## Load execution‑phase data
setwd("C:/Users/Marcel/Desktop/Scripts")
df <- read_excel("hurst_df.xlsx")
df$Block <- factor(df$Block, ordered = FALSE, levels = c('1','2','3','4','5','6','7','8','9','10'))
df$Trial <- as.numeric(df$Trial)
df$Subject <- factor(df$Subject)
df$trial_acc <- factor(df$trial_acc)
df_acc <- df %>%
dplyr::filter(trial_acc == "1")
str(df)
## tibble [10,560 × 12] (S3: tbl_df/tbl/data.frame)
## $ Subject : Factor w/ 22 levels "11","12","13",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Block : Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Trial : num [1:10560] 1 2 3 4 5 6 7 8 9 10 ...
## $ Sequence : chr [1:10560] "1" "2" "1" "1" ...
## $ trial_mean_rt : num [1:10560] 1466 930 981 1210 1494 ...
## $ trial_total_rt: num [1:10560] 8796 5578 5884 7263 8965 ...
## $ trial_acc : Factor w/ 2 levels "0","1": 1 2 1 1 1 2 2 2 2 2 ...
## $ Logtrialmeanrt: num [1:10560] 7.29 6.83 6.89 7.1 7.31 ...
## $ Logtrialsumrt : num [1:10560] 9.08 8.63 8.68 8.89 9.1 ...
## $ Phase : chr [1:10560] "Learning" "Learning" "Learning" "Learning" ...
## $ H_prep_vm : num [1:10560] 0.817 0.995 0.987 0.84 0.797 ...
## $ H_exec_vm : num [1:10560] 0.994 0.993 0.991 0.996 0.993 ...
df$Block_Num <- as.numeric(as.character(df$Block))
# Subset for Learning and Test Phases
df_learning <- df %>% filter(Phase == "Learning")
df_test <- df %>% filter(Phase == "Test")
# Subset for Learning and Test Phases with only accurate Trials
df_learning_acc <- subset(df_acc, Block %in% c("1","2","3","4","5","6","7","8"))
df_test_acc <- subset(df_acc, Block %in% c("9", "10"))
df_learning_acc$Block <- factor(df_learning_acc$Block, levels = c('1','2','3','4','5','6','7','8'))
df_test_acc$Block <- factor(df_test_acc$Block, levels = c('9', '10'))
df_learning_acc$Subject <- factor(df_learning_acc$Subject)
df_test_acc$Subject <- factor(df_test_acc$Subject)
str(df_learning_acc)
## tibble [7,606 × 12] (S3: tbl_df/tbl/data.frame)
## $ Subject : Factor w/ 22 levels "11","12","13",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Block : Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Trial : num [1:7606] 2 6 7 8 9 10 12 13 14 15 ...
## $ Sequence : chr [1:7606] "2" "2" "2" "1" ...
## $ trial_mean_rt : num [1:7606] 930 1237 853 1031 837 ...
## $ trial_total_rt: num [1:7606] 5578 7420 5117 6188 5021 ...
## $ trial_acc : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Logtrialmeanrt: num [1:7606] 6.83 7.12 6.75 6.94 6.73 ...
## $ Logtrialsumrt : num [1:7606] 8.63 8.91 8.54 8.73 8.52 ...
## $ Phase : chr [1:7606] "Learning" "Learning" "Learning" "Learning" ...
## $ H_prep_vm : num [1:7606] 0.995 0.846 0.985 0.991 0.971 ...
## $ H_exec_vm : num [1:7606] 0.993 0.995 0.995 0.993 0.991 ...
str(df_test_acc)
## tibble [1,833 × 12] (S3: tbl_df/tbl/data.frame)
## $ Subject : Factor w/ 22 levels "11","12","13",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Block : Factor w/ 2 levels "9","10": 1 1 1 1 1 1 1 1 1 1 ...
## $ Trial : num [1:1833] 2 3 4 5 6 9 10 11 12 13 ...
## $ Sequence : chr [1:1833] "2" "1" "2" "2" ...
## $ trial_mean_rt : num [1:1833] 282 217 240 192 243 ...
## $ trial_total_rt: num [1:1833] 1689 1303 1437 1151 1458 ...
## $ trial_acc : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Logtrialmeanrt: num [1:1833] 5.64 5.38 5.48 5.26 5.49 ...
