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@@ -6,7 +6,7 @@
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We load the data and relevant functions:
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-```{r, warning=FALSE, message=FALSE, echo=TRUE}
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+```{r, warning=FALSE, message=FALSE}
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# find the path to the root of this project:
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if (!requireNamespace("here")) install.packages("here")
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if ( basename(here::here()) == "highspeed" ) {
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@@ -70,7 +70,6 @@ trial as the specific stimulus identities are not considered important here.
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We verify if the correct number of trials was used for the calculations.
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Next, we average across participants. We calculate the average probability
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of each serial event and also calculate the standard error of them mean.
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-Finally, we are plotting the data
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```{r}
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dt_pred_rep_timecourses = dt_pred_rep %>%
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@@ -85,10 +84,13 @@ dt_pred_rep_timecourses = dt_pred_rep %>%
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mean_probability = mean(mean_probability),
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sem_upper = mean(mean_probability) + (sd(mean_probability)/sqrt(.N)),
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sem_lower = mean(mean_probability) - (sd(mean_probability)/sqrt(.N))
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- )] %>% verify(all(num_sub == 36))
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+ )] %>%
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+ verify(all(num_sub == 36))
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```
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-```{r, echo=TRUE}
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+We define the colors used for plotting as well as the facet labels:
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+
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+```{r}
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# define colors for plotting:
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colors_inseq = colorRampPalette(c("dodgerblue", "red"), space = "Lab")(2)
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colors_outseq = colorRampPalette(c("gray70", "gray90"), alpha = TRUE)(3)
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@@ -108,6 +110,8 @@ names(facet_labels_new) = facet_labels_old
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#### Figure 4a
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+We plot the probability time courses:
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+
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```{r, echo=TRUE}
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trs = c(2, 7)
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plot_rep_probas <- function(data, xmin = 1, xmax = 13) {
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@@ -146,7 +150,7 @@ fig_s1_a = plot_rep_probas(data = subset(dt_pred_rep_timecourses, classification
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fig_a; fig_s1_a;
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```
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-```{r, echo=FALSE}
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+```{r, echo=FALSE, eval=FALSE, include=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition_timecourse_forward_backward.pdf",
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plot = fig_a, device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 4.5, height = 2.5)
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@@ -159,6 +163,8 @@ ggsave(filename = "highspeed_plot_decoding_repetition_timecourses_supplement.pdf
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#### Figure 4b
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+We calculate the mean decoding probabilities for each event:
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+
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```{r, results="hold"}
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# define the subset of selected TRs:
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trs = seq(2, 7)
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@@ -166,7 +172,7 @@ trs = seq(2, 7)
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dt_pred_rep_prob = dt_pred_rep %>%
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# only consider the two extreme conditions (2 and 9):
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filter(change %in% c(2, 9)) %>% verify(length(unique(change)) == 2) %>% setDT(.) %>%
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- # verify that the number of reptition trials per condition is correct:
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+ # verify that the number of repetition trials per condition is correct:
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verify(all(.[, by = .(classification, id, change), .(
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num_trials = length(unique(trial))
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)]$num_trials == 5)) %>%
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@@ -260,7 +266,7 @@ fig_b = ggplot(data = subset(dt_pred_rep_mean_prob_plot, classification == "ovr"
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fig_b
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```
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-```{r, echo = FALSE}
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+```{r, echo=FALSE, include=FALSE,}
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ggsave(filename = "highspeed_plot_decoding_repetition_probabilities_mean_all_trs.pdf",
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plot = fig_b, device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 4, height = 3)
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@@ -363,7 +369,7 @@ fig_c = ggplot(data = dt_plot, aes(
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fig_c
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```
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-```{r}
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+```{r, echo=FALSE, eval=FALSE, include=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition_average_probabilities.pdf",
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plot = fig_c, device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 5, height = 3)
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@@ -450,7 +456,7 @@ test1; test2; test3
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p.adjust(c(test1$p.value, test2$p.value, test3$p.value), method = "bonferroni", n = 6)
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```
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-```{r, echo = FALSE}
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+```{r}
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fig_d = ggplot(data = subset(dt_pred_rep_all_reps_mean, classification == "ovr"), aes(
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x = as.factor(occurence), y = as.numeric(mean_probability),
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group = as.factor(position_label), color = as.factor(position_label))) +
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@@ -478,7 +484,7 @@ fig_d = ggplot(data = subset(dt_pred_rep_all_reps_mean, classification == "ovr")
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fig_d
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```
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-```{r, echo = FALSE}
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+```{r, echo=FALSE, eval=FALSE, include=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition_duration_supplement.pdf",
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plot = fig_d, device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 3.5, height = 2)
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@@ -578,10 +584,6 @@ emmeans_results = emmeans(lme_rep_count, list(pairwise ~ type | change))
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emmeans_summary = summary(emmeans_results[[2]])
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```
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-
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-
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-
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-
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```{r, echo = FALSE}
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dt_significance = data.table(
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change = rep(emmeans_summary$change, each = 2),
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@@ -642,7 +644,8 @@ fig_e = ggplot(data = subset(dt_pred_rep_count, classification == "ovr" & change
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axis.ticks.x = element_line(colour = "white"))
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fig_e
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```
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-```{r}
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+
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+```{r, echo=FALSE, eval=TRUE, include=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition_maxprob.pdf",
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plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 5, height = 3)
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@@ -801,7 +804,7 @@ dt_pred_rep_step_mean = dt_pred_rep_step_version1 %>%
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filter(classification == "ovr")
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```
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-```{r, dev = "cairo_pdf"}
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+```{r}
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plot_rep_trans_mat = function(dt){
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ggplot(data = dt, mapping = aes(
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x = as.factor(head), y = fct_rev(as_factor(tail)),
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@@ -860,7 +863,7 @@ fig_trans_mat
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```
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-```{r, echo = FALSE}
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+```{r, echo=FALSE, eval=FALSE, include=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition_transition_matrix.pdf",
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plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 8, height = 3)
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@@ -915,7 +918,7 @@ fig_trans_prop = plot_rep_trans(dt = subset(
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fig_trans_prop
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```
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-```{r, echo = FALSE}
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+```{r, echo=FALSE, eval=FALSE, include=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition_transition_types.pdf",
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plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 5, height = 3)
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@@ -952,7 +955,7 @@ dt_pred_rep_trans_test
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## Figure 4
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-```{r, fig.width = 10, fig.height = 10, dev = "cairo_pdf"}
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+```{r, fig.width = 10, fig.height = 10}
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plot_grid(plot_grid(fig_a, fig_b, labels = c("a", "b")),
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#plot_grid(fig_c, fig_d, labels = c("c", "d")),
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plot_grid(fig_c, fig_trans_prop, labels = c("c", "d")),
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@@ -960,7 +963,7 @@ plot_grid(plot_grid(fig_a, fig_b, labels = c("a", "b")),
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ncol = 1, label_fontface = "bold")
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```
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-```{r}
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+```{r, echo=FALSE, include=FALSE, eval=FALSE}
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ggsave(filename = "highspeed_plot_decoding_repetition.pdf",
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plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
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dpi = "retina", width = 10, height = 10)
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