Statistical properties of n-back sequences bias behaviors. These bias, under specified structure, allows multiple cognitive strategies, producing heterogeneous behavior in a “gold standard” cognitive task.
-load('./data/CL2015.RData')
-window_size <- 8\(P=\{V,D,C,W\}\)
-\(V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}\)
-\(D=\{\}\)
- - - -with_lures <- function(stimulus, stimulus_type, n) {
- sapply(1:length(stimulus), function(i) {
- lures <- c(as.character(stimulus[i-n-1]), as.character(stimulus[i-n+1]))
- are_valid_trials <- i>n && all(!is.na(c(lures,stimulus[i])))
- ifelse(are_valid_trials && stimulus[i] %in% lures,
- "lure",
- as.character(stimulus_type[i]))
- })
-}
-with_history <- function(stimuli, length=16, fixed=F) {
- seq <- paste(stimuli, collapse = '')
-
- sapply(1:length(stimuli), function(i) {
- stri_reverse(str_sub(seq, max(1,i-length+1), i))
- })
- #ifelse(fixed, h[str_length(h)==size], h)
-}
-# $x_{s,local}$
-with_skewness <- function(history) {
- sapply(history, function(h) {
- freqs <- table(unlist(str_split(h,"")))
- sum(sort(freqs, decreasing = T)[1:2]) - 1
- })
-}
-# $x_{u,local}$
-with_lumpiness <- function(history) {
- sapply(history, function(h) {
- freqs <- table(unlist(str_split(h,"")))
- max(freqs) - 1
- })
-}
-with_targets_ratio <- function(stimulus_type, length=16) {
- sapply(1:length(stimulus_type), function(i) {
- trials <- stimulus_type[max(1,i-length):i]
- length(trials[trials=="target"]) / length(trials)
- })
-}
-with_lures_ratio <- function(stimulus_type, length=16) {
- sapply(1:length(stimulus_type), function(i) {
- trials <- stimulus_type[max(1,i-length):i]
- length(trials[trials=="lure"]) / length(trials)
- })
-}
-NB2 <- NB %>%
- filter(participant=="P13") %>%
- group_by(participant, condition, block) %>%
- mutate(n = ifelse(condition=='2-back',2,3)) %>%
- mutate(stimulus_type = with_lures(stimulus, stimulus_type, n)) %>%
- mutate(history = with_history(stimulus, window_size)) %>%
- mutate(x_sl = with_skewness(history)) %>%
- mutate(x_ul = with_lumpiness(history)) %>%
- mutate(x_t = with_targets_ratio(stimulus_type, window_size)) %>%
- mutate(x_l = with_lures_ratio(stimulus_type, window_size)) %>%
- ungroup()
-
-pca <- prcomp(~x_sl+x_ul+x_t+x_l, NB2, center = TRUE,scale. = TRUE, na.action=na.exclude)
-NB2 <- NB2 %>% mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
-# caret
-library(caret)
-# Compile cross-validation settings
-any(is.na(NB2))[1] TRUENB2 <- na.omit(NB2)
-# set.seed(100)
-# trainingfold <- createMultiFolds(NB2@correct, k = 5, times = 10)
-#
-# # PLS
-# mod1 <- train(correct ~ ., data = NB2[,c("correct","x_sl","x_ul","x_t","x_l")],
-# method = "pls",
-# metric = "Accuracy",
-# tuneLength = 20,
-# trControl = trainControl("repeatedcv", index = trainingfold, selectionFunction = "oneSE"),
-# preProc = c("zv","center","scale"))
-#
-# # Check CV
-# plot(mod1)
-plsResult <- plsR(correct ~ ., data=NB2[,c("correct","x_sl","x_ul","x_t","x_l")],3)____************************************************____
-____Component____ 1 ____
-____Component____ 2 ____
-____Component____ 3 ____
-____Predicting X without NA neither in X nor in Y____
-****________________________________________________****plsResultNumber of required components:
-[1] 3
-Number of successfully computed components:
