 0.64144    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.80305 0.09525
## load:block        0.95681 0.86467
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])   
## block      0.87759   0.001979 **
## load:block 0.97118   0.636426   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps  Pr(>F[HF])
## block      0.9385436 0.001516115
## load:block 1.0474625 0.641440300
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                  Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 0.15628  1  0.019326  0.86711      1     44 0.3568
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 17.92473 ±1.87%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

ITC S

Fres = computeANOVA(6, Dtmp1[[6]])

## =====================================================================
##                         Intertrial_Coherence_S
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.4462053 0.3965190 0.4958917
##    no     2 45 0.4632179 0.4130473 0.5133884
##    no     3 45 0.4547615 0.4083251 0.5011980
##    no     4 45 0.4292198 0.3807013 0.4777384
##  high     1 45 0.4528388 0.4048025 0.5008751
##  high     2 45 0.4560185 0.4048860 0.5071510
##  high     3 45 0.4453270 0.3937112 0.4969428
##  high     4 45 0.4180863 0.3688029 0.4673696
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 71.516      1   7.8710     44 399.7894 < 2.2e-16 ***
## load         0.003      1   0.3816     44   0.2897  0.593146    
## block        0.064      3   0.6568    132   4.3006  0.006259 ** 
## load:block   0.004      3   0.6020    132   0.3242  0.807860    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.82732 0.15100
## load:block        0.84065 0.19162
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])   
## block      0.88718   0.008675 **
## load:block 0.89642   0.786000   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps Pr(>F[HF])
## block      0.9496345 0.00723897
## load:block 0.9603401 0.79985032
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                   Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 0.008781  1  0.017496  0.78355      1     44 0.3809
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 26.38811 ±3.47%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

visual P3

Fres = computeANOVA(7, Dtmp1[[7]])

## =====================================================================
##                         Visual_P3
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N     Mean       CI_LL    CI_UL
##   low     1 42 7.526945  5.22281119 9.831078
##   low     2 42 6.905203  4.97479183 8.835615
##   low     3 42 6.929089  5.33363977 8.524537
##   low     4 42 6.285659  4.63110959 7.940208
##  high     1 42 1.218411 -0.07473078 2.511552
##  high     2 42 2.198978  0.95162162 3.446334
##  high     3 42 2.411057  1.23859668 3.583517
##  high     4 42 2.601892  1.47603053 3.727753
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df F value    Pr(>F)    
## (Intercept) 6833.2      1   5149.2     41 54.4084 4.876e-09 ***
## load        1938.7      1   1383.8     41 57.4430 2.539e-09 ***
## block          4.3      3   1116.6    123  0.1562   0.92553    
## load:block    75.8      3    874.3    123  3.5550   0.01641 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.68061 0.00924
## load:block        0.80846 0.13344
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])  
## block      0.79972     0.8899  
## load:block 0.89595     0.0204 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps Pr(>F[HF])
## block      0.8528708 0.90092668
## load:block 0.9647891 0.01766311
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                  Value Df test stat approx F num Df den Df   Pr(>F)   
## load1 : block1 -1.2748  1   0.22852   12.145      1     41 0.001187 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 2.459765 ±1.29%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

summary

Summary of interaction analyses:

Overall_interaction has 3 dfs.
Linear_interaction has 1 df and captures the interaction of load with the linear trend over blocks.
BF01_interaction is the BF for a model without the overall interaction versus a model with the overall interaction (prior: Cauchy r scale = 0.5).
BF01_error% estimates the error of the BF in percent. For example, if BF = 10 and the error is 10%, then the BF is estimated to vary between 9 and 11.

write.table(Fres, file = file.path(dir_fig,
                                   paste0("table_ANOVA_by_block_interaction_p.tsv")),
                                   row.names = F, dec = ".", sep = "\t")
Fres[,2:5] = round(Fres[,2:5], digits = 3)
Fres[Fres < 0.001] = 0.001
Fres %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Variable	Overall_Interaction_p	Linear_Interaction_p	BF01_Interaction	BF01_error%
Amplitude_SmN	0.481	0.981	13.607	2.049
Amplitude_SNR	0.858	0.633	25.680	2.689
Amplitude_S	0.172	0.982	3.652	13.111
Intertrial_Coherence_SmN	0.700	0.321	22.539	2.100
Intertrial_Coherence_SNR	0.636	0.357	17.925	1.870
Intertrial_Coherence_S	0.786	0.381	26.388	3.465
Visual_P3	0.020	0.001	2.460	1.294

reliability

Reliability of EEG variables over blocks. Results show mean correlations between the four blocks.
Note that raw scores (i.e., no and high load) have decent reliability whereas the difference scores show low reliability. This is not necessarily a problem: If all subjects change similarly, any variability in the difference scores would reflect mainly measurement noise, and the difference scores would show a low correlations over blocks (i.e., low reliability). Because of their low reliability, the difference scores are a poor measure if the goal is to distinguish the order among individual subjects.