## $ Logtrialsumrt : num [1:1833] 7.43 7.17 7.27 7.05 7.28 ...
## $ Phase : chr [1:1833] "Test" "Test" "Test" "Test" ...
## $ H_prep_vm : num [1:1833] 0.979 0.994 0.715 0.956 0.993 ...
## $ H_exec_vm : num [1:1833] 0.972 0.904 0.975 0.969 0.981 ...
## ===============================================================
## 1) Learning phase model (Blocks 1–8) - Accuracy as predictor
## ===============================================================
mod_rtacc_learn <- glmmTMB(
Logtrialmeanrt ~ Block + trial_acc + (1 | Subject),
family = gaussian(),
data = df_learning
)
summary(mod_rtacc_learn)
## Family: gaussian ( identity )
## Formula: Logtrialmeanrt ~ Block + trial_acc + (1 | Subject)
## Data: df_learning
##
## AIC BIC logLik -2*log(L) df.resid
## 3871.3 3948.8 -1924.6 3849.3 8437
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.11881 0.3447
## Residual 0.09086 0.3014
## Number of obs: 8448, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.0909
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.96814 0.07459 93.42 <2e-16 ***
## Block2 -0.35766 0.01321 -27.08 <2e-16 ***
## Block3 -0.42580 0.01322 -32.21 <2e-16 ***
## Block4 -0.49019 0.01321 -37.12 <2e-16 ***
## Block5 -0.53660 0.01320 -40.65 <2e-16 ***
## Block6 -0.55208 0.01320 -41.83 <2e-16 ***
## Block7 -0.60478 0.01319 -45.84 <2e-16 ***
## Block8 -0.64505 0.01321 -48.85 <2e-16 ***
## trial_acc1 -0.58619 0.01123 -52.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(mod_rtacc_learn)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Logtrialmeanrt
## Chisq Df Pr(>Chisq)
## Block 3350.2 7 < 2.2e-16 ***
## trial_acc 2722.7 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2(mod_rtacc_learn)
## # R2 for Mixed Models
##
## Conditional R2: 0.682
## Marginal R2: 0.266
# Predicted Log RT per Block, separately for each accuracy level
emm_rt_block_by_acc_learn <- emmeans(mod_rtacc_learn, ~ Block | trial_acc)
emm_rt_block_by_acc_learn
## trial_acc = 0:
## Block emmean SE df lower.CL upper.CL
## 1 6.97 0.0746 8437 6.82 7.11
## 2 6.61 0.0748 8437 6.46 6.76
## 3 6.54 0.0748 8437 6.40 6.69
## 4 6.48 0.0748 8437 6.33 6.62
## 5 6.43 0.0748 8437 6.28 6.58
## 6 6.42 0.0748 8437 6.27 6.56
## 7 6.36 0.0748 8437 6.22 6.51
## 8 6.32 0.0748 8437 6.18 6.47
##
## trial_acc = 1:
## Block emmean SE df lower.CL upper.CL
## 1 6.38 0.0741 8437 6.24 6.53
## 2 6.02 0.0741 8437 5.88 6.17
## 3 5.96 0.0741 8437 5.81 6.10
## 4 5.89 0.0741 8437 5.75 6.04
## 5 5.85 0.0741 8437 5.70 5.99
## 6 5.83 0.0741 8437 5.68 5.98
## 7 5.78 0.0741 8437 5.63 5.92
## 8 5.74 0.0741 8437 5.59 5.88
##
## Confidence level used: 0.95
# Pairwise block comparisons within each accuracy level
pairs(emm_rt_block_by_acc_learn)
## trial_acc = 0:
## contrast estimate SE df t.ratio p.value
## Block1 - Block2 0.3577 0.0132 8437 27.083 <.0001
## Block1 - Block3 0.4258 0.0132 8437 32.209 <.0001
## Block1 - Block4 0.4902 0.0132 8437 37.119 <.0001
## Block1 - Block5 0.5366 0.0132 8437 40.648 <.0001
## Block1 - Block6 0.5521 0.0132 8437 41.832 <.0001
## Block1 - Block7 0.6048 0.0132 8437 45.841 <.0001
## Block1 - Block8 0.6451 0.0132 8437 48.845 <.0001
## Block2 - Block3 0.0681 0.0131 8437 5.194 <.0001
## Block2 - Block4 0.1325 0.0131 8437 10.103 <.0001
## Block2 - Block5 0.1789 0.0131 8437 13.641 <.0001
## Block2 - Block6 0.1944 0.0131 8437 14.820 <.0001
## Block2 - Block7 0.2471 0.0131 8437 18.837 <.0001
## Block2 - Block8 0.2874 0.0131 8437 21.908 <.0001
## Block3 - Block4 0.0644 0.0131 8437 4.909 <.0001
## Block3 - Block5 0.1108 0.0131 8437 8.446 <.0001
## Block3 - Block6 0.1263 0.0131 8437 9.625 <.0001
## Block3 - Block7 0.1790 0.0131 8437 13.642 <.0001
## Block3 - Block8 0.2193 0.0131 8437 16.713 <.0001
## Block4 - Block5 0.0464 0.0131 8437 3.538 0.0097
## Block4 - Block6 0.0619 0.0131 8437 4.717 0.0001
## Block4 - Block7 0.1146 0.0131 8437 8.735 <.0001
## Block4 - Block8 0.1549 0.0131 8437 11.805 <.0001
## Block5 - Block6 0.0155 0.0131 8437 1.180 0.9378
## Block5 - Block7 0.0682 0.0131 8437 5.197 <.0001
## Block5 - Block8 0.1085 0.0131 8437 8.267 <.0001
## Block6 - Block7 0.0527 0.0131 8437 4.018 0.0015
## Block6 - Block8 0.0930 0.0131 8437 7.087 <.0001
## Block7 - Block8 0.0403 0.0131 8437 3.070 0.0447
##
## trial_acc = 1:
## contrast estimate SE df t.ratio p.value
## Block1 - Block2 0.3577 0.0132 8437 27.083 <.0001
## Block1 - Block3 0.4258 0.0132 8437 32.209 <.0001
## Block1 - Block4 0.4902 0.0132 8437 37.119 <.0001
## Block1 - Block5 0.5366 0.0132 8437 40.648 <.0001
## Block1 - Block6 0.5521 0.0132 8437 41.832 <.0001
## Block1 - Block7 0.6048 0.0132 8437 45.841 <.0001
## Block1 - Block8 0.6451 0.0132 8437 48.845 <.0001
## Block2 - Block3 0.0681 0.0131 8437 5.194 <.0001
## Block2 - Block4 0.1325 0.0131 8437 10.103 <.0001
## Block2 - Block5 0.1789 0.0131 8437 13.641 <.0001
## Block2 - Block6 0.1944 0.0131 8437 14.820 <.0001
## Block2 - Block7 0.2471 0.0131 8437 18.837 <.0001
## Block2 - Block8 0.2874 0.0131 8437 21.908 <.0001
## Block3 - Block4 0.0644 0.0131 8437 4.909 <.0001
## Block3 - Block5 0.1108 0.0131 8437 8.446 <.0001
## Block3 - Block6 0.1263 0.0131 8437 9.625 <.0001
## Block3 - Block7 0.1790 0.0131 8437 13.642 <.0001
## Block3 - Block8 0.2193 0.0131 8437 16.713 <.0001
## Block4 - Block5 0.0464 0.0131 8437 3.538 0.0097
## Block4 - Block6 0.0619 0.0131 8437 4.717 0.0001
## Block4 - Block7 0.1146 0.0131 8437 8.735 <.0001
## Block4 - Block8 0.1549 0.0131 8437 11.805 <.0001
## Block5 - Block6 0.0155 0.0131 8437 1.180 0.9378
## Block5 - Block7 0.0682 0.0131 8437 5.197 <.0001
## Block5 - Block8 0.1085 0.0131 8437 8.267 <.0001
## Block6 - Block7 0.0527 0.0131 8437 4.018 0.0015
## Block6 - Block8 0.0930 0.0131 8437 7.087 <.0001
## Block7 - Block8 0.0403 0.0131 8437 3.070 0.0447
##
## P value adjustment: tukey method for comparing a family of 8 estimates
# accuracy differences *within each block*
emm_acc_by_block_learn <- emmeans(mod_rtacc_learn, ~ trial_acc | Block)