-[1] 3
-Coefficients:
- [,1]
-Intercept 1.008159108
-x_sl -0.012385995
-x_ul 0.019020579
-x_t -0.009732895
-x_l 0.015588407
-Information criteria and Fit statistics:
- AIC RSS_Y R2_Y R2_residY RSS_residY AIC.std DoF.dof
-Nb_Comp_0 -400.8879 0.9942529 NA NA 173.0000 496.7877 1.000000
-Nb_Comp_1 -399.2749 0.9920439 0.002221709 0.002221709 172.6156 498.4007 4.020014
-Nb_Comp_2 -397.5844 0.9902807 0.003995089 0.003995089 172.3088 500.0912 5.000000
-Nb_Comp_3 -396.2011 0.9867774 0.007518680 0.007518680 171.6993 501.4745 5.000000
- sigmahat.dof AIC.dof BIC.dof GMDL.dof DoF.naive sigmahat.naive
-Nb_Comp_0 0.07580980 0.005780156 0.005884498 -441.1050 1 0.07580980
-Nb_Comp_1 0.07617155 0.005969500 0.006392969 -427.5293 2 0.07594536
-Nb_Comp_2 0.07632287 0.006026049 0.006554844 -423.3755 3 0.07609938
-Nb_Comp_3 0.07618775 0.006004731 0.006531655 -423.6749 4 0.07618775
- AIC.naive BIC.naive GMDL.naive
-Nb_Comp_0 0.005780156 0.005884498 -441.1050
-Nb_Comp_1 0.005833993 0.006043423 -436.3376
-Nb_Comp_2 0.005890962 0.006206384 -431.7944
-Nb_Comp_3 0.005938011 0.006359551 -427.5722Statistical properties of n-back sequences bias behaviors. These bias, under specified structure, allows multiple cognitive strategies, producing heterogeneous behavior in a “gold standard” cognitive task.
-load('./data/CL2015.RData')
-window_size <- 8\(P=\{V,D,C,W\}\)
-\(V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}\)
-\(D=\{\}\)
- - - -with_lures <- function(stimulus, stimulus_type, n) {
- sapply(1:length(stimulus), function(i) {
- lures <- c(as.character(stimulus[i-n-1]), as.character(stimulus[i-n+1]))
- are_valid_trials <- i>n && all(!is.na(c(lures,stimulus[i])))
- ifelse(are_valid_trials && stimulus[i] %in% lures,
- "lure",
- as.character(stimulus_type[i]))
- })
-}
-with_history <- function(stimuli, length=16, fixed=F) {
- seq <- paste(stimuli, collapse = '')
-
- sapply(1:length(stimuli), function(i) {
- stri_reverse(str_sub(seq, max(1,i-length+1), i))
- })
- #ifelse(fixed, h[str_length(h)==size], h)
-}
-# $x_{s,local}$
-with_skewness <- function(history) {
- sapply(history, function(h) {
- freqs <- table(unlist(str_split(h,"")))
- sum(sort(freqs, decreasing = T)[1:2]) - 1
- })
-}
-# $x_{u,local}$
-with_lumpiness <- function(history) {
- sapply(history, function(h) {
- freqs <- table(unlist(str_split(h,"")))
- max(freqs) - 1
- })
-}
-with_targets_ratio <- function(stimulus_type, length=16) {
- sapply(1:length(stimulus_type), function(i) {
- trials <- stimulus_type[max(1,i-length):i]
- length(trials[trials=="target"]) / length(trials)
- })
-}
-with_lures_ratio <- function(stimulus_type, length=16) {
- sapply(1:length(stimulus_type), function(i) {
- trials <- stimulus_type[max(1,i-length):i]
- length(trials[trials=="lure"]) / length(trials)
- })
-}
-NB2 <- NB %>%
- filter(participant=="P13") %>%
- group_by(participant, condition, block) %>%
- mutate(n = ifelse(condition=='2-back',2,3)) %>%
- mutate(stimulus_type = with_lures(stimulus, stimulus_type, n)) %>%
- mutate(history = with_history(stimulus, window_size)) %>%
- mutate(x_sl = with_skewness(history)) %>%
- mutate(x_ul = with_lumpiness(history)) %>%
- mutate(x_t = with_targets_ratio(stimulus_type, window_size)) %>%
- mutate(x_l = with_lures_ratio(stimulus_type, window_size)) %>%
- ungroup()
