Calculate the idealized trajectory and the perpendicular deviations of the actual trajectory from it for each logged position.
mt_deviations( data, use = "trajectories", save_as = use, dimensions = c("xpos", "ypos"), start_ideal = NULL, end_ideal = NULL, prefix = "", verbose = FALSE )
a mousetrap data object created using one of the mt_import
functions (see mt_example for details). Alternatively, a trajectory
array can be provided directly (in this case
a character string specifying which trajectory data should be used.
a character string specifying where the resulting trajectory data should be stored.
a character vector specifying the two dimensions in the
trajectory array that contain the mouse positions. By default
an optional vector specifying the start position (see Example). If specified, this position will be used as the starting point of the idealized trajectory (instead of the actual starting point).
an optional vector specifying the end position (see Example). If specified, this position will be used as the end point of the idealized trajectory (instead of the actual end point).
an optional character string that is added as a prefix to the to be created new trajectory dimensions.
logical indicating whether function should report its progress.
A mousetrap data object (see mt_example) where the positions
of the idealized trajectory (by default called
ypos_ideal) and the perpendicular deviations of the actual
trajectory from the idealized trajectory (by default called
dev_ideal) have been added as additional variables to the trajectory
array. If the trajectory array was provided directly as
the trajectory array will be returned.
The idealized trajectory is defined as the straight line connecting the start and end point of the actual trajectory (e.g., Freeman & Ambady, 2010). The deviation for each position is calculated as the perpendicular deviation of the actual trajectory from the idealized trajectory.
If a deviation occurs above the direct path, this is denoted by a positive
value. If it occurs below the direct path, this is denoted by a negative
value. This assumes that the complete movement in the trial was from bottom
to top (i.e., the end point has a higher y-position than the start points). In
case the movement was from top to bottom,
automatically flips the signs. Note that the second dimension specified in
dimensions is used for determining all this.
Freeman, J. B., & Ambady, N. (2010). MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behavior Research Methods, 42(1), 226-241.
mt_measures for calculating per-trial mouse-tracking measures.
# Calculate deviations from idealized trajectory # (straight line connecting the start and end point of each trial) mt_example <- mt_deviations(mt_example) # Calculate deviations from idealized trajectory with # constant start and end points across trials mt_example <- mt_deviations(mt_example, start_ideal=c(0,0), end_ideal=c(-665,974))