emm_acc_by_block_learn
## Block = 1:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.97 0.0746 8437 6.82 7.11
## 1 6.38 0.0741 8437 6.24 6.53
##
## Block = 2:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.61 0.0748 8437 6.46 6.76
## 1 6.02 0.0741 8437 5.88 6.17
##
## Block = 3:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.54 0.0748 8437 6.40 6.69
## 1 5.96 0.0741 8437 5.81 6.10
##
## Block = 4:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.48 0.0748 8437 6.33 6.62
## 1 5.89 0.0741 8437 5.75 6.04
##
## Block = 5:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.43 0.0748 8437 6.28 6.58
## 1 5.85 0.0741 8437 5.70 5.99
##
## Block = 6:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.42 0.0748 8437 6.27 6.56
## 1 5.83 0.0741 8437 5.68 5.98
##
## Block = 7:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.36 0.0748 8437 6.22 6.51
## 1 5.78 0.0741 8437 5.63 5.92
##
## Block = 8:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.32 0.0748 8437 6.18 6.47
## 1 5.74 0.0741 8437 5.59 5.88
##
## Confidence level used: 0.95
pairs(emm_acc_by_block_learn)
## Block = 1:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 2:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 3:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 4:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 5:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 6:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 7:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
##
## Block = 8:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.586 0.0112 8437 52.180 <.0001
## ===============================================================
## 2) Test phase model (Blocks 9–10) - Accuracy as predictor
## ===============================================================
mod_rtacc_test <- glmmTMB(
Logtrialmeanrt ~ Block + trial_acc + (1 | Subject),
family = gaussian(),
data = df_test
)
summary(mod_rtacc_test)
## Family: gaussian ( identity )
## Formula: Logtrialmeanrt ~ Block + trial_acc + (1 | Subject)
## Data: df_test
##
## AIC BIC logLik -2*log(L) df.resid
## 904.9 933.2 -447.5 894.9 2107
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.1465 0.3828
## Residual 0.0848 0.2912
## Number of obs: 2112, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.0848
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.21228 0.08386 74.08 <2e-16 ***
## Block10 0.17217 0.01271 13.55 <2e-16 ***
## trial_acc1 -0.50705 0.01915 -26.48 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(mod_rtacc_test)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Logtrialmeanrt
## Chisq Df Pr(>Chisq)
## Block 183.59 1 < 2.2e-16 ***
## trial_acc 700.99 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2(mod_rtacc_test)
## # R2 for Mixed Models
##
## Conditional R2: 0.686
## Marginal R2: 0.144
# Predicted Log RT per Block, separately for each accuracy level
emm_rt_block_by_acc_test <- emmeans(mod_rtacc_test, ~ Block | trial_acc)
emm_rt_block_by_acc_test
## trial_acc = 0:
## Block emmean SE df lower.CL upper.CL
## 9 6.21 0.0839 2107 6.05 6.38
## 10 6.38 0.0837 2107 6.22 6.55
##
## trial_acc = 1:
## Block emmean SE df lower.CL upper.CL
## 9 5.71 0.0821 2107 5.54 5.87
## 10 5.88 0.0822 2107 5.72 6.04
##
## Confidence level used: 0.95
# Pairwise block comparisons within each accuracy level
pairs(emm_rt_block_by_acc_test)
## trial_acc = 0:
## contrast estimate SE df t.ratio p.value
## Block9 - Block10 -0.172 0.0127 2107 -13.549 <.0001
##
## trial_acc = 1:
## contrast estimate SE df t.ratio p.value
## Block9 - Block10 -0.172 0.0127 2107 -13.549 <.0001
# accuracy differences *within each block*
emm_acc_by_block_test <- emmeans(mod_rtacc_test, ~ trial_acc | Block)
emm_acc_by_block_test
## Block = 9:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.21 0.0839 2107 6.05 6.38
## 1 5.71 0.0821 2107 5.54 5.87
##
## Block = 10:
## trial_acc emmean SE df lower.CL upper.CL
## 0 6.38 0.0837 2107 6.22 6.55
## 1 5.88 0.0822 2107 5.72 6.04
##
## Confidence level used: 0.95
pairs(emm_acc_by_block_test)
## Block = 9:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.507 0.0192 2107 26.476 <.0001
##
## Block = 10:
## contrast estimate SE df t.ratio p.value
## trial_acc0 - trial_acc1 0.507 0.0192 2107 26.476 <.0001
# make accuracy a factor with readable labels
df <- df %>%
mutate(
trial_acc_fac = factor(
trial_acc,
levels = c(0, 1),
labels = c("Inaccurate", "Accurate")
)
)
p_rt <- ggplot(
df,
aes(
x = Block,
y = Logtrialmeanrt,
colour = trial_acc_fac,
fill = trial_acc_fac
)
) +
geom_boxplot(
position = position_dodge(width = 0.8),
alpha = 0.35,
outlier.shape = NA
) +
geom_jitter(
position = position_jitterdodge(
jitter.width = 0.15,
dodge.width = 0.8
),
alpha = 0.4,
size = 1
) +
geom_vline(
xintercept = 8.5,
linetype = "solid",
colour = "grey30",
linewidth = 1
) +
scale_colour_manual(
name = "Accuracy",
values = c("Inaccurate" = "#3C4F8CFF",
"Accurate" = "#67CC5CFF")
) +
scale_fill_manual(
name = "Accuracy",
values = c("Inaccurate" = "#3C4F8CFF",
"Accurate" = "#67CC5CFF")
) +
labs(
x = "Block",
y = "Log RT",
title = "Trial-wise Log RT across blocks"
) +
common_theme
p_rt