-
-pca <- prcomp(~x_sl+x_ul+x_t+x_l, NB2, center = TRUE,scale. = TRUE, na.action=na.exclude)
-NB2 <- NB2 %>% mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
-# caret
-library(caret)
-# Compile cross-validation settings
-any(is.na(NB2))[1] TRUENB2 <- na.omit(NB2)
-# set.seed(100)
-# trainingfold <- createMultiFolds(NB2@correct, k = 5, times = 10)
-#
-# # PLS
-# mod1 <- train(correct ~ ., data = NB2[,c("correct","x_sl","x_ul","x_t","x_l")],
-# method = "pls",
-# metric = "Accuracy",
-# tuneLength = 20,
-# trControl = trainControl("repeatedcv", index = trainingfold, selectionFunction = "oneSE"),
-# preProc = c("zv","center","scale"))
-#
-# # Check CV
-# plot(mod1)
-plsResult <- plsR(correct ~ ., data=NB2[,c("correct","x_sl","x_ul","x_t","x_l")],3)____************************************************____
-____Component____ 1 ____
-____Component____ 2 ____
-____Component____ 3 ____
-____Predicting X without NA neither in X nor in Y____
-****________________________________________________****plsResultNumber of required components:
-[1] 3
-Number of successfully computed components:
-[1] 3
-Coefficients:
- [,1]
-Intercept 1.008159108
-x_sl -0.012385995
-x_ul 0.019020579
-x_t -0.009732895
-x_l 0.015588407
-Information criteria and Fit statistics:
- AIC RSS_Y R2_Y R2_residY RSS_residY AIC.std DoF.dof
-Nb_Comp_0 -400.8879 0.9942529 NA NA 173.0000 496.7877 1.000000
-Nb_Comp_1 -399.2749 0.9920439 0.002221709 0.002221709 172.6156 498.4007 4.020014
-Nb_Comp_2 -397.5844 0.9902807 0.003995089 0.003995089 172.3088 500.0912 5.000000
-Nb_Comp_3 -396.2011 0.9867774 0.007518680 0.007518680 171.6993 501.4745 5.000000
- sigmahat.dof AIC.dof BIC.dof GMDL.dof DoF.naive sigmahat.naive
-Nb_Comp_0 0.07580980 0.005780156 0.005884498 -441.1050 1 0.07580980
-Nb_Comp_1 0.07617155 0.005969500 0.006392969 -427.5293 2 0.07594536
-Nb_Comp_2 0.07632287 0.006026049 0.006554844 -423.3755 3 0.07609938
-Nb_Comp_3 0.07618775 0.006004731 0.006531655 -423.6749 4 0.07618775
- AIC.naive BIC.naive GMDL.naive
-Nb_Comp_0 0.005780156 0.005884498 -441.1050
-Nb_Comp_1 0.005833993 0.006043423 -436.3376
-Nb_Comp_2 0.005890962 0.006206384 -431.7944
-Nb_Comp_3 0.005938011 0.006359551 -427.5722Statistical properties of n-back sequences bias behaviors. These bias, under specified structure, allows multiple cognitive strategies, producing heterogeneous behavior in a “gold standard” cognitive task.
-load('./data/CL2015.RData')
-window_size <- 8\(P=\{V,D,C,W\}\)
-\(V=\{x_N,x_{T},x_{T,local},x_L,x_{L,local},x_V,x_U,x_S,x_{S,local},x_G\}\)
-\(D=\{\}\)
- - - -with_lures <- function(stimulus, stimulus_type, n) {
- sapply(1:length(stimulus), function(i) {
- lures <- c(as.character(stimulus[i-n-1]), as.character(stimulus[i-n+1]))
- are_valid_trials <- i>n && all(!is.na(c(lures,stimulus[i])))
- ifelse(are_valid_trials && stimulus[i] %in% lures,
- "lure",
- as.character(stimulus_type[i]))
- })
-}
-with_history <- function(stimuli, length=16, fixed=F) {
- seq <- paste(stimuli, collapse = '')
-
- sapply(1:length(stimuli), function(i) {
- stri_reverse(str_sub(seq, max(1,i-length+1), i))
- })
- #ifelse(fixed, h[str_length(h)==size], h)
-}
-# $x_{s,local}$
-with_skewness <- function(history) {
- sapply(history, function(h) {
- freqs <- table(unlist(str_split(h,"")))
- sum(sort(freqs, decreasing = T)[1:2]) - 1