Cres = (matrix(ncol = 4, nrow = 4))
Cres = as.data.frame(Cres)
colnames(Cres) = c('Variable','no','high','noMhi')
Vlabels = c("ampSNR", "ampS", "ampSmN", "itcSNR", "itcS", "itcSmN")
for (i in 1:length(Vlabels)) {
  Cres[i,1] = Vlabels[i]
  Dtmp2 = Dtmp1[[i]]
  Dtmp2$fp = NULL
  # no  
  tmp = cor(Dtmp2[,1:4])
  diag(tmp) = NA
  tmp = FisherZ(tmp)
  Cres[i,2] = FisherZInv(mean(tmp, na.rm = TRUE))
  # high
  tmp = cor(Dtmp2[,5:8])
  diag(tmp) = NA
  tmp = FisherZ(tmp)
  Cres[i,3] = FisherZInv(mean(tmp, na.rm = TRUE))
  # no minus high
  tmp = Dtmp2[,1:4] - Dtmp2[,5:8]
  tmp = cor(tmp)
  diag(tmp) = NA
  tmp = FisherZ(tmp)
  Cres[i,4] = FisherZInv(mean(tmp, na.rm = TRUE))
}
Cres %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Variable	no	high	noMhi
ampSNR	0.791	0.834	0.074
ampS	0.764	0.761	-0.041
ampSmN	0.812	0.839	0.008
itcSNR	0.817	0.840	0.163
itcS	0.748	0.769	0.037
itcSmN	0.822	0.840	0.192

effect of minute

Analysis of EEG data in 1-min blocks.
Compares no vs high load.

The data in each 3-min block were analyzed by minute (this was done during preprocessing in MNE-python). For each minute, mean amp (and itc) were computed and then averaged across the four blocks of load.
Note that the R output uses the term “block,” but this is meant to refer to “minute.”

by measure

# The data in each 3-min block are analyzed by minute. For each minute, the amp and itc are computed and then averaged across the four blocks of load.  
Dtmp1a = subset(D, select=c(fp, 
                            m1noampSmN, m2noampSmN, m3noampSmN,
                            m1hiampSmN, m2hiampSmN, m3hiampSmN))
Dtmp1b = subset(D, select=c(fp, 
                            m1noampSNR, m2noampSNR, m3noampSNR,
                            m1hiampSNR, m2hiampSNR, m3hiampSNR))
Dtmp1c = subset(D, select=c(fp, 
                            m1noampS, m2noampS, m3noampS,
                            m1hiampS, m2hiampS, m3hiampS))
Dtmp1d = subset(D, select=c(fp, 
                            m1noitcSmN, m2noitcSmN, m3noitcSmN,
                            m1hiitcSmN, m2hiitcSmN, m3hiitcSmN))
Dtmp1e = subset(D, select=c(fp, 
                            m1noitcSNR, m2noitcSNR, m3noitcSNR,
                            m1hiitcSNR, m2hiitcSNR, m3hiitcSNR))
Dtmp1f = subset(D, select=c(fp, 
                            m1noitcS, m2noitcS, m3noitcS,
                            m1hiitcS, m2hiitcS, m3hiitcS))
Dtmp1 = list(Dtmp1a, Dtmp1b, Dtmp1c, Dtmp1d, Dtmp1e, Dtmp1f)
# rename variables to be able to call computeANOVA
for (i in 1:6){ 
  tmp = Dtmp1[[i]]
  colnames(tmp) <- c('fp','no1','no2','no3','hi1','hi2','hi3')
  Dtmp1[[i]] = tmp
}
Dvars = c('Amplitude_SmN','Amplitude_SNR','Amplitude_S',
          'Intertrial_Coherence_SmN','Intertrial_Coherence_SNR','Intertrial_Coherence_S')
Fres = (matrix(ncol = 5, nrow = length(Dvars)))
Fres = as.data.frame(Fres)
colnames(Fres) = c('Variable','Overall_Interaction_p','Linear_Interaction_p','BF01_Interaction', 'BF01_error%')

amplitude SmN

Fres = computeANOVA(1, Dtmp1[[1]], "minute")