##
## =============================================================
## LEARNING PHASE – H_exec_vm ~ Block_Num
## Model summary
## =============================================================
## Family: gaussian ( identity )
## Formula: H_exec_vm ~ Block + (1 | Subject)
## Data: df_learning_acc
##
## AIC BIC logLik -2*log(L) df.resid
## -42783.4 -42714.0 21401.7 -42803.4 7596
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0002558 0.01600
## Residual 0.0002070 0.01439
## Number of obs: 7606, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.000207
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9913410 0.0034468 287.62 < 2e-16 ***
## Block2 -0.0029995 0.0006814 -4.40 1.07e-05 ***
## Block3 -0.0057796 0.0006798 -8.50 < 2e-16 ***
## Block4 -0.0061642 0.0006814 -9.05 < 2e-16 ***
## Block5 -0.0058291 0.0006820 -8.55 < 2e-16 ***
## Block6 -0.0068351 0.0006826 -10.01 < 2e-16 ***
## Block7 -0.0076954 0.0006834 -11.26 < 2e-16 ***
## Block8 -0.0098483 0.0006814 -14.45 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## LEARNING PHASE – H_exec_vm ~ Block_Num
## Type II/III ANOVA
## -------------------------------------------------------------
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: H_exec_vm
## Chisq Df Pr(>Chisq)
## Block 269.76 7 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.560
## Marginal R2: 0.016
##
## =============================================================
## LEARNING PHASE – H_prep_vm ~ Block_Num
## Model summary
## =============================================================
## Family: gaussian ( identity )
## Formula: H_prep_vm ~ Block + (1 | Subject)
## Data: df_learning_acc
##
## AIC BIC logLik -2*log(L) df.resid
## -9571.3 -9501.9 4795.6 -9591.3 7596
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.005511 0.07423
## Residual 0.016364 0.12792
## Number of obs: 7606, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.0164
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.824281 0.016441 50.13 < 2e-16 ***
## Block2 0.001371 0.006059 0.23 0.821062
## Block3 0.008765 0.006045 1.45 0.147027
## Block4 0.019093 0.006059 3.15 0.001626 **
## Block5 0.012002 0.006065 1.98 0.047813 *
## Block6 0.021470 0.006070 3.54 0.000404 ***
## Block7 0.036393 0.006077 5.99 2.11e-09 ***
## Block8 0.037565 0.006059 6.20 5.65e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## LEARNING PHASE – H_prep_vm ~ Block_Num
## Type II/III ANOVA
## -------------------------------------------------------------
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: H_prep_vm
## Chisq Df Pr(>Chisq)
## Block 83.047 7 3.288e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # R2 for Mixed Models
##
## Conditional R2: 0.258
## Marginal R2: 0.008
##
## =============================================================
## TEST PHASE – H_exec_vm ~ Block_Num
## Model summary
## =============================================================
## Family: gaussian ( identity )
## Formula: H_exec_vm ~ Block + (1 | Subject)
## Data: df_test_acc
##
## AIC BIC logLik -2*log(L) df.resid
## -9957.1 -9935.0 4982.5 -9965.1 1829
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.0003976 0.01994
## Residual 0.0002403 0.01550
## Number of obs: 1833, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.00024
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9812985 0.0042810 229.22 <2e-16 ***
## Block10 0.0008839 0.0007261 1.22 0.223
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## TEST PHASE – H_exec_vm ~ Block_Num
## Type II/III ANOVA
## -------------------------------------------------------------
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: H_exec_vm
## Chisq Df Pr(>Chisq)
## Block 1.4821 1 0.2235
## # R2 for Mixed Models
##
## Conditional R2: 0.623
## Marginal R2: 0.000
##
## =============================================================
## TEST PHASE – H_prep_vm ~ Block_Num
## Model summary
## =============================================================
## Family: gaussian ( identity )
## Formula: H_prep_vm ~ Block + (1 | Subject)
## Data: df_test_acc
##
## AIC BIC logLik -2*log(L) df.resid
## -2503.9 -2481.8 1255.9 -2511.9 1829
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.007384 0.08593
## Residual 0.014212 0.11921
## Number of obs: 1833, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.0142
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.848429 0.018728 45.3 <2e-16 ***
## Block10 0.006705 0.005583 1.2 0.23
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## TEST PHASE – H_prep_vm ~ Block_Num
## Type II/III ANOVA
## -------------------------------------------------------------
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: H_prep_vm
## Chisq Df Pr(>Chisq)
## Block 1.4421 1 0.2298
## # R2 for Mixed Models
##
## Conditional R2: 0.342
## Marginal R2: 0.001
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## ---------- EXECUTION H: across blocks ----------
p_exec <- ggplot(df_acc, aes(x = Block, y = H_exec_vm)) +
geom_boxplot(fill = "#67CC5CFF", alpha = 0.35, outlier.shape = NA) +
geom_jitter(width = 0.15, alpha = 0.4, size = 1, colour = "#67CC5CFF") +
scale_y_continuous(limits = c(0.8, 1.0)) +
geom_vline(xintercept = 8.5, linetype = "solid", colour = "grey30", linewidth = 1) +
labs(
x = "Block",
y = "Execution H",
title = "Execution H across blocks"
) +
common_theme
## ---------- PREPARATION H: across blocks ----------
p_prep <- ggplot(df_acc, aes(x = Block, y = H_prep_vm)) +
geom_boxplot(fill = "#3C4F8CFF", alpha = 0.35, outlier.shape = NA) +
geom_jitter(width = 0.15, alpha = 0.4, size = 1, colour = "#3C4F8CFF") +
geom_vline(xintercept = 8.5, linetype = "solid", colour = "grey30", linewidth = 1) +
labs(
x = "Block",
y = "Preparation H",
title = "Preparation H across blocks"
) +
common_theme
p_exec
## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).