- })
-}
-# $x_{u,local}$
-with_lumpiness <- function(history) {
- sapply(history, function(h) {
- freqs <- table(unlist(str_split(h,"")))
- max(freqs) - 1
- })
-}
-with_targets_ratio <- function(stimulus_type, length=16) {
- sapply(1:length(stimulus_type), function(i) {
- trials <- stimulus_type[max(1,i-length):i]
- length(trials[trials=="target"]) / length(trials)
- })
-}
-with_lures_ratio <- function(stimulus_type, length=16) {
- sapply(1:length(stimulus_type), function(i) {
- trials <- stimulus_type[max(1,i-length):i]
- length(trials[trials=="lure"]) / length(trials)
- })
-}
-NB2 <- NB %>%
- filter(participant=="P13") %>%
- group_by(participant, condition, block) %>%
- mutate(n = ifelse(condition=='2-back',2,3)) %>%
- mutate(stimulus_type = with_lures(stimulus, stimulus_type, n)) %>%
- mutate(history = with_history(stimulus, window_size)) %>%
- mutate(x_sl = with_skewness(history)) %>%
- mutate(x_ul = with_lumpiness(history)) %>%
- mutate(x_t = with_targets_ratio(stimulus_type, window_size)) %>%
- mutate(x_l = with_lures_ratio(stimulus_type, window_size)) %>%
- ungroup()
-
-pca <- prcomp(~x_sl+x_ul+x_t+x_l, NB2, center = TRUE,scale. = TRUE, na.action=na.exclude)
-NB2 <- NB2 %>% mutate(pc1=pca$x[,'PC1'], pc2=pca$x[,'PC2'])
-# caret
-library(caret)
-# Compile cross-validation settings
-any(is.na(NB2))[1] TRUENB2 <- na.omit(NB2)
-# set.seed(100)
-# trainingfold <- createMultiFolds(NB2@correct, k = 5, times = 10)
-#
-# # PLS
-# mod1 <- train(correct ~ ., data = NB2[,c("correct","x_sl","x_ul","x_t","x_l")],
-# method = "pls",
-# metric = "Accuracy",
-# tuneLength = 20,
-# trControl = trainControl("repeatedcv", index = trainingfold, selectionFunction = "oneSE"),
-# preProc = c("zv","center","scale"))
-#
-# # Check CV
-# plot(mod1)
-plsResult <- plsR(correct ~ ., data=NB2[,c("correct","x_sl","x_ul","x_t","x_l")],3)____************************************************____
-____Component____ 1 ____
-____Component____ 2 ____
-____Component____ 3 ____
-____Predicting X without NA neither in X nor in Y____
-****________________________________________________****plsResultNumber of required components:
-[1] 3
-Number of successfully computed components:
-[1] 3
-Coefficients:
- [,1]
-Intercept 1.008159108
-x_sl -0.012385995
-x_ul 0.019020579
-x_t -0.009732895
-x_l 0.015588407
-Information criteria and Fit statistics:
- AIC RSS_Y R2_Y R2_residY RSS_residY AIC.std DoF.dof
-Nb_Comp_0 -400.8879 0.9942529 NA NA 173.0000 496.7877 1.000000
-Nb_Comp_1 -399.2749 0.9920439 0.002221709 0.002221709 172.6156 498.4007 4.020014
-Nb_Comp_2 -397.5844 0.9902807 0.003995089 0.003995089 172.3088 500.0912 5.000000
-Nb_Comp_3 -396.2011 0.9867774 0.007518680 0.007518680 171.6993 501.4745 5.000000
- sigmahat.dof AIC.dof BIC.dof GMDL.dof DoF.naive sigmahat.naive
-Nb_Comp_0 0.07580980 0.005780156 0.005884498 -441.1050 1 0.07580980
-Nb_Comp_1 0.07617155 0.005969500 0.006392969 -427.5293 2 0.07594536
-Nb_Comp_2 0.07632287 0.006026049 0.006554844 -423.3755 3 0.07609938
-Nb_Comp_3 0.07618775 0.006004731 0.006531655 -423.6749 4 0.07618775
- AIC.naive BIC.naive GMDL.naive
-Nb_Comp_0 0.005780156 0.005884498 -441.1050
-Nb_Comp_1 0.005833993 0.006043423 -436.3376
-Nb_Comp_2 0.005890962 0.006206384 -431.7944
-Nb_Comp_3 0.005938011 0.006359551 -427.5722