## =====================================================================
##                         Amplitude_SmN
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.2673304 0.2276504 0.3070103
##    no     2 45 0.2598172 0.2211833 0.2984511
##    no     3 45 0.2497098 0.2107499 0.2886697
##  high     1 45 0.2541013 0.2142862 0.2939163
##  high     2 45 0.2624151 0.2273724 0.2974579
##  high     3 45 0.2491483 0.2101146 0.2881820
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df  F value Pr(>F)    
## (Intercept) 17.8453      1   3.8880     44 201.9542 <2e-16 ***
## load         0.0009      1   0.1009     44   0.4096 0.5255    
## block        0.0079      2   0.2244     88   1.5535 0.2172    
## load:block   0.0032      2   0.1359     88   1.0224 0.3640    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.82977  0.0181
## load:block        0.95715  0.3900
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])
## block      0.85453     0.2200
## load:block 0.95891     0.3615
## 
##               HF eps Pr(>F[HF])
## block      0.8856283  0.2195016
## load:block 1.0016340  0.3639655
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                     Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 -0.0063338  1  0.026833   1.2132      1     44 0.2767
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 7.586289 ±1.13%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

amplitude SNR

Fres = computeANOVA(2, Dtmp1[[2]], "minute")

## =====================================================================
##                         Amplitude_SNR
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N     Mean    CI_LL    CI_UL
##    no     1 45 4.866360 4.164390 5.568330
##    no     2 45 4.721056 4.030760 5.411352
##    no     3 45 4.492041 3.798660 5.185421
##  high     1 45 4.751134 4.056132 5.446137
##  high     2 45 4.624352 3.997678 5.251026
##  high     3 45 4.337621 3.682142 4.993100
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 5793.2      1  1221.12     44 208.7440 < 2.2e-16 ***
## load           1.0      1    46.72     44   0.9480  0.335543    
## block          7.2      2    42.89     88   7.3915  0.001078 ** 
## load:block     0.0      2    32.50     88   0.0529  0.948501    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.86049 0.03954
## load:block        0.96500 0.46483
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])   
## block      0.87757   0.001823 **
## load:block 0.96618   0.944152   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps  Pr(>F[HF])
## block      0.9111302 0.001578235
## load:block 1.0097561 0.948500839
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                   Value Df  test stat approx F num Df den Df Pr(>F)
## load1 : block1 0.019597  1 0.00092632 0.040796      1     44 0.8409
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 13.45949 ±2.06%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

amplitude S

Fres = computeANOVA(3, Dtmp1[[3]], "minute")

## =====================================================================
##                         Amplitude_S
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.3580625 0.3189450 0.3971799
##    no     2 45 0.3520963 0.3133708 0.3908217
##    no     3 45 0.3438373 0.3054124 0.3822621
##  high     1 45 0.3372365 0.2992148 0.3752582
##  high     2 45 0.3503366 0.3156798 0.3849934
##  high     3 45 0.3423072 0.3057068 0.3789076
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value Pr(>F)    
## (Intercept) 32.569      1   3.7216     44 385.0554 <2e-16 ***
## load         0.004      1   0.0794     44   2.4156 0.1273    
## block        0.003      2   0.2181     88   0.6052 0.5482    
## load:block   0.006      2   0.1208     88   2.0105 0.1400    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.87166 0.05218
## load:block        0.95819 0.39926
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])
## block      0.88626     0.5292
## load:block 0.95987     0.1421
## 
##               HF eps Pr(>F[HF])
## block      0.9207657  0.5352293
## load:block 1.0027088  0.1400295
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                     Value Df test stat approx F num Df den Df  Pr(>F)  
## load1 : block1 -0.0096479  1  0.065045   3.0611      1     44 0.08716 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 4.067075 ±2.01%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

ITC SmN

Fres = computeANOVA(4, Dtmp1[[4]], "minute")