p_prep
## ===============================================================
## 1) Learning phase model (Blocks 1–8) - Logtrialmeanrt as DV
## ===============================================================
model_learning <- glmmTMB(
Logtrialmeanrt ~ H_prep_vm * H_exec_vm * Block +
(1 | Subject),
data = df_learning_acc,
family = gaussian()
)
summary(model_learning)
## Family: gaussian ( identity )
## Formula:
## Logtrialmeanrt ~ H_prep_vm * H_exec_vm * Block + (1 | Subject)
## Data: df_learning_acc
##
## AIC BIC logLik -2*log(L) df.resid
## 1687.9 1923.7 -809.9 1619.9 7572
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.11729 0.3425
## Residual 0.07113 0.2667
## Number of obs: 7606, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.0711
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 25.525 4.611 5.536 3.10e-08 ***
## H_prep_vm -25.449 5.200 -4.894 9.86e-07 ***
## H_exec_vm -19.506 4.650 -4.195 2.73e-05 ***
## Block2 -19.482 5.387 -3.617 0.000298 ***
## Block3 -17.232 5.593 -3.081 0.002061 **
## Block4 -16.461 5.251 -3.135 0.001718 **
## Block5 -16.275 5.389 -3.020 0.002526 **
## Block6 -22.082 5.537 -3.988 6.67e-05 ***
## Block7 -22.889 5.314 -4.307 1.65e-05 ***
## Block8 -15.663 5.147 -3.043 0.002340 **
## H_prep_vm:H_exec_vm 25.902 5.245 4.938 7.88e-07 ***
## H_prep_vm:Block2 23.049 6.214 3.709 0.000208 ***
## H_prep_vm:Block3 20.660 6.316 3.271 0.001071 **
## H_prep_vm:Block4 19.650 5.992 3.279 0.001041 **
## H_prep_vm:Block5 20.796 6.096 3.411 0.000647 ***
## H_prep_vm:Block6 26.685 6.281 4.248 2.15e-05 ***
## H_prep_vm:Block7 27.454 5.988 4.584 4.55e-06 ***
## H_prep_vm:Block8 18.049 5.811 3.106 0.001898 **
## H_exec_vm:Block2 19.529 5.435 3.593 0.000327 ***
## H_exec_vm:Block3 17.342 5.644 3.072 0.002123 **
## H_exec_vm:Block4 16.386 5.300 3.091 0.001992 **
## H_exec_vm:Block5 16.106 5.440 2.960 0.003072 **
## H_exec_vm:Block6 21.906 5.591 3.918 8.93e-05 ***
## H_exec_vm:Block7 22.704 5.366 4.231 2.33e-05 ***
## H_exec_vm:Block8 15.229 5.196 2.931 0.003379 **
## H_prep_vm:H_exec_vm:Block2 -23.519 6.272 -3.750 0.000177 ***
## H_prep_vm:H_exec_vm:Block3 -21.286 6.376 -3.338 0.000843 ***
## H_prep_vm:H_exec_vm:Block4 -20.131 6.051 -3.327 0.000878 ***
## H_prep_vm:H_exec_vm:Block5 -21.238 6.156 -3.450 0.000561 ***
## H_prep_vm:H_exec_vm:Block6 -27.131 6.344 -4.277 1.90e-05 ***
## H_prep_vm:H_exec_vm:Block7 -27.959 6.049 -4.622 3.79e-06 ***
## H_prep_vm:H_exec_vm:Block8 -18.269 5.868 -3.113 0.001851 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(model_learning)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Logtrialmeanrt
## Chisq Df Pr(>Chisq)
## H_prep_vm 0.3157 1 0.5741815
## H_exec_vm 59.5933 1 1.166e-14 ***
## Block 3220.1052 7 < 2.2e-16 ***
## H_prep_vm:H_exec_vm 14.6604 1 0.0001287 ***
## H_prep_vm:Block 38.1020 7 2.898e-06 ***
## H_exec_vm:Block 17.1039 7 0.0167384 *
## H_prep_vm:H_exec_vm:Block 25.9254 7 0.0005194 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2(model_learning)
## # R2 for Mixed Models
##
## Conditional R2: 0.682
## Marginal R2: 0.157
# LEARNING
p_learning <- plot_model(
model_learning,
type = "pred",
terms = c("H_exec_vm", "H_prep_vm", "Block"),
colors = c("#3C4F8CFF", "#21918CFF", "#FDE725FF")
) +
geom_line(linewidth = 1) +
labs(
x = "Execution H",
y = "Log RT",
title = "Predicted values of Log RT",
colour = "Prep H",
fill = "Prep H"
