+
data_growth <- data_smooth |>
filter(pNum == 118, trial == 1) |>
mutate(in_AOI = case_when(between(x, HCL_AOIs$x[1] - HCL_AOIs$width_radius[1]/2, HCL_AOIs$x[1] + HCL_AOIs$width_radius[1]/2) &
@@ -358,11 +350,9 @@
-
+
-
Image showing the proportion of time spent in an
AOI over time
@@ -371,7 +361,7 @@
Plotting time spent
Or just predictive and non-predictive cues
Done by adding a single filter in the in_AOI column
-
+
data_growth_partial <- data_smooth |>
filter(pNum == 118, trial == 1) |>
mutate(in_AOI = case_when(between(x, HCL_AOIs$x[1] - HCL_AOIs$width_radius[1]/2, HCL_AOIs$x[1] + HCL_AOIs$width_radius[1]/2) &
@@ -404,11 +394,9 @@
-
+
-
Image showing the proportion of time spent in an
AOI over time
@@ -423,7 +411,7 @@
Or just predictive and non-p