## =====================================================================
##                         Intertrial_Coherence_SmN
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.4067182 0.3510711 0.4623653
##    no     2 45 0.3853908 0.3349144 0.4358673
##    no     3 45 0.3672219 0.3122740 0.4221698
##  high     1 45 0.3946982 0.3373811 0.4520153
##  high     2 45 0.3846980 0.3348897 0.4345064
##  high     3 45 0.3593230 0.3056851 0.4129609
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 39.608      1   7.5927     44 229.5275 < 2.2e-16 ***
## load         0.003      1   0.2798     44   0.5010 0.4827765    
## block        0.064      2   0.3156     88   8.8697 0.0003096 ***
## load:block   0.001      2   0.2486     88   0.2618 0.7702622    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic  p-value
## block             0.89743 0.097623
## load:block        0.89406 0.090022
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])    
## block      0.90698  0.0005117 ***
## load:block 0.90421  0.7480920    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps   Pr(>F[HF])
## block      0.9437688 0.0004194039
## load:block 0.9406909 0.7568612430
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                     Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 -0.0020605  1 0.0014841 0.065398      1     44 0.7994
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 11.65029 ±2.13%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

ITC SNR

Fres = computeANOVA(5, Dtmp1[[5]], "minute")

## =====================================================================
##                         Intertrial_Coherence_SNR
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N     Mean    CI_LL    CI_UL
##    no     1 45 4.049719 3.618167 4.481271
##    no     2 45 3.800942 3.421601 4.180284
##    no     3 45 3.695258 3.292194 4.098322
##  high     1 45 3.941257 3.506068 4.376446
##  high     2 45 3.813865 3.459928 4.167802
##  high     3 45 3.625299 3.226132 4.024466
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value    Pr(>F)    
## (Intercept) 3942.1      1   416.52     44 416.4325 < 2.2e-16 ***
## load           0.2      1    15.98     44   0.5658    0.4559    
## block          5.1      2    20.47     88  10.9243 5.785e-05 ***
## load:block     0.2      2    18.23     88   0.4179    0.6597    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic p-value
## block             0.90886 0.12813
## load:block        0.90170 0.10810
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])    
## block      0.91647  0.0001032 ***
## load:block 0.91050  0.6408600    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps   Pr(>F[HF])
## block      0.9543252 7.935582e-05
## load:block 0.9476846 6.489116e-01
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                    Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 -0.019251  1 0.0020468 0.090243      1     44 0.7653
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 9.601923 ±9.03%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

ITC S

Fres = computeANOVA(6, Dtmp1[[6]], "minute")

## =====================================================================
##                         Intertrial_Coherence_S
## =====================================================================
## 
## 
## Descriptives
## ============
##  load block  N      Mean     CI_LL     CI_UL
##    no     1 45 0.5426056 0.4872752 0.5979359
##    no     2 45 0.5258366 0.4752990 0.5763743
##    no     3 45 0.5057792 0.4508772 0.5606812
##  high     1 45 0.5311734 0.4741227 0.5882242
##  high     2 45 0.5232539 0.4727252 0.5737826
##  high     3 45 0.4997347 0.4467401 0.5527294
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##             Sum Sq num Df Error SS den Df  F value  Pr(>F)    
## (Intercept) 73.401      1   7.5609     44 427.1518 < 2e-16 ***
## load         0.003      1   0.2858     44   0.4646 0.49906    
## block        0.054      2   0.3242     88   7.2961 0.00117 ** 
## load:block   0.001      2   0.2362     88   0.1667 0.84673    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##            Test statistic  p-value
## block             0.90138 0.107279
## load:block        0.89567 0.093588
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##             GG eps Pr(>F[GG])   
## block      0.91023   0.001708 **
## load:block 0.90553   0.825970   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##               HF eps  Pr(>F[HF])
## block      0.9473896 0.001460326
## load:block 0.9421621 0.834392724
## 
## 
## Linear interaction contrast
## ===========================
## Multivariate Test: Pillai test statistic
## P-value adjustment method: holm
##                     Value Df test stat approx F num Df den Df Pr(>F)
## load1 : block1 -0.0026938  1 0.0026347  0.11623      1     44 0.7348
## 
## 
## 
## BF without vs with interaction
## ==============================
## Bayes factor analysis
## --------------
## [1] load + block + fp : 12.44145 ±1.72%
## 
## Against denominator:
##   eeg_dv ~ load + block + load:block + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

summary

Summary of interaction analyses:

Overall_interaction has 3 dfs.
Linear_interaction has 1 df and captures the interaction of load with the linear trend over blocks.
BF01_interaction is the BF for a model without the overall interaction versus a model with the overall interaction (prior: Cauchy r scale = 0.5).
BF01_error% estimates the error of the BF in percent. For example, if BF = 10 and the error is 10%, then the BF is estimated to vary between 9 and 11.

