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@@ -106,6 +106,8 @@ facet_labels_old = as.character(sort(unique(dt_pred_rep_timecourses$change)))
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names(facet_labels_new) = facet_labels_old
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```
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+#### Figure 4a
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+
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```{r, echo=TRUE}
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trs = c(2, 7)
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plot_rep_probas <- function(data, xmin = 1, xmax = 13) {
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@@ -433,9 +435,9 @@ dt_pred_rep_all_reps_stats = dt_pred_rep_all_reps_sub %>%
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spread(key = position_label, value = probability, drop = FALSE) %>%
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filter(classification == "ovr")
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# some ugly code to perform three t-tests:
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-summary(dt_pred_rep_all_reps_stats$pos_2); sd(dt_pred_rep_all_reps_stats$pos_2)
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-summary(dt_pred_rep_all_reps_stats$pos_1); sd(dt_pred_rep_all_reps_stats$pos_1)
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-summary(dt_pred_rep_all_reps_stats$pos_none); sd(dt_pred_rep_all_reps_stats$pos_none)
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+summary(dt_pred_rep_all_reps_stats$pos_2); round(sd(dt_pred_rep_all_reps_stats$pos_2), 2)
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+summary(dt_pred_rep_all_reps_stats$pos_1); round(sd(dt_pred_rep_all_reps_stats$pos_1), 2)
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+summary(dt_pred_rep_all_reps_stats$pos_none); round(sd(dt_pred_rep_all_reps_stats$pos_none), 2)
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# perform separate t-tests
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test1 = t.test(dt_pred_rep_all_reps_stats$pos_2, dt_pred_rep_all_reps_stats$pos_1,
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paired = TRUE, alternative = "two.sided")
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@@ -488,8 +490,6 @@ Are there more within sequence items in the classifier predictions?
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To this end, we check if serial events 1 and 2 (of 2) are decoded more
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often than other (out-of-sequence) serial events in the repetition trials.
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-#### Figure 4d
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-
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```{r}
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# define the number of TRs per trial (used below):
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select_trs = seq(2, 7)
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@@ -660,7 +660,7 @@ briefly presented item in a 32 ms sequence compared to items that were not
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presented, when the item (last event) is preceded by a statistical signal
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(change = 9 condition).
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-```{r}
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+```{r, echo=FALSE, eval=FALSE}
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# label all data points with position = 1 or 2 as part of the sequnece:
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dt_pred_rep_count$type_group[dt_pred_rep_count$type %in% c(1,2)] = "in_seq"
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# label all data points with positions > 2 as *not* part of the sequence:
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@@ -723,6 +723,8 @@ transition_type <- function(head, tail){
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}
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```
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+#### Figure 4e
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+
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```{r}
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trs = seq(2, 7)
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# define the number of transitions, which is the number of TRs - 1
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@@ -809,7 +811,7 @@ plot_rep_trans_mat = function(dt){
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xlab("Decoded event at t") +
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ylab("Decoded event at t + 1") +
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facet_wrap(~ as.factor(change), labeller = as_labeller(facet_labels_new)) +
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- scale_size(range = c(1,15), name = "Proportion per trial (in %)",
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+ scale_size(range = c(1, 15), name = "Proportion per trial (in %)",
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guide = guide_legend(
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title.position = "top", direction = "horizontal",
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nrow = 1,
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@@ -864,7 +866,7 @@ ggsave(filename = "highspeed_plot_decoding_repetition_transition_matrix.pdf",
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dpi = "retina", width = 8, height = 3)
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```
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-
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+#### Figure 4d
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```{r, echo = FALSE, dev = "cairo_pdf"}
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plot_rep_trans <- function(dt){
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@@ -948,6 +950,8 @@ dt_pred_rep_trans_test = dt_pred_rep_step_version1 %>%
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dt_pred_rep_trans_test
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```
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+## Figure 4
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+
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```{r, fig.width = 10, fig.height = 10, dev = "cairo_pdf"}
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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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Lennart Wittkuhn