) +
common_theme
p_learning
## ===============================================================
## Post-hoc: H_prep × H_exec interaction per Block
## ===============================================================
## Idea:
## For each block, relevel Block so that this block is the reference,
## refit the SAME model, and read off the coefficient for
## H_prep_vm:H_exec_vm. That coefficient is the simple
## H_prep × H_exec interaction in that block.
## Then collect all block-wise estimates and apply Holm correction.
## ===============================================================
blocks_learning <- levels(df_learning_acc$Block)
interaction_results_learning <- lapply(blocks_learning, function(b) {
## Relevel Block so that block b is the reference level
df_tmp <- df_learning_acc %>%
mutate(Block_ref = relevel(Block, ref = b))
## Refit the same model structure with Block_ref
mod_b <- glmmTMB(
Logtrialmeanrt ~ H_prep_vm * H_exec_vm * Block_ref +
(1 | Subject),
data = df_tmp,
family = gaussian()
)
## Extract the simple interaction term in this reference block:
## coefficient for H_prep_vm:H_exec_vm
coefs <- summary(mod_b)$coefficients$cond
est <- coefs["H_prep_vm:H_exec_vm", "Estimate"]
se <- coefs["H_prep_vm:H_exec_vm", "Std. Error"]
z <- coefs["H_prep_vm:H_exec_vm", "z value"]
p <- coefs["H_prep_vm:H_exec_vm", "Pr(>|z|)"]
data.frame(
Block = b,
Estimate = est,
SE = se,
z_value = z,
p_value = p,
stringsAsFactors = FALSE
)
})
interaction_table_learning <- bind_rows(interaction_results_learning) %>%
mutate(
p_adj_holm = p.adjust(p_value, method = "holm")
)
interaction_table_learning
## Block Estimate SE z_value p_value p_adj_holm
## 1 1 25.902115 5.245138 4.9383094 7.880274e-07 6.304219e-06
## 2 2 2.383638 3.453266 0.6902561 4.900332e-01 1.000000e+00
## 3 3 4.615964 3.656175 1.2625116 2.067648e-01 8.270590e-01
## 4 4 5.770418 3.032639 1.9027709 5.707044e-02 3.424226e-01
## 5 5 4.663589 3.229812 1.4439198 1.487615e-01 7.438077e-01
## 6 6 -1.229567 3.572486 -0.3441769 7.307133e-01 1.000000e+00
## 7 7 -2.056964 3.036948 -0.6773129 4.982074e-01 1.000000e+00
## 8 8 7.632627 2.657739 2.8718502 4.080764e-03 2.856535e-02
## ===============================================================
## 2) Test phase model (Blocks 9–10) - Logtrialmeanrt as DV
## ===============================================================
model_test <- glmmTMB(
Logtrialmeanrt ~ H_prep_vm * H_exec_vm * Block +
(1 | Subject),
family = gaussian(),
data = df_test_acc
)
summary(model_test)
## Family: gaussian ( identity )
## Formula:
## Logtrialmeanrt ~ H_prep_vm * H_exec_vm * Block + (1 | Subject)
## Data: df_test_acc
##
## AIC BIC logLik -2*log(L) df.resid
## 224.9 280.1 -102.5 204.9 1823
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.13018 0.3608
## Residual 0.06153 0.2481
## Number of obs: 1833, groups: Subject, 22
##
## Dispersion estimate for gaussian family (sigma^2): 0.0615
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.63935 3.33337 1.692 0.0907 .
## H_prep_vm -2.65272 3.57201 -0.743 0.4577
## H_exec_vm -0.09203 3.37772 -0.027 0.9783
## Block10 -6.90770 4.27426 -1.616 0.1061
## H_prep_vm:H_exec_vm 2.87813 3.62177 0.795 0.4268
## H_prep_vm:Block10 6.27672 4.66034 1.347 0.1780
## H_exec_vm:Block10 7.53499 4.32775 1.741 0.0817 .
## H_prep_vm:H_exec_vm:Block10 -6.74790 4.72071 -1.429 0.1529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Anova(model_test)
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Logtrialmeanrt
## Chisq Df Pr(>Chisq)
## H_prep_vm 0.0007 1 0.979249
## H_exec_vm 74.6506 1 < 2.2e-16 ***
## Block 289.5205 1 < 2.2e-16 ***
## H_prep_vm:H_exec_vm 0.1754 1 0.675315
## H_prep_vm:Block 22.8309 1 1.769e-06 ***
## H_exec_vm:Block 8.7220 1 0.003144 **
## H_prep_vm:H_exec_vm:Block 2.0433 1 0.152882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2(model_test)
## # R2 for Mixed Models
##
## Conditional R2: 0.706
## Marginal R2: 0.085
# TEST
p_test <- plot_model(
model_test,
type = "pred",
terms = c("H_exec_vm", "H_prep_vm", "Block"),
colors = c("#3C4F8CFF", "#21918CFF", "#FDE725FF")
) +
geom_line(linewidth = 1) +
labs(
x = "Execution H",
y = "Log RT",
title = "Predicted values of Log RT",
colour = "Prep H",
fill = "Prep H"
) +
common_theme
p_test
## ===============================================================
## Post-hoc: H_prep × H_exec interaction per Block
## ===============================================================
## Idea:
## For each block, relevel Block so that this block is the reference,
## refit the SAME model, and read off the coefficient for
## H_prep_vm:H_exec_vm. That coefficient is the simple
## H_prep × H_exec interaction in that block.
## Then collect all block-wise estimates and apply Holm correction.
## ===============================================================
blocks_test <- levels(df_test_acc$Block)
interaction_results_test <- lapply(blocks_test, function(b) {
## Relevel Block so that block b is the reference level
df_tmp <- df_test_acc %>%
mutate(Block_ref = relevel(Block, ref = b))
## Same model structure in the test phase
mod_b <- glmmTMB(
Logtrialmeanrt ~ H_prep_vm * H_exec_vm * Block_ref +
(1 | Subject),
data = df_tmp,
family = gaussian()
)
## Simple H_prep × H_exec interaction in block b
coefs <- summary(mod_b)$coefficients$cond
est <- coefs["H_prep_vm:H_exec_vm", "Estimate"]
se <- coefs["H_prep_vm:H_exec_vm", "Std. Error"]
z <- coefs["H_prep_vm:H_exec_vm", "z value"]
p <- coefs["H_prep_vm:H_exec_vm", "Pr(>|z|)"]
data.frame(
Block = b,
Estimate = est,
SE = se,
z_value = z,
p_value = p,
stringsAsFactors = FALSE
)
})
interaction_table_test <- bind_rows(interaction_results_test) %>%
mutate(