write.table(Fres, file = file.path(dir_fig,
 paste0("table_ANOVA_by_minute_interaction_p.tsv")),
                                  row.names = F, dec = ".", sep = "\t")
Fres[,2:5] = round(Fres[,2:5], digits = 3)
Fres[Fres < 0.001] = 0.001
Fres %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Variable	Overall_Interaction_p	Linear_Interaction_p	BF01_Interaction	BF01_error%
Amplitude_SmN	0.362	0.277	7.586	1.126
Amplitude_SNR	0.944	0.841	13.459	2.061
Amplitude_S	0.142	0.087	4.067	2.014
Intertrial_Coherence_SmN	0.748	0.799	11.650	2.135
Intertrial_Coherence_SNR	0.641	0.765	9.602	9.027
Intertrial_Coherence_S	0.826	0.735	12.441	1.723

Dtmp = D
Dtmp$study = 'Study2'
Dtmp = subset(Dtmp, select=c(study, fp,
  AmpSmNno, AmpSmNlo, AmpSmNhi, ItcSmNno, ItcSmNlo, ItcSmNhi))
write.table(Dtmp, paste0("data_export_SmN_ASSR_study2.tsv"), sep="\t", row.names = FALSE)
rm(list=ls(pattern="Dtmp"))

time-frequency analysis at 2 Hz

The goal of this exploratory time-frequency analysis was to examine if the 40-Hz signal changed periodically with the onset of the visual stimuli, which were shown every 500 ms (i.e., at 2 Hz). To detect this period change, epochs were rather long (10 s), and each epoch began every 20th visual stimulus (see MNE-python script).

These epochs should contain a 40-Hz signal (even though the phase shifts between epochs).
If the onsets of the visual stimuli affect the 40-Hz signal periodically, then the 40-Hz signal should change at 2 Hz within an epoch.
Thus, within the 40-Hz signal, there should be a signal at 2 Hz.
The noise can be defined by surrounding frequencies (10 on each side excluding the 2 nearest neighbors).
Results show amplitude SmN and S for the 2-Hz signal.

A potential concern is that the 40-Hz response may be confounded by indirect visual effects on the same electrodes as used for ASSRs. That is, the electrodes that were used to record ASSRs may pick up unrelated ERP activity from visual onsets. However, because the response is recorded at 40 Hz whereas visual events occur at 2 Hz, an analysis of only the 40-Hz response should already remove confounding effects of the visual events at 2 Hz (because the frequency is much lower). Accordingly, any 2-Hz activity within the 40-Hz response suggests that the 40-Hz response is actually affected by the visual onsets.

Although results suggest some activity at 2 Hz, there were no differences among the load conditions.

figure of amplitude spectrum

The figure shows amplitude over time (top) and spectrum (bottom). The figure was generated in MNE-python (see script).

knitr::include_graphics("40_Hz_amplitude_over_time_and_spectrum_for_10s_ind.jpg")

Amplitude and spectrum

#Dtf = read.csv(file.path(dir_tf, "data_audio_signal_noise_tf.tsv"), sep="\t", header=TRUE, dec=".")
Dtf = read.csv("data_audio_signal_noise_tf_amplitude.tsv", sep="\t", header=TRUE, dec=".")
Dvars = c('AmpSmN','AmpS')

Fres = (matrix(ncol = 4, nrow = length(Dvars)))
Fres = as.data.frame(Fres)
colnames(Fres) = c('Variable','P_load','BF01_load', 'BF01_error%')

by measure

amplitude SmN

Fres = computeANOVAtf(Dtf, 1)

## =====================================================================
##                         AmpSmN
## =====================================================================
## 
## 
## Descriptives
## ============
##       load  N      Mean      CI_LL    CI_UL
##         no 42 0.8405075 -0.1889680 1.869983
##        low 42 1.3182676  0.1596759 2.476859
##       high 42 1.1394366  0.4187611 1.860112
##  very high 42 0.9122387  0.3366768 1.487801
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df F value  Pr(>F)  
## (Intercept) 186.143      1  1083.90     41  7.0411 0.01128 *
## load          5.998      3   289.46    123  0.8495 0.46939  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##      Test statistic    p-value
## load         0.5855 0.00072565
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##       GG eps Pr(>F[GG])
## load 0.75449     0.4431
## 
##        HF eps Pr(>F[HF])
## load 0.800841  0.4487255
## 
## 
## Polynomial contrasts
## ====================
## Analysis of Variance Table
## 
## Response: eeg_dv
##            Df  Sum Sq Mean Sq F value Pr(>F)    
## loadlin     1    0.00  0.0028  0.0012 0.9727    
## loadqua     1    5.22  5.2181  2.2174 0.1390    
## loadcub     1    0.78  0.7769  0.3301 0.5666    
## fp         41 1083.90 26.4367 11.2338 <2e-16 ***
## Residuals 123  289.46  2.3533                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## 
## BF of main effect
## =================
## Bayes factor analysis
## --------------
## [1] fp : 11.8772 ±0.46%
## 
## Against denominator:
##   eeg_dv ~ load + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

amplitude S

Fres = computeANOVAtf(Dtf, 2)

## =====================================================================
##                         AmpS
## =====================================================================
## 
## 
## Descriptives
## ============
##       load  N     Mean    CI_LL    CI_UL
##         no 42 3.012330 1.686699 4.337962
##        low 42 3.310861 1.882000 4.739721
##       high 42 3.010470 2.103617 3.917323
##  very high 42 2.835109 2.057216 3.613002
## 
## 
## Anova
## =====
## 
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
## 
##              Sum Sq num Df Error SS den Df F value    Pr(>F)    
## (Intercept) 1554.83      1  1893.07     41 33.6744 8.248e-07 ***
## load           4.91      3   313.58    123  0.6423    0.5892    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Mauchly Tests for Sphericity
## 
##      Test statistic    p-value
## load        0.52581 0.00011053
## 
## 
## Greenhouse-Geisser and Huynh-Feldt Corrections
##  for Departure from Sphericity
## 
##      GG eps Pr(>F[GG])
## load 0.6968     0.5353
## 
##         HF eps Pr(>F[HF])
## load 0.7350124  0.5431675
## 
## 
## Polynomial contrasts
## ====================
## Analysis of Variance Table
## 
## Response: eeg_dv
##            Df  Sum Sq Mean Sq F value Pr(>F)    
## loadlin     1    1.45   1.454  0.5703 0.4516    
## loadqua     1    2.36   2.358  0.9249 0.3381    
## loadcub     1    1.10   1.101  0.4317 0.5124    
## fp         41 1893.07  46.172 18.1108 <2e-16 ***
## Residuals 123  313.58   2.549                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## 
## BF of main effect
## =================
## Bayes factor analysis
## --------------
## [1] fp : 15.1405 ±0.62%
## 
## Against denominator:
##   eeg_dv ~ load + fp 
## ---
## Bayes factor type: BFlinearModel, JZS

summary

Summary of 2-Hz tf analysis:

P_load is the p value (from the ANOVA) for the main effect of load with 3 dfs.
BF01_load is the BF for a model without the main effect versus a model with the main effect (prior: Cauchy r scale = 0.5).
BF01_error% estimates the error of the BF in percent. For example, if BF = 10 and the error is 10%, then the BF is estimated to vary between 9 and 11.

Fres[,2:4] = round(Fres[,2:4], digits = 3)
Fres[Fres < 0.001] = 0.001
Fres %>%
  kable(digits = 3) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = F, position = 'left')

Variable	P_load	BF01_load	BF01_error%
AmpSmN	0.449	11.877	0.456
AmpS	0.543	15.141	0.617

ASSR Study 2 (2020)

Stefan Wiens & Malina Szychowska

2020-08-24

recode load

quick data check

hit rates

table

plot task performance

NASA-TLX ratings

mental

physical

temporal

performance

effort

frustration

all ratings

visual P3

Bayes factors for load

amplitude SmN

amplitude SNR

amplitude S

ITC SmN

ITC SNR

ITC S

BF only for SmN

plot amp and ITC

correlations with behavior

table

scatterplots

correlations with wm capacity

table

scatterplots

effect of block

by measure

amplitude SmN

amplitude SNR

amplitude S

ITC SmN

ITC SNR

ITC S

visual P3

summary

reliability

effect of minute

by measure

amplitude SmN

amplitude SNR

amplitude S

ITC SmN

ITC SNR

ITC S

summary

time-frequency analysis at 2 Hz

figure of amplitude spectrum

by measure

amplitude SmN

amplitude S

summary