p_adj_holm = p.adjust(p_value, method = "holm")
)
interaction_table_test
## Block Estimate SE z_value p_value p_adj_holm
## 1 9 2.878128 3.621771 0.7946741 0.4268031 0.4310406
## 2 10 -3.869776 3.124496 -1.2385281 0.2155203 0.4310406
## ===============================================================
## STITCHED SURFACES: Blocks 1–8 from model_learning,
## Blocks 9–10 from model_test
## ===============================================================
## ---------------------------------------------------------------
## Hyperparameters
## ---------------------------------------------------------------
q_lower <- 0.025
q_upper <- 0.975
q_lower_pct <- q_lower * 100
q_upper_pct <- q_upper * 100
viridis_option <- "viridis"
viridis_begin <- 0
viridis_end <- 1
fast_color <- "light"
viridis_direction <- if (fast_color == "dark") 1L else -1L
## ---------------------------------------------------------------
## Block sets and labels
## ---------------------------------------------------------------
blocks_learning <- levels(df_learning_acc$Block)
blocks_test <- levels(df_test_acc$Block)
blocks_all <- c(blocks_learning, blocks_test)
block_labs_all <- setNames(
paste0("Block ", blocks_all),
blocks_all
)
# block_labs_all["10"] <- "Block 10 (unfamiliar)"
## ===============================================================
## A. WITHIN-BLOCK SURFACES (per-block H ranges)
## ===============================================================
## ---------------------------
## Learning within-block grids
## ---------------------------
grid_list_block_learning <- lapply(blocks_learning, function(b) {
df_b <- df_learning_acc %>% dplyr::filter(Block == b)
H_exec_seq_b <- seq(
quantile(df_b$H_exec_vm, q_lower, na.rm = TRUE),
quantile(df_b$H_exec_vm, q_upper, na.rm = TRUE),
length.out = 100
)
H_prep_seq_b <- seq(
quantile(df_b$H_prep_vm, q_lower, na.rm = TRUE),
quantile(df_b$H_prep_vm, q_upper, na.rm = TRUE),
length.out = 100
)
expand.grid(
H_exec_vm = H_exec_seq_b,
H_prep_vm = H_prep_seq_b,
Block = b,
KEEP.OUT.ATTRS = FALSE,
stringsAsFactors = FALSE
)
})
pred_grid_block_learning <- bind_rows(grid_list_block_learning)
pred_grid_block_learning$Block <- factor(
pred_grid_block_learning$Block,
levels = blocks_all
)
pred_grid_block_learning <- as.data.frame(pred_grid_block_learning)
pred_grid_block_learning$pred_logrt <- predict(
model_learning,
newdata = pred_grid_block_learning,
type = "response",
re.form = NA,
allow.new.levels = TRUE
)
## ---------------------------
## Test within-block grids
## ---------------------------
grid_list_block_test <- lapply(blocks_test, function(b) {
df_b <- df_test_acc %>% dplyr::filter(Block == b)
H_exec_seq_b <- seq(
quantile(df_b$H_exec_vm, q_lower, na.rm = TRUE),
quantile(df_b$H_exec_vm, q_upper, na.rm = TRUE),
length.out = 100
)
H_prep_seq_b <- seq(
quantile(df_b$H_prep_vm, q_lower, na.rm = TRUE),
quantile(df_b$H_prep_vm, q_upper, na.rm = TRUE),
length.out = 100
)
expand.grid(
H_exec_vm = H_exec_seq_b,
H_prep_vm = H_prep_seq_b,
Block = b,
KEEP.OUT.ATTRS = FALSE,
stringsAsFactors = FALSE
)
})
pred_grid_block_test <- bind_rows(grid_list_block_test)
pred_grid_block_test$Block <- factor(
pred_grid_block_test$Block,
levels = blocks_all
)
pred_grid_block_test <- as.data.frame(pred_grid_block_test)
pred_grid_block_test$pred_logrt <- predict(
model_test,
newdata = pred_grid_block_test,
type = "response",
re.form = NA,
allow.new.levels = TRUE
)
## ---------------------------
## Combine within-block grids
## ---------------------------
pred_grid_block_all <- bind_rows(
pred_grid_block_learning,
pred_grid_block_test
)
## ===============================================================
## B. COMMON COLOR RANGE AND THEME FOR MODEL SURFACES
## ===============================================================
rt_range_all <- range(pred_grid_block_all$pred_logrt, na.rm = TRUE)
## ===============================================================
## C. STITCHED MODEL HEATMAPS (WITHIN-BLOCK RANGES)
## ===============================================================
p_block_all <- ggplot() +
geom_tile(
data = pred_grid_block_all,
aes(
x = H_prep_vm,
y = H_exec_vm,
fill = pred_logrt
)
) +
facet_wrap(
~ Block,
ncol = 5,
labeller = labeller(Block = block_labs_all)
) +
scale_fill_viridis_c(
option = viridis_option,
begin = viridis_begin,
end = viridis_end,
direction = viridis_direction,
limits = rt_range_all,
name = "Predicted\nlog RT"
) +
labs(
title = "Predicted RT surfaces",
x = "Preparation H",
y = "Execution H"
) +
common_theme
print(p_block_all)
## ===============================================================
## Mosaic of prep/exec H quadrants by block
## - Area shows number of trials
## - Fill shows mean log RT
## ===============================================================
## --- Global bins across all 10 blocks ---------------------------
prep_breaks <- quantile(df_acc$H_prep_vm, probs = c(0, 0.5, 1), na.rm = TRUE)
exec_breaks <- quantile(df_acc$H_exec_vm, probs = c(0, 0.5, 1), na.rm = TRUE)
df_quad <- df_acc %>%
mutate(
prep_bin = cut(
H_prep_vm,
breaks = prep_breaks,
labels = c("🡻 prep H", " 🢁 prep H"),
include.lowest = TRUE
),
exec_bin = cut(
H_exec_vm,
breaks = exec_breaks,
labels = c("🢀 exec H", " 🢂 exec H"),
include.lowest = TRUE
)
) %>%
group_by(Block, prep_bin, exec_bin) %>%
summarise(
mean_rt = mean(Logtrialmeanrt, na.rm = TRUE),
n = n(),
.groups = "drop"
)
## --- Mosaic coordinates (area proportional to n) -----------------
prep_marg <- df_quad %>%
group_by(Block, prep_bin) %>%
summarise(n_prep = sum(n), .groups = "drop") %>%
group_by(Block) %>%
mutate(
total_n = sum(n_prep),
width = n_prep / total_n,
x_min = dplyr::lag(cumsum(width), default = 0),
x_max = cumsum(width)
) %>%
ungroup()
df_mosaic <- df_quad %>%
left_join(prep_marg, by = c("Block", "prep_bin")) %>%
group_by(Block, prep_bin) %>%
mutate(
height = n / n_prep,
y_min = dplyr::lag(cumsum(height), default = 0),
y_max = cumsum(height)
) %>%
ungroup()
## --- Axis label positions (tighter) ------------------------------------
prep_labels <- prep_marg %>%
mutate(
x = (x_min + x_max) / 2,
y = -0.06
)
exec_marg2 <- df_mosaic %>%
group_by(Block, exec_bin) %>%
summarise(
y_min = min(y_min),
y_max = max(y_max),
.groups = "drop"
) %>%
mutate(
y = (y_min + y_max) / 2,
x = -0.06
)
## --- Facet layout info -----------------------------------------------
n_cols <- 5
n_blocks <- length(levels(df_acc$Block))
n_rows <- ceiling(n_blocks / n_cols)
block_levels <- levels(df_acc$Block)
bottom_row_blocks <- block_levels[((n_rows - 1) * n_cols + 1):n_blocks]
left_col_blocks <- block_levels[seq(1, n_blocks, by = n_cols)]
## --- Plot --------------------------------------------------------------
gg_mosaic <- ggplot() +
geom_rect(
data = df_mosaic,
aes(
xmin = x_min,
xmax = x_max,
ymin = y_min,
ymax = y_max,
fill = mean_rt
),
color = "grey20"
) +
geom_text(
data = prep_labels %>% dplyr::filter(Block %in% bottom_row_blocks),
aes(x = x, y = y, label = prep_bin),
inherit.aes = FALSE,
size = 3
) +
geom_text(
data = exec_marg2 %>% dplyr::filter(Block %in% left_col_blocks),
aes(x = x, y = y, label = exec_bin),
inherit.aes = FALSE,
angle = 90,
hjust = 0.5,
size = 3
) +
facet_wrap(
~ Block,
nrow = 2,
labeller = labeller(Block = block_labs_all)
) +
scale_fill_viridis_c(
option = "viridis",
direction = -1,
name = "Mean\nlog RT"
) +
coord_equal(clip = "off") +
labs(
x = NULL,
y = NULL,
title = "Prep/exec H quadrants over blocks") +
theme_minimal() +
theme(
panel.grid = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
strip.background = element_rect(fill = "grey90", color = NA),
strip.text = element_text(size = 12, face = "bold"),
plot.margin = margin(10, 10, 25, 25),
legend.position = "right",
legend.margin = margin(t = 2, r = 0, b = 0, l = 0)
)
gg_mosaic
##
## =============================================================
## LEARNING PHASE – highprep_lowexec ~ Block
## Model summary
## =============================================================
## Family: binomial ( logit )
## Formula: highprep_lowexec ~ Block + (1 | Subject)
## Data: df_learning_acc
##
## AIC BIC logLik -2*log(L) df.resid
## 7054.5 7116.9 -3518.3 7036.5 7597
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 2.502 1.582
## Number of obs: 7606, groups: Subject, 22
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.6441 0.3596 -7.353 1.94e-13 ***
## Block2 0.5626 0.1442 3.900 9.60e-05 ***
## Block3 1.0761 0.1393 7.726 1.11e-14 ***
## Block4 1.3583 0.1378 9.855 < 2e-16 ***
## Block5 1.1818 0.1390 8.500 < 2e-16 ***
## Block6 1.1215 0.1394 8.043 8.76e-16 ***
## Block7 1.4282 0.1380 10.347 < 2e-16 ***
## Block8 1.6068 0.1371 11.717 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## LEARNING PHASE – highprep_lowexec ~ Block
## Type II ANOVA (Wald chi-square)
## -------------------------------------------------------------
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: highprep_lowexec
## Chisq Df Pr(>Chisq)
## Block 199.75 7 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## LEARNING PHASE – highprep_lowexec ~ Block
## R2 (marginal / conditional)
## -------------------------------------------------------------
## # R2 for Mixed Models
##
## Conditional R2: 0.453
## Marginal R2: 0.037
##
## -------------------------------------------------------------
## LEARNING PHASE – Odds ratios by Block (reference = Block 1)
## -------------------------------------------------------------
## Block 1 (reference): OR = 1.000 (reference level)
## Block 2: OR = 1.755; 95% CI [1.323, 2.329]
## Block 3: OR = 2.933; 95% CI [2.232, 3.854]
## Block 4: OR = 3.890; 95% CI [2.969, 5.096]
## Block 5: OR = 3.260; 95% CI [2.483, 4.282]
## Block 6: OR = 3.069; 95% CI [2.335, 4.034]
## Block 7: OR = 4.171; 95% CI [3.182, 5.467]
## Block 8: OR = 4.987; 95% CI [3.812, 6.525]
##
## =============================================================
## TEST PHASE – highprep_lowexec ~ Block
## Model summary
## =============================================================
## Family: binomial ( logit )
## Formula: highprep_lowexec ~ Block + (1 | Subject)
## Data: df_test_acc
##
## AIC BIC logLik -2*log(L) df.resid
## 1884.8 1901.4 -939.4 1878.8 1830
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 1.964 1.401
## Number of obs: 1833, groups: Subject, 22
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.05362 0.31262 -3.370 0.000751 ***
## Block10 -0.02608 0.11541 -0.226 0.821239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## -------------------------------------------------------------
## TEST PHASE – highprep_lowexec ~ Block
## Type II ANOVA (Wald chi-square)
## -------------------------------------------------------------
## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: highprep_lowexec
## Chisq Df Pr(>Chisq)
## Block 0.0511 1 0.8212
##
## -------------------------------------------------------------
## TEST PHASE – highprep_lowexec ~ Block
## R2 (marginal / conditional)
## -------------------------------------------------------------
## # R2 for Mixed Models
##
## Conditional R2: 0.374
## Marginal R2: 0.000
##
## -------------------------------------------------------------
## TEST PHASE – Odds ratios by Block (reference = Block 9)
## -------------------------------------------------------------
## Block 9 (reference): OR = 1.000 (reference level)
## Block 10: OR = 0.974; 95% CI [0.777, 